In this paper, a fuzzy behavior-based approach for a three wheeled omnidirectional mobile robot(TWOMR) navigation has been proposed. The robot has to track either static or dynamic target while avoiding either static ...In this paper, a fuzzy behavior-based approach for a three wheeled omnidirectional mobile robot(TWOMR) navigation has been proposed. The robot has to track either static or dynamic target while avoiding either static or dynamic obstacles along its path. A simple controller design is adopted, and to do so, two fuzzy behaviors "Track the Target" and "Avoid Obstacles and Wall Following" are considered based on reduced rule bases(six and five rules respectively). This strategy employs a system of five ultrasonic sensors which provide the necessary information about obstacles in the environment. Simulation platform was designed to demonstrate the effectiveness of the proposed approach.展开更多
Unmanned Aerial Vehicle(UAV)swarm collaboration enhances mission effectiveness.However,fixed-wing UAV swarm flights face collaborative safety control problems within a limited airspace in complex environments.Aimed at...Unmanned Aerial Vehicle(UAV)swarm collaboration enhances mission effectiveness.However,fixed-wing UAV swarm flights face collaborative safety control problems within a limited airspace in complex environments.Aimed at the cooperative control problem of fixed-wing UAV swarm flights under the airspace constraints of a virtual tube in a complex environment,this paper proposes a behavior-based distributed control method for fixed-wing UAV swarm considering flight safety constraints.Considering the fixed-wing UAV swarm flight problem in complex environment,a virtual tube model based on generator curve is established.The tube keeping,centerline tracking and flight safety behavioral control strategies of the UAV swarm are designed to ensure that the UAV swarm flies along the inside of the virtual tube safety and does not go beyond its boundary.On this basis,a maneuvering decision-making method based on behavioral fusion is proposed to ensure the safe flight of UAV swarm in the restricted airspace.This cooperative control method eliminates the need for respective pre-planned trajectories,reduces communication requirements,and achieves a high level of intelligence.Simulation results show that the proposed behaviorbased UAV swarm cooperative control method is able to make the fixed-wing UAV swarm,which is faster and unable to hover,fly along the virtual tube airspace under various virtual tube shapes and different swarm sizes,and the spacing between the UAVs is larger than the minimum safe distance during the flight.展开更多
While different species in nature have safely solved the problem of navigation in a dynamic environment, this remains a challenging task for researchers around the world. The paper addresses the problem of autonomous ...While different species in nature have safely solved the problem of navigation in a dynamic environment, this remains a challenging task for researchers around the world. The paper addresses the problem of autonomous navigation in an unknown dynamic environment for a single and a group of three wheeled omnidirectional mobile robots(TWOMRs). The robot has to track a dynamic target while avoiding dynamic obstacles and dynamic walls in an unknown and very dense environment. It adopts a behavior-based controller that consists of four behaviors: "target tracking", "obstacle avoidance", "dynamic wall following" and "avoid robots". The paper considers the problem of kinematic saturation. In addition, it introduces a strategy for predicting the velocity of dynamic obstacles based on two successive measurements of the ultrasonic sensors to calculate the velocity of the obstacle expressed in the sensor frame. Furthermore, the paper proposes a strategy to deal with dynamic walls even when they have U-like or V-like shapes. The approach can also deal with the formation control of a group of robots based on the leader-follower structure and the behavior-based control, where the robots have to get together and maintain a given formation while navigating toward the target, avoiding obstacles and walls in a dynamic environment. The effectiveness of the proposed approaches is demonstrated via simulation.展开更多
The liquid cooling system(LCS)of fuel cells is challenged by significant time delays,model uncertainties,pump and fan coupling,and frequent disturbances,leading to overshoot and control oscillations that degrade tempe...The liquid cooling system(LCS)of fuel cells is challenged by significant time delays,model uncertainties,pump and fan coupling,and frequent disturbances,leading to overshoot and control oscillations that degrade temperature regulation performance.To address these challenges,we propose a composite control scheme combining fuzzy logic and a variable-gain generalized supertwisting algorithm(VG-GSTA).Firstly,a one-dimensional(1D)fuzzy logic controler(FLC)for the pump ensures stable coolant flow,while a two-dimensional(2D)FLC for the fan regulates the stack temperature near the reference value.The VG-GSTA is then introduced to eliminate steady-state errors,offering resistance to disturbances and minimizing control oscillations.The equilibrium optimizer is used to fine-tune VG-GSTA parameters.Co-simulation verifies the effectiveness of our method,demonstrating its advantages in terms of disturbance immunity,overshoot suppression,tracking accuracy and response speed.展开更多
This paper investigates the problem of fuzzy adaptive finite-time inverse optimal control for active suspension systems(ASSs).The fuzzy logic systems(FLSs)are utilized to learn the unknown non-linear dynamics and an a...This paper investigates the problem of fuzzy adaptive finite-time inverse optimal control for active suspension systems(ASSs).The fuzzy logic systems(FLSs)are utilized to learn the unknown non-linear dynamics and an auxiliary system is established.Based on the finite-time stability theory and inverse optimal theory,a fuzzy adaptive inverse finite-time inverse optimal control method is proposed.It is proven that the formulated control approach guarantees the stability of the controlled systems,while ensuring that errors converge to a small neighborhood of zero within finite time.Moreover,the optimized control performance can be achieved.Eventually,the simulation results demonstrate the effectiveness of the proposed fuzzy adaptive finite-time inverse optimal control scheme.展开更多
Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of cha...Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of charge(SOC)information of the energy storage system,thereby reducing the system flexibility.In this study,we propose an adaptive coordinated control strategy that employs a two-layer fuzzy neural network controller(FNNC)to adapt to varying operating conditions in an IDCMG with multiple PV and battery energy storage system(BESS)units.The first-layer FNNC generates optimal operating mode commands for each DG,thereby avoiding the requirement for complex operating modes based on SOC segmentation.An optimal switching sequence logic prioritizes the most appropriate units during mode transitions.The second-layer FNNC dynamically adjusts the droop power to overcome power distribution challenges among DG groups.This helps in preventing the PV power from exceeding the limits and mitigating the risk of BESS overcharging or over-discharging.The simulation results indicate that the proposed strategy enhances the coordinated operation of multi-DG IDCMGs,thereby ensuring the efficient and safe utilization of PV and BESS.展开更多
In the field of flexible polishing,the accuracy of contact force control directly affects processing quality and material removal uniformity.However,the complex dynamic contact model and inherent strong hysteresis of ...In the field of flexible polishing,the accuracy of contact force control directly affects processing quality and material removal uniformity.However,the complex dynamic contact model and inherent strong hysteresis of pneumatic systems can significantly impact the force control accuracy of pneumatic polishing system end-effectors.To enhance responsiveness and control precision during the flexible polishing process,this study proposes an observer-based fuzzy adaptive control(OBFAC)scheme.To ensure control accuracy under an uncertain dynamic contact model,a fuzzy state observer is designed to estimate unmeasured states,while fuzzy logic approximates the uncertain nonlinear functions in the model to improve control performance.Additionally,the integral barrier Lyapunov function is employed to ensure that all states remain within predefined constraints.The stability of the proposed control scheme is analyzed using the Lyapunov function,and a pneumatic polishing experimental platform is constructed to conduct polishing contact force control experiments under multiple scenarios.Experimental results demonstrate that the proposed OBFAC scheme achieves superior tracking control performance compared to existing control schemes.展开更多
Dividing wall batch distillation with middle vessel(DWBDM)is a new type of batch distillation column,with outstanding advantages of low capital cost,energy saving and flexible operation.However,temperature control of ...Dividing wall batch distillation with middle vessel(DWBDM)is a new type of batch distillation column,with outstanding advantages of low capital cost,energy saving and flexible operation.However,temperature control of DWBDM process is challenging,since inherently dynamic and highly nonlinear,which make it difficult to give the controller reasonable set value or optimal temperature profile for temperature control scheme.To overcome this obstacle,this study proposes a new strategy to develop temperature control scheme for DWBDM combining neural network soft-sensor with fuzzy control.Dynamic model of DWBDM was firstly developed and numerically solved by Python,with three control schemes:composition control by PID and fuzzy control respectively,and temperature control by fuzzy control with neural network soft-sensor.For dynamic process,the neural networks with memory functions,such as RNN,LSTM and GRU,are used to handle with time-series data.The results from a case example show that the new control scheme can perform a good temperature control of DWBDM with the same or even better product purities as traditional PID or fuzzy control,and fuzzy control could reduce the effect of prediction error from neural network,indicating that it is a highly feasible and effective control approach for DWBDM,and could even be extended to other dynamic processes.展开更多
In response to the need for a supportive on-orbit platform for future Mars exploration missions,this paper proposes the design and implementation of an autonomous spacecraft formation flying system near the Martian sy...In response to the need for a supportive on-orbit platform for future Mars exploration missions,this paper proposes the design and implementation of an autonomous spacecraft formation flying system near the Martian synchronous orbit using fuzzy learning-based intelligent control.A detailed analysis of spacecraft relative motion in the Mars environment is conducted,deducing the necessary conditions to reach the Martian synchronous orbit constraints.The modified Clohessy-Wiltshire(C-W)equation with Martian J_(2)(Oblateness index)perturbation is used as a reference to design a fuzzy learning-based intelligent and robust nonlinear control approach,which helps to autonomously track the desired formation configuration and stabilizes it.An introduction to spacecraft propulsion mechanisms is provided to analyze the feasibility of using electrical thrusters for spacecraft formation configuration tracking and stabilization in Martian synchronous orbits.The simulations show the effectiveness of the proposed control system for long-term on-orbit operations and reveal its reliability for designing intelligent deep-space formation flying configurations,such as an autonomous Mars observatory,a Martian telescope,or an interferometer.展开更多
