In this paper, neural network control systems for decreasing the spatter of CO2 welding have been created. The Generalized inverse Learning Architecture(GILA), the SPecialized inverse Learning Architecture(SILA)-I &a...In this paper, neural network control systems for decreasing the spatter of CO2 welding have been created. The Generalized inverse Learning Architecture(GILA), the SPecialized inverse Learning Architecture(SILA)-I & H and the Error Back Propagating Model(EBPM) are adopted respectively to simulate the static and dynamic welding control processes. The results of simulation and experiment show that the SILA-I and EBPM have betted properties. The factors affecting the simulating results and the dynamic response quality have also been analyzed.展开更多
Web service applications are increasing tremendously in support of high-level businesses.There must be a need of better server load balancing mechanism for improving the performance of web services in business.Though ...Web service applications are increasing tremendously in support of high-level businesses.There must be a need of better server load balancing mechanism for improving the performance of web services in business.Though many load balancing methods exist,there is still a need for sophisticated load bal-ancing mechanism for not letting the clients to get frustrated.In this work,the ser-ver with minimum response time and the server having less traffic volume were selected for the aimed server to process the forthcoming requests.The Servers are probed with adaptive control of time with two thresholds L and U to indicate the status of server load in terms of response time difference as low,medium and high load by the load balancing application.Fetching the real time responses of entire servers in the server farm is a key component of this intelligent Load balancing system.Many Load Balancing schemes are based on the graded thresholds,because the exact information about the networkflux is difficult to obtain.Using two thresholds L and U,it is possible to indicate the load on particular server as low,medium or high depending on the Maximum response time difference of the servers present in the server farm which is below L,between L and U or above U respectively.However,the existing works of load balancing in the server farm incorporatefixed time to measure real time response time,which in general are not optimal for all traffic conditions.Therefore,an algorithm based on Propor-tional Integration and Derivative neural network controller was designed with two thresholds for tuning the timing to probe the server for near optimal perfor-mance.The emulation results has shown a significant gain in the performance by tuning the threshold time.In addition to that,tuning algorithm is implemented in conjunction with Load Balancing scheme which does not tune thefixed time slots.展开更多
A global approximation based adaptive radial basis function（RBF） neural network control strategy is proposed for the trajectory tracking control of supercavitating vehicles（SV）.A nominal model is built firstly wit...A global approximation based adaptive radial basis function（RBF） neural network control strategy is proposed for the trajectory tracking control of supercavitating vehicles（SV）.A nominal model is built firstly with the unknown disturbance.Next, the control scheme is established consisting of a computed torque controller（CTC） for the practical vehicle and an RBF neural network controller to estimate model error between the practical vehicle and the nominal model. The network weights are adapted by employing a Lyapunov-based design. Then it is shown by the Lyapunov theory that the trajectory tracking errors asymptotically converge to a small neighborhood of zero. The control performance of the proposed controller is illustrated by simulation.展开更多
This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unkn...This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively.The uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives(CMD) system, which satisfies the structure of nonlinear system,is taken for simulation to confirm the effectiveness of the method.Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.展开更多
This paper thoroughly studies a control system with control allocation for a manned submersible in deep sea being developed in China. The proposed control system consists of a neural-network-based direct adaptive cont...This paper thoroughly studies a control system with control allocation for a manned submersible in deep sea being developed in China. The proposed control system consists of a neural-network-based direct adaptive controller and a dynamic control allocation module. A control energy cost function is used as the optimization criteria of the control allocation module, and weighted pseudo-inverse is used to find the solution of the control allocation problem. In the presence of bounded unknown disturbance and neural networks approximation error, stability of the closed-loop control system of manned submersible is proved with Lyaponov theory. The feasibility and validity of the proposed control system is further verified through experiments conducted on a semi-physical simulation platform for the manned submersible in deep sea.展开更多
In this paper, an adaptive dynamic control scheme based on a fuzzy neural network is presented, that presents utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neu...In this paper, an adaptive dynamic control scheme based on a fuzzy neural network is presented, that presents utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neural network with both identification and control role, and the latter is a fuzzy neural algorithm, which is introduced to provide additional control enhancement. The feedforward controller provides only coarse control, whereas the feedback controller can generate on-line conditional proposition rule automatically to improve the overall control action. These properties make the design very versatile and applicable to a range of industrial applications.展开更多