An Interval Type-2(IT-2)fuzzy controller design approach is proposed in this research to simultaneously achievemultiple control objectives inNonlinearMulti-Agent Systems(NMASs),including formation,containment,and coll...An Interval Type-2(IT-2)fuzzy controller design approach is proposed in this research to simultaneously achievemultiple control objectives inNonlinearMulti-Agent Systems(NMASs),including formation,containment,and collision avoidance.However,inherent nonlinearities and uncertainties present in practical control systems contribute to the challenge of achieving precise control performance.Based on the IT-2 Takagi-Sugeno Fuzzy Model(T-SFM),the fuzzy control approach can offer a more effective solution for NMASs facing uncertainties.Unlike existing control methods for NMASs,the Formation and Containment(F-and-C)control problem with collision avoidance capability under uncertainties based on the IT-2 T-SFM is discussed for the first time.Moreover,an IT-2 fuzzy tracking control approach is proposed to solve the formation task for leaders in NMASs without requiring communication.This control scheme makes the design process of the IT-2 fuzzy Formation Controller(FC)more straightforward and effective.According to the communication interaction protocol,the IT-2 Containment Controller(CC)design approach is proposed for followers to ensure convergence into the region defined by the leaders.Leveraging the IT-2 T-SFM representation,the analysis methods developed for linear Multi-Agent Systems(MASs)are successfully extended to perform containment analysis without requiring the additional assumptions imposed in existing research.Notably,the IT-2 fuzzy tracking controller can also be applied in collision avoidance situations to track the desired trajectories calculated by the avoidance algorithm under the Artificial Potential Field(APF).Benefiting from the combination of vortex and source APFs,the leaders can properly adjust the system dynamics to prevent potential collision risk.Integrating the fuzzy theory and APFs avoidance algorithm,an IT-2 fuzzy controller design approach is proposed to achieve the F-and-C purposewhile ensuring collision avoidance capability.Finally,amulti-ship simulation is conducted to validate the feasibility and effectiveness of the designed IT-2 fuzzy controller.展开更多
Inverse reinforcement learning optimal control is under the framework of learner-expert.The learner system can imitate the expert system's demonstrated behaviors and does not require the predefined cost function,s...Inverse reinforcement learning optimal control is under the framework of learner-expert.The learner system can imitate the expert system's demonstrated behaviors and does not require the predefined cost function,so it can handle optimal control problems effectively.This paper proposes an inverse reinforcement learning optimal control method for Takagi-Sugeno(T-S)fuzzy systems.Based on learner systems,an expert system is constructed,where the learner system only knows the expert system's optimal control policy.To reconstruct the unknown cost function,we firstly develop a model-based inverse reinforcement learning algorithm for the case that systems dynamics are known.The developed model-based learning algorithm is consists of two learning stages:an inner reinforcement learning loop and an outer inverse optimal control loop.The inner loop desires to obtain optimal control policy via learner's cost function and the outer loop aims to update learner's state-penalty matrices via only using expert's optimal control policy.Then,to eliminate the requirement that the system dynamics must be known,a data-driven integral learning algorithm is presented.It is proved that the presented two algorithms are convergent and the developed inverse reinforcement learning optimal control scheme can ensure the controlled fuzzy learner systems to be asymptotically stable.Finally,we apply the proposed fuzzy optimal control to the truck-trailer system,and the computer simulation results verify the effectiveness of the presented approach.展开更多
A fuzzy adaptive admittance control method based on real-time estimation is proposed for the motion of the hexapod wheeled-legged robot in various environments.Firstly,the mechanical structure of the robot is designed...A fuzzy adaptive admittance control method based on real-time estimation is proposed for the motion of the hexapod wheeled-legged robot in various environments.Firstly,the mechanical structure of the robot is designed,and a control system framework is proposed according to the different motion environments.To address the adaptability issue of the robot foot contact with the ground,a position-based admittance control method is proposed.Secondly,to improve the tracking performance of the robot foot contact force when the ground environment changes,a fuzzy adaptive admittance parameter adjustment method is proposed.Furthermore,to address the problem of sudden changes in the tracking difference of the foot contact force when the ground environment changes,a real-time estimation method is proposed to estimate the dynamic foot contact force.Finally,a simulation experiment is conducted in MATLAB and Simscape to verify the effectiveness of the robot motion control system,admittance control,fuzzy adaptive admittance parameters adjustment,and the realtime estimation method.Through multi-scenario experiments with the robot prototype,the control method demonstrates its effectiveness and adaptability in various environments.展开更多
This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates senso...This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates sensor and actuator disturbances within the dynamics of follower robots.Each follower robot has unknown dynamics and control inputs,which expose it to the risks of both sensor and actuator attacks.The leader robot,described by a secondorder,time-varying nonlinear model,transmits its position,velocity,and acceleration information to follower robots through a wireless connection.To handle the complex setup and communication among robots in the network,we design a robust hybrid distributed adaptive control strategy combining the effect of sensor and actuator attack,which ensures asymptotic consensus,extending beyond conventional bounded consensus results.The proposed framework employs fuzzy logic systems(FLSs)as proactive controllers to estimate unknown nonlinear behaviors,while also effectively managing sensor and actuator attacks,ensuring stable consensus among all agents.To counter the impact of the combined signal attack on follower dynamics,a specialized robust control mechanism is designed,sustaining system stability and performance under adversarial conditions.The efficiency of this control strategy is demonstrated through simulations conducted across two different directed communication topologies,underscoring the protocol’s adaptability,resilience,and effectiveness in maintaining global consensus under complex attack scenarios.展开更多
This paper discusses and compares some common architectures used inautonomous mobile robotics. Then it describes a behavior-based autonomous mobile robot that wasimplemented successfully in the Robotics Lab of the Dep...This paper discusses and compares some common architectures used inautonomous mobile robotics. Then it describes a behavior-based autonomous mobile robot that wasimplemented successfully in the Robotics Lab of the Department of Precision Mechanical Engineering.Fuzzy controller was used to implement the emergency behavior, the behaviors arbitration wasimplemented using the subsumption architecture. In an unknown dynamic indoor environment, the robotachieved real-time obstacle avoidance properties that are cruel for mobile robotics.展开更多
The control of the clutch engagement for an automatic mechanical transmission in the process of a tracklayer getting to start is studied. The dynamic model of power transmission and automatic clutch system is develope...The control of the clutch engagement for an automatic mechanical transmission in the process of a tracklayer getting to start is studied. The dynamic model of power transmission and automatic clutch system is developed. Using tools of Simulink, the transient characteristics during the vehicle starting, including the jerk and the clutch slip time, are provided here. Based on the analyses of the simulation results and driver’s experiences, a fuzzy controller is designed to control the clutch engagement. Simulation results verify its value.展开更多
A new scheme of direct adaptive fuzzy controller for a class of nonlinear systems with unknown triangular control gain structure is proposed. The design is based on the principle of sliding mode control and the approx...A new scheme of direct adaptive fuzzy controller for a class of nonlinear systems with unknown triangular control gain structure is proposed. The design is based on the principle of sliding mode control and the approximation capability of the first type fuzzy systems. By introducing integral-type Lyapunov function and adopting the adaptive compensation term of optimal approximation error, the closed-loop control system is proved to be globally stable, with tracking error converging to zero. Simulation results demonstrate the effectiveness of the approach.展开更多
The performance of genetic algorithm(GA) is determined by the capability of search and optimization for satisfactory solutions. The new adaptive genetic algorithm(AGA) is built for inducing suitable search and optimiz...The performance of genetic algorithm(GA) is determined by the capability of search and optimization for satisfactory solutions. The new adaptive genetic algorithm(AGA) is built for inducing suitable search and optimization relationship. The use of six fuzzy logic controllers(6FLCs) is proposed for dynamic control genetic operating parameters of a symbolic-coded GA. This paper uses AGA based on 6FLCs to deal with the travelling salesman problem (TSP). Experimental results show that AGA based on 6FLCs is more efficient than a standard GA in solving combinatorial optimization problems similar to TSP.展开更多
In order to overcome the wide-range load tracking and unknown disturbance issues of an ultra-supercritical boiler- turbine unit, a fuzzy disturbance rejection predictive control approach is proposed using the techniq...In order to overcome the wide-range load tracking and unknown disturbance issues of an ultra-supercritical boiler- turbine unit, a fuzzy disturbance rejection predictive control approach is proposed using the techniques of fuzzy scheduling, model predictive control and extended state observer. Local state-space models are established on the basis of nonlinearity analysis and subspace identification. To eiJiance thedisturbance rejection capability of the controller, a extended state observer is employed to estimate unnown disturbances and model mismatches. The disturbance estimation ennaced local predictive controllers ae subsequently devised based on the local models, the performance of which is further strengthened by incorporating the fuzzy scheduling technique. The simulation results verify the merits of the proposed strategy in achieving satisfactory wide-range load tracking ad disturbance rejection performance.展开更多
A fuzzy neural network controller with the teaching controller guidance and parameter regulations for vector-controlled induction motor is proposed. The design procedures of the fuzzy neural controller and the teachin...A fuzzy neural network controller with the teaching controller guidance and parameter regulations for vector-controlled induction motor is proposed. The design procedures of the fuzzy neural controller and the teaching controller are described. The parameters of the membership function are regulated by an on-line learning algorithm. The speed responses of the system under the condition, where the target functions are chosen as I qs and ω, are analyzed. The system responses with the variant of parameter moment of inertial J, viscous coefficients B and torque constant K tare also analyzed. Simulation results show that the control scheme and the controller have the advantages of rapid speed response and good robustness.展开更多
This paper analyzes the fuzzy variable structure control algorithms for delay systems and describes the compensation mechanism of the integral factor to the effect of the delay. Based on the linearized model of the co...This paper analyzes the fuzzy variable structure control algorithms for delay systems and describes the compensation mechanism of the integral factor to the effect of the delay. Based on the linearized model of the congestion-avoidance flow-control mode of transmission control protocol (TCP), we present delay control algorithms for active queue management (AQM) and discuss the parameter tuning of the algorithms. The NS (network simulator) simulation results show that the proposed control scheme for the nonlinear TCP/AQM model has good performance and robustness with respect to the uncertainties of the round-trip time (RTT) and the number of active TCP sessions. Compared to other similar schemes, our algorithms perform better in terms of packet loss ratio, throughput and butter fluctuation.展开更多
文摘In this paper, a fuzzy behavior-based approach for a three wheeled omnidirectional mobile robot(TWOMR) navigation has been proposed. The robot has to track either static or dynamic target while avoiding either static or dynamic obstacles along its path. A simple controller design is adopted, and to do so, two fuzzy behaviors "Track the Target" and "Avoid Obstacles and Wall Following" are considered based on reduced rule bases(six and five rules respectively). This strategy employs a system of five ultrasonic sensors which provide the necessary information about obstacles in the environment. Simulation platform was designed to demonstrate the effectiveness of the proposed approach.