The problem of direct adaptive neural network control for a class of large-scale systems with unknown function control gains and the high-order interconneetions is studied in this paper. Based on the principle of slid...The problem of direct adaptive neural network control for a class of large-scale systems with unknown function control gains and the high-order interconneetions is studied in this paper. Based on the principle of sliding mode control and the approximation capability of multilayer neural networks, a design scheme of decentralized di- rect adaptive sliding mode controller is proposed. The plant dynamic uncertainty and modeling errors are adaptively compensated by adjusted the weights and sliding mode gains on-line for each subsystem using only local informa- tion. According to the Lyapunov method, the closed-loop adaptive control system is proven to be globally stable, with tracking errors converging to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.展开更多
Combining with the characteristic of the fuzzy control and the neural networkcontrol(NNC), a new kind of the fuzzy neural network controller is proposed, and the synthesisdesign method of the control law and fast spee...Combining with the characteristic of the fuzzy control and the neural networkcontrol(NNC), a new kind of the fuzzy neural network controller is proposed, and the synthesisdesign method of the control law and fast speed learning algorithm of the parameters of networks areput forward. The output of the controller is composed of two parts, part one is derived on basis ofthe principle of sliding control, the lower order model and the estimated parameters of the plantare only required, part two is derived on basis FNN, it is used to compensate the uncertainties ofthe systems. Because new type of FNN controller extracts from the advantages of the intelligentcontrol and model based sliding mode control, the numbers of adjusting parameters and the structureof FNN are simplified at large, and the practical significance and variation range are attached toeach layer of the network and its connected weights, the control performance and learning speed areincreased at large. The Tightness of the conclusions is verified by the experiment of anelectro-hydraulic position servo system of the mold of the continuous casting machinery.展开更多
This paper proposed a robust adaptive neural network control for an XY table. The XY table composes of two AC servo drives controlled independently. The neural network with radial basis function is employed for veloci...This paper proposed a robust adaptive neural network control for an XY table. The XY table composes of two AC servo drives controlled independently. The neural network with radial basis function is employed for velocity and position tracking control of AC servo drives to improve the system’s dynamic performance and precision. A robust adaptive term is applied to overcome the external disturbances. The stability and the convergence of the system are proved by Lyapunov theory. The proposed controller is implemented in a DSP-based motion board. The validity and robustness of the controller are verified through experimental results.展开更多
This paper aims at modeling and developing vibration control methods for a flexible piezoelectric beam. A collocated sensor/actuator placement is used. Finite element analysis （FEA） method is adopted to derive the d...This paper aims at modeling and developing vibration control methods for a flexible piezoelectric beam. A collocated sensor/actuator placement is used. Finite element analysis （FEA） method is adopted to derive the dynamics model of the system. A back propagation neural network （BPNN） based proportional-derivative （PD） algorithm is applied to suppress the vibration. Simulation and experiments are conducted using the FEA model and BPNN-PD control law. Experimental results show good agreement with the simulation results using finite element modeling and the neural network control algorithm.展开更多
In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinear...In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinearities compensator. Since the actuator nonlinearities are usually included in the robot driving motor, a compensator using radial basis function (RBF) network is proposed to estimate the actuator nonlinearities and eliminate their effects. Subsequently, an adaptive neural network controller that neither requires the evaluation of inverse dynamical model nor the time-consuming training process is given. In addition, GL matrix and its product operator are introduced to help prove the stability of the closed control system. Considering the adaptive neural network controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded (UUB). The whole scheme provides a general procedure to control the robot manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.展开更多
This paper proposes an optimization algorithm based on a multi-loop control system with a neural network controller,in which the objective function that is used is the control plant of each sub-control system.To obtai...This paper proposes an optimization algorithm based on a multi-loop control system with a neural network controller,in which the objective function that is used is the control plant of each sub-control system.To obtain the global optimization solution from a control plant that has many local minimum points,a transformation function is presented.On the one hand,this approach changes a complex objective function into a simple function under the condition of an unchanged globally optimal solution,to find the global optimization solution more easily by using a multi-loop control system.On the other hand,a special neural network(in which the node function can be simply positioned locally)that is composed of multiple transformation functions is used as the controller,which reduces the possibility of falling into local minimum points.At the same time,a filled function is presented as a control law;it can jump out of a local minimum point and move to another local minimum point that has a smaller value of the objective function.Finally,18 simulation examples are provided to show the effectiveness of the proposed method.展开更多