基金co-supported by the National Natural Science Foundation of China(No.61903350)the Ministry of Education industry-university-research innovation project,China(No.2021ZYA02002)the Beijing Institute of Technology Research Fund Program for Young Scholars,China(No.3010011182130)。
文摘Unmanned Aerial Vehicle(UAV)swarm collaboration enhances mission effectiveness.However,fixed-wing UAV swarm flights face collaborative safety control problems within a limited airspace in complex environments.Aimed at the cooperative control problem of fixed-wing UAV swarm flights under the airspace constraints of a virtual tube in a complex environment,this paper proposes a behavior-based distributed control method for fixed-wing UAV swarm considering flight safety constraints.Considering the fixed-wing UAV swarm flight problem in complex environment,a virtual tube model based on generator curve is established.The tube keeping,centerline tracking and flight safety behavioral control strategies of the UAV swarm are designed to ensure that the UAV swarm flies along the inside of the virtual tube safety and does not go beyond its boundary.On this basis,a maneuvering decision-making method based on behavioral fusion is proposed to ensure the safe flight of UAV swarm in the restricted airspace.This cooperative control method eliminates the need for respective pre-planned trajectories,reduces communication requirements,and achieves a high level of intelligence.Simulation results show that the proposed behaviorbased UAV swarm cooperative control method is able to make the fixed-wing UAV swarm,which is faster and unable to hover,fly along the virtual tube airspace under various virtual tube shapes and different swarm sizes,and the spacing between the UAVs is larger than the minimum safe distance during the flight.
文摘While different species in nature have safely solved the problem of navigation in a dynamic environment, this remains a challenging task for researchers around the world. The paper addresses the problem of autonomous navigation in an unknown dynamic environment for a single and a group of three wheeled omnidirectional mobile robots(TWOMRs). The robot has to track a dynamic target while avoiding dynamic obstacles and dynamic walls in an unknown and very dense environment. It adopts a behavior-based controller that consists of four behaviors: "target tracking", "obstacle avoidance", "dynamic wall following" and "avoid robots". The paper considers the problem of kinematic saturation. In addition, it introduces a strategy for predicting the velocity of dynamic obstacles based on two successive measurements of the ultrasonic sensors to calculate the velocity of the obstacle expressed in the sensor frame. Furthermore, the paper proposes a strategy to deal with dynamic walls even when they have U-like or V-like shapes. The approach can also deal with the formation control of a group of robots based on the leader-follower structure and the behavior-based control, where the robots have to get together and maintain a given formation while navigating toward the target, avoiding obstacles and walls in a dynamic environment. The effectiveness of the proposed approaches is demonstrated via simulation.
基金Supported by the Major Science and Technology Project of Jilin Province(20220301010GX)the International Scientific and Technological Cooperation(20240402071GH).
文摘The liquid cooling system(LCS)of fuel cells is challenged by significant time delays,model uncertainties,pump and fan coupling,and frequent disturbances,leading to overshoot and control oscillations that degrade temperature regulation performance.To address these challenges,we propose a composite control scheme combining fuzzy logic and a variable-gain generalized supertwisting algorithm(VG-GSTA).Firstly,a one-dimensional(1D)fuzzy logic controler(FLC)for the pump ensures stable coolant flow,while a two-dimensional(2D)FLC for the fan regulates the stack temperature near the reference value.The VG-GSTA is then introduced to eliminate steady-state errors,offering resistance to disturbances and minimizing control oscillations.The equilibrium optimizer is used to fine-tune VG-GSTA parameters.Co-simulation verifies the effectiveness of our method,demonstrating its advantages in terms of disturbance immunity,overshoot suppression,tracking accuracy and response speed.
基金supported by the National Natural Science Foundation of China under 62173172。
文摘This paper investigates the problem of fuzzy adaptive finite-time inverse optimal control for active suspension systems(ASSs).The fuzzy logic systems(FLSs)are utilized to learn the unknown non-linear dynamics and an auxiliary system is established.Based on the finite-time stability theory and inverse optimal theory,a fuzzy adaptive inverse finite-time inverse optimal control method is proposed.It is proven that the formulated control approach guarantees the stability of the controlled systems,while ensuring that errors converge to a small neighborhood of zero within finite time.Moreover,the optimized control performance can be achieved.Eventually,the simulation results demonstrate the effectiveness of the proposed fuzzy adaptive finite-time inverse optimal control scheme.
基金supported by National Key R&D Program of ChinaunderGrant,(2021YFB2601403).
文摘Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of charge(SOC)information of the energy storage system,thereby reducing the system flexibility.In this study,we propose an adaptive coordinated control strategy that employs a two-layer fuzzy neural network controller(FNNC)to adapt to varying operating conditions in an IDCMG with multiple PV and battery energy storage system(BESS)units.The first-layer FNNC generates optimal operating mode commands for each DG,thereby avoiding the requirement for complex operating modes based on SOC segmentation.An optimal switching sequence logic prioritizes the most appropriate units during mode transitions.The second-layer FNNC dynamically adjusts the droop power to overcome power distribution challenges among DG groups.This helps in preventing the PV power from exceeding the limits and mitigating the risk of BESS overcharging or over-discharging.The simulation results indicate that the proposed strategy enhances the coordinated operation of multi-DG IDCMGs,thereby ensuring the efficient and safe utilization of PV and BESS.
基金Supported by National Key Research and Development Program of China(Grant No.2022YFB3403402)National Natural Science Foundation of China Basic Research Programme for PhD Students(Grant No.524B2049)。
文摘In the field of flexible polishing,the accuracy of contact force control directly affects processing quality and material removal uniformity.However,the complex dynamic contact model and inherent strong hysteresis of pneumatic systems can significantly impact the force control accuracy of pneumatic polishing system end-effectors.To enhance responsiveness and control precision during the flexible polishing process,this study proposes an observer-based fuzzy adaptive control(OBFAC)scheme.To ensure control accuracy under an uncertain dynamic contact model,a fuzzy state observer is designed to estimate unmeasured states,while fuzzy logic approximates the uncertain nonlinear functions in the model to improve control performance.Additionally,the integral barrier Lyapunov function is employed to ensure that all states remain within predefined constraints.The stability of the proposed control scheme is analyzed using the Lyapunov function,and a pneumatic polishing experimental platform is constructed to conduct polishing contact force control experiments under multiple scenarios.Experimental results demonstrate that the proposed OBFAC scheme achieves superior tracking control performance compared to existing control schemes.
基金supported by Beijing Natural Science Foundation(2222037)the Special Educating Project of the Talent for Carbon Peak and Carbon Neutrality of University of Chinese Academy of Sciences(Innovation of talent cultivation model for“dual carbon”in chemical engineering industry,E3E56501A2).
文摘Dividing wall batch distillation with middle vessel(DWBDM)is a new type of batch distillation column,with outstanding advantages of low capital cost,energy saving and flexible operation.However,temperature control of DWBDM process is challenging,since inherently dynamic and highly nonlinear,which make it difficult to give the controller reasonable set value or optimal temperature profile for temperature control scheme.To overcome this obstacle,this study proposes a new strategy to develop temperature control scheme for DWBDM combining neural network soft-sensor with fuzzy control.Dynamic model of DWBDM was firstly developed and numerically solved by Python,with three control schemes:composition control by PID and fuzzy control respectively,and temperature control by fuzzy control with neural network soft-sensor.For dynamic process,the neural networks with memory functions,such as RNN,LSTM and GRU,are used to handle with time-series data.The results from a case example show that the new control scheme can perform a good temperature control of DWBDM with the same or even better product purities as traditional PID or fuzzy control,and fuzzy control could reduce the effect of prediction error from neural network,indicating that it is a highly feasible and effective control approach for DWBDM,and could even be extended to other dynamic processes.
基金supported by the National Laboratory of Space Intelligent Control(No.HTKJ2023KL502007)the Chinese Government Scholarship(CSC)。
文摘In response to the need for a supportive on-orbit platform for future Mars exploration missions,this paper proposes the design and implementation of an autonomous spacecraft formation flying system near the Martian synchronous orbit using fuzzy learning-based intelligent control.A detailed analysis of spacecraft relative motion in the Mars environment is conducted,deducing the necessary conditions to reach the Martian synchronous orbit constraints.The modified Clohessy-Wiltshire(C-W)equation with Martian J_(2)(Oblateness index)perturbation is used as a reference to design a fuzzy learning-based intelligent and robust nonlinear control approach,which helps to autonomously track the desired formation configuration and stabilizes it.An introduction to spacecraft propulsion mechanisms is provided to analyze the feasibility of using electrical thrusters for spacecraft formation configuration tracking and stabilization in Martian synchronous orbits.The simulations show the effectiveness of the proposed control system for long-term on-orbit operations and reveal its reliability for designing intelligent deep-space formation flying configurations,such as an autonomous Mars observatory,a Martian telescope,or an interferometer.