The weld pool shape control by intelligent strategy was studied. In order to improve the ability of self-learning and self-adaptation of the ordinary fuzzy control, a self-learning fuzzy neural network controller (FNN...The weld pool shape control by intelligent strategy was studied. In order to improve the ability of self-learning and self-adaptation of the ordinary fuzzy control, a self-learning fuzzy neural network controller (FNNC) for backside width of weld pool in pulsed gas tungsten arc welding (GTAW) with wire filler was designed. In FNNC, the fuzzy system was expressed by an equivalence neural network, the membership functions and inference rulers were decided through the learning of the neural network. Then, the FNNC control arithmetic was analyzed, simulating experiment was done, and the validating experiments on varied heat sink workpiece and varied gap workpiece were implemented. The maximum error between the real value and the given one was 0.39mm, the mean error was 0.014mm, and the root-mean-square was 0.14mm. The real backside width was maintained around the given value. The results show that the self-learning fuzzy neural network control strategy can achieve a perfect control effect under different set values and conditions, and is suitable for the welding process with the varied structure and coefficients of control model.展开更多
A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimiz...A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimization （HPSO） algorithm based on immune theory and nonlinear decreasing inertia weight （NDIW） strategy is proposed. Owing to the restraint factor and NDIW strategy, an HPSO algorithm can effectively prevent premature convergence and keep balance between global and local searching abilities. Meanwhile, the algorithm maintains the ability of handling multimodal and multidimensional problems. The HPSO algorithm has the fastest convergence velocity and finds the best solutions compared to GA, IGA, and basic PSO algorithm in simulation experiments. Experimental results on the AUV simulation platform show that HPSO-based controllers perform well and have strong abilities against current disturbance. It can thus be concluded that the proposed algorithm is feasible for application to AUVs.展开更多
Control of coordinated motion between the base attitude and the arm joints of a free-floating dual-arm space robot with uncertain parameters is discussed. By combining the relation of system linear momentum conversati...Control of coordinated motion between the base attitude and the arm joints of a free-floating dual-arm space robot with uncertain parameters is discussed. By combining the relation of system linear momentum conversation with the Lagrangian approach, the dynamic equation of a robot is established. Based on the above results, the free-floating dual-arm space robot system is modeled with RBF neural networks, the GL matrix and its product operator. With all uncertain inertial system parameters, an adaptive RBF neural network control scheme is developed for coordinated motion between the base attitude and the arm joints. The proposed scheme does not need linear parameterization of the dynamic equation of the system and any accurate prior-knowledge of the actual inertial parameters. Also it does not need to train the neural network offline so that it would present real-time and online applications. A planar free-floating dual-arm space robot is simulated to show feasibility of the proposed scheme.展开更多
Disability is defined as a condition that makes it difficult for a person to perform certain vital activities.In recent years,the integration of the concepts of intelligence in solving various problems for disabled pe...Disability is defined as a condition that makes it difficult for a person to perform certain vital activities.In recent years,the integration of the concepts of intelligence in solving various problems for disabled persons has become more frequent.However,controlling an exoskeleton for rehabilitation presents challenges due to their nonlinear characteristics and external disturbances caused by the structure itself or the patient wearing the exoskeleton.To remedy these problems,this paper presents a novel adaptive control strategy for upper-limb rehabilitation exoskeletons,addressing the challenges of nonlinear dynamics and external disturbances.The proposed controller integrated a Radial Basis Function Neural Network(RBFNN)with a disturbance observer and employed a high-dimensional integral Lyapunov function to guarantee system stability and trajectory tracking performance.In the control system,the role of the RBFNN was to estimate uncertain signals in the dynamic model,while the disturbance observer tackled external disturbances during trajectory tracking.Artificially created scenarios for Human-Robot interactive experiments and periodically repeated reference trajectory experiments validated the controller’s performance,demonstrating efficient tracking.The proposed controller is found to achieve superior tracking accuracy with Root-Mean-Squared(RMS)errors of 0.022-0.026 rad for all joints,outperforming conventional Proportional-Integral-Derivative(PID)by 73%and Neural-Fuzzy Adaptive Control(NFAC)by 389.47%lower error.These results suggested that the RBFNN adaptive controller,coupled with disturbance compensation,could serve as an effective rehabilitation tool for upper-limb exoskeletons.These results demonstrate the superiority of the proposed method in enhancing rehabilitation accuracy and robustness,offering a promising solution for the control of upper-limb assistive devices.Based on the obtained results and due to their high robustness,the proposed control schemes can be extended to other motor disabilities,including lower limb exoskeletons.展开更多