基金founded by the National Science and Technology Council of the Republic of China under contract NSTC113-2221-E-019-032.
文摘An Interval Type-2(IT-2)fuzzy controller design approach is proposed in this research to simultaneously achievemultiple control objectives inNonlinearMulti-Agent Systems(NMASs),including formation,containment,and collision avoidance.However,inherent nonlinearities and uncertainties present in practical control systems contribute to the challenge of achieving precise control performance.Based on the IT-2 Takagi-Sugeno Fuzzy Model(T-SFM),the fuzzy control approach can offer a more effective solution for NMASs facing uncertainties.Unlike existing control methods for NMASs,the Formation and Containment(F-and-C)control problem with collision avoidance capability under uncertainties based on the IT-2 T-SFM is discussed for the first time.Moreover,an IT-2 fuzzy tracking control approach is proposed to solve the formation task for leaders in NMASs without requiring communication.This control scheme makes the design process of the IT-2 fuzzy Formation Controller(FC)more straightforward and effective.According to the communication interaction protocol,the IT-2 Containment Controller(CC)design approach is proposed for followers to ensure convergence into the region defined by the leaders.Leveraging the IT-2 T-SFM representation,the analysis methods developed for linear Multi-Agent Systems(MASs)are successfully extended to perform containment analysis without requiring the additional assumptions imposed in existing research.Notably,the IT-2 fuzzy tracking controller can also be applied in collision avoidance situations to track the desired trajectories calculated by the avoidance algorithm under the Artificial Potential Field(APF).Benefiting from the combination of vortex and source APFs,the leaders can properly adjust the system dynamics to prevent potential collision risk.Integrating the fuzzy theory and APFs avoidance algorithm,an IT-2 fuzzy controller design approach is proposed to achieve the F-and-C purposewhile ensuring collision avoidance capability.Finally,amulti-ship simulation is conducted to validate the feasibility and effectiveness of the designed IT-2 fuzzy controller.
基金The National Natural Science Foundation of China(62173172).
文摘Inverse reinforcement learning optimal control is under the framework of learner-expert.The learner system can imitate the expert system's demonstrated behaviors and does not require the predefined cost function,so it can handle optimal control problems effectively.This paper proposes an inverse reinforcement learning optimal control method for Takagi-Sugeno(T-S)fuzzy systems.Based on learner systems,an expert system is constructed,where the learner system only knows the expert system's optimal control policy.To reconstruct the unknown cost function,we firstly develop a model-based inverse reinforcement learning algorithm for the case that systems dynamics are known.The developed model-based learning algorithm is consists of two learning stages:an inner reinforcement learning loop and an outer inverse optimal control loop.The inner loop desires to obtain optimal control policy via learner's cost function and the outer loop aims to update learner's state-penalty matrices via only using expert's optimal control policy.Then,to eliminate the requirement that the system dynamics must be known,a data-driven integral learning algorithm is presented.It is proved that the presented two algorithms are convergent and the developed inverse reinforcement learning optimal control scheme can ensure the controlled fuzzy learner systems to be asymptotically stable.Finally,we apply the proposed fuzzy optimal control to the truck-trailer system,and the computer simulation results verify the effectiveness of the presented approach.
基金National Natural Science Foundation of China(No.U1831123)。
文摘A fuzzy adaptive admittance control method based on real-time estimation is proposed for the motion of the hexapod wheeled-legged robot in various environments.Firstly,the mechanical structure of the robot is designed,and a control system framework is proposed according to the different motion environments.To address the adaptability issue of the robot foot contact with the ground,a position-based admittance control method is proposed.Secondly,to improve the tracking performance of the robot foot contact force when the ground environment changes,a fuzzy adaptive admittance parameter adjustment method is proposed.Furthermore,to address the problem of sudden changes in the tracking difference of the foot contact force when the ground environment changes,a real-time estimation method is proposed to estimate the dynamic foot contact force.Finally,a simulation experiment is conducted in MATLAB and Simscape to verify the effectiveness of the robot motion control system,admittance control,fuzzy adaptive admittance parameters adjustment,and the realtime estimation method.Through multi-scenario experiments with the robot prototype,the control method demonstrates its effectiveness and adaptability in various environments.
文摘This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates sensor and actuator disturbances within the dynamics of follower robots.Each follower robot has unknown dynamics and control inputs,which expose it to the risks of both sensor and actuator attacks.The leader robot,described by a secondorder,time-varying nonlinear model,transmits its position,velocity,and acceleration information to follower robots through a wireless connection.To handle the complex setup and communication among robots in the network,we design a robust hybrid distributed adaptive control strategy combining the effect of sensor and actuator attack,which ensures asymptotic consensus,extending beyond conventional bounded consensus results.The proposed framework employs fuzzy logic systems(FLSs)as proactive controllers to estimate unknown nonlinear behaviors,while also effectively managing sensor and actuator attacks,ensuring stable consensus among all agents.To counter the impact of the combined signal attack on follower dynamics,a specialized robust control mechanism is designed,sustaining system stability and performance under adversarial conditions.The efficiency of this control strategy is demonstrated through simulations conducted across two different directed communication topologies,underscoring the protocol’s adaptability,resilience,and effectiveness in maintaining global consensus under complex attack scenarios.
文摘This paper discusses and compares some common architectures used inautonomous mobile robotics. Then it describes a behavior-based autonomous mobile robot that wasimplemented successfully in the Robotics Lab of the Department of Precision Mechanical Engineering.Fuzzy controller was used to implement the emergency behavior, the behaviors arbitration wasimplemented using the subsumption architecture. In an unknown dynamic indoor environment, the robotachieved real-time obstacle avoidance properties that are cruel for mobile robotics.
文摘The control of the clutch engagement for an automatic mechanical transmission in the process of a tracklayer getting to start is studied. The dynamic model of power transmission and automatic clutch system is developed. Using tools of Simulink, the transient characteristics during the vehicle starting, including the jerk and the clutch slip time, are provided here. Based on the analyses of the simulation results and driver’s experiences, a fuzzy controller is designed to control the clutch engagement. Simulation results verify its value.
基金The National Natural Science Foundation of PRC (60074013) the Natural Science Foundation of Education Bureau of Jiangsu Province (00KJB510006 & 00KJB470006).
文摘A new scheme of direct adaptive fuzzy controller for a class of nonlinear systems with unknown triangular control gain structure is proposed. The design is based on the principle of sliding mode control and the approximation capability of the first type fuzzy systems. By introducing integral-type Lyapunov function and adopting the adaptive compensation term of optimal approximation error, the closed-loop control system is proved to be globally stable, with tracking error converging to zero. Simulation results demonstrate the effectiveness of the approach.
文摘The performance of genetic algorithm(GA) is determined by the capability of search and optimization for satisfactory solutions. The new adaptive genetic algorithm(AGA) is built for inducing suitable search and optimization relationship. The use of six fuzzy logic controllers(6FLCs) is proposed for dynamic control genetic operating parameters of a symbolic-coded GA. This paper uses AGA based on 6FLCs to deal with the travelling salesman problem (TSP). Experimental results show that AGA based on 6FLCs is more efficient than a standard GA in solving combinatorial optimization problems similar to TSP.
基金The National Natural Science Foundation of China(No.51506029,51576041)the Natural Science Foundation of Jiangsu Province(No.BK20150631)China Postdoctoral Science Foundation
文摘In order to overcome the wide-range load tracking and unknown disturbance issues of an ultra-supercritical boiler- turbine unit, a fuzzy disturbance rejection predictive control approach is proposed using the techniques of fuzzy scheduling, model predictive control and extended state observer. Local state-space models are established on the basis of nonlinearity analysis and subspace identification. To eiJiance thedisturbance rejection capability of the controller, a extended state observer is employed to estimate unnown disturbances and model mismatches. The disturbance estimation ennaced local predictive controllers ae subsequently devised based on the local models, the performance of which is further strengthened by incorporating the fuzzy scheduling technique. The simulation results verify the merits of the proposed strategy in achieving satisfactory wide-range load tracking ad disturbance rejection performance.
文摘A fuzzy neural network controller with the teaching controller guidance and parameter regulations for vector-controlled induction motor is proposed. The design procedures of the fuzzy neural controller and the teaching controller are described. The parameters of the membership function are regulated by an on-line learning algorithm. The speed responses of the system under the condition, where the target functions are chosen as I qs and ω, are analyzed. The system responses with the variant of parameter moment of inertial J, viscous coefficients B and torque constant K tare also analyzed. Simulation results show that the control scheme and the controller have the advantages of rapid speed response and good robustness.
文摘This paper analyzes the fuzzy variable structure control algorithms for delay systems and describes the compensation mechanism of the integral factor to the effect of the delay. Based on the linearized model of the congestion-avoidance flow-control mode of transmission control protocol (TCP), we present delay control algorithms for active queue management (AQM) and discuss the parameter tuning of the algorithms. The NS (network simulator) simulation results show that the proposed control scheme for the nonlinear TCP/AQM model has good performance and robustness with respect to the uncertainties of the round-trip time (RTT) and the number of active TCP sessions. Compared to other similar schemes, our algorithms perform better in terms of packet loss ratio, throughput and butter fluctuation.