Tracking control of tendon-driven manipulators has become a prevalent research area.However,the existence of flexible elastic tendons generates substantial residual vibrations,resulting in difficulties for trajectory ...Tracking control of tendon-driven manipulators has become a prevalent research area.However,the existence of flexible elastic tendons generates substantial residual vibrations,resulting in difficulties for trajectory tracking control of the manipulator.This paper proposes the radial basis function neural network adaptive hierarchical sliding mode control(RBFNNA-HSMC)method,which combines the dynamic model of the elastic tendon-driven manipulator(ETDM)with radial basis neural network adaptive control and hierarchical sliding mode control technology.The aim is to achieve trajectory tracking control of ETDM even under conditions of model inaccuracy and disturbance.The Lyapunov stability theory demonstrates the stability of the proposed RBFNNA-HSM controller.In order to assess the effectiveness and adaptability of the proposed control method,simulations and experiments were performed on a two-DOF ETDM.The RBFNNA-HSM method shows superior tracking accuracy compared to traditional modelbased HSM control.The experiment shows that the maximum tracking error for ETDM double-joint trajectory tracking is below 2.593×10-3rad and 1.624×10-3rad,respectively.展开更多
This paper proposes a robust control scheme based on the sequential convex programming and learning-based model for nonlinear system subjected to additive uncertainties.For the problem of system nonlinearty and unknow...This paper proposes a robust control scheme based on the sequential convex programming and learning-based model for nonlinear system subjected to additive uncertainties.For the problem of system nonlinearty and unknown uncertainties,we study the tube-based model predictive control scheme that makes use of feedforward neural network.Based on the characteristics of the bounded limit of the average cost function while time approaching infinity,a min-max optimization problem(referred to as min-max OP)is formulated to design the controller.The feasibility of this optimization problem and the practical stability of the controlled system are ensured.To demonstrate the efficacy of the proposed approach,a numerical simulation on a double-tank system is conducted.The results of the simulation serve as verification of the effectualness of the proposed scheme.展开更多
Thermoacoustic instability phenomena often encounter in gas turbine combustors,especially for the premixed combustor design,with many possible detrimental results.As a classical experiment,the Rijke tube is the simple...Thermoacoustic instability phenomena often encounter in gas turbine combustors,especially for the premixed combustor design,with many possible detrimental results.As a classical experiment,the Rijke tube is the simplest and the most effective illustration to study the thermoacoustic instability.This paper investigates the active control approach of the thermoacoustic instability in a horizontal Rijke tube.What’s more,the radial basis function(RBF)neural network is adopted to estimate the complex unknown continuous nonlinear heat release rate in the Rijke tube.Then,based on the proposed second-order fully actuated system model,the authors present an adaptive neural network controller to guarantee the flow velocity fluctuation and pressure fluctuation to converge to a small region of the origin.Finally,simulation results demonstrate the feasibility of the design method.展开更多
In IBVS (image based visual servoing), the error signal in image space should be transformed into the control signal in the input space quickly. To avoid the iterative adjustment and complicated inverse solution of im...In IBVS (image based visual servoing), the error signal in image space should be transformed into the control signal in the input space quickly. To avoid the iterative adjustment and complicated inverse solution of image Jacobian, CMAC (cerebellar model articulation controller) neural network is inserted into visual servo control loop to implement the nonlinear mapping. Two control schemes are used. Simulation results on two schemes are provided, which show a better tracking precision and stability can be achieved using scheme 2.展开更多
文摘In this paper, neural network control systems for decreasing the spatter of CO2 welding have been created. The Generalized inverse Learning Architecture(GILA), the SPecialized inverse Learning Architecture(SILA)-I & H and the Error Back Propagating Model(EBPM) are adopted respectively to simulate the static and dynamic welding control processes. The results of simulation and experiment show that the SILA-I and EBPM have betted properties. The factors affecting the simulating results and the dynamic response quality have also been analyzed.
文摘Web service applications are increasing tremendously in support of high-level businesses.There must be a need of better server load balancing mechanism for improving the performance of web services in business.Though many load balancing methods exist,there is still a need for sophisticated load bal-ancing mechanism for not letting the clients to get frustrated.In this work,the ser-ver with minimum response time and the server having less traffic volume were selected for the aimed server to process the forthcoming requests.The Servers are probed with adaptive control of time with two thresholds L and U to indicate the status of server load in terms of response time difference as low,medium and high load by the load balancing application.Fetching the real time responses of entire servers in the server farm is a key component of this intelligent Load balancing system.Many Load Balancing schemes are based on the graded thresholds,because the exact information about the networkflux is difficult to obtain.Using two thresholds L and U,it is possible to indicate the load on particular server as low,medium or high depending on the Maximum response time difference of the servers present in the server farm which is below L,between L and U or above U respectively.However,the existing works of load balancing in the server farm incorporatefixed time to measure real time response time,which in general are not optimal for all traffic conditions.Therefore,an algorithm based on Propor-tional Integration and Derivative neural network controller was designed with two thresholds for tuning the timing to probe the server for near optimal perfor-mance.The emulation results has shown a significant gain in the performance by tuning the threshold time.In addition to that,tuning algorithm is implemented in conjunction with Load Balancing scheme which does not tune thefixed time slots.
基金supported by the National Natural Science Foundation of China(5167920161473233)
文摘A global approximation based adaptive radial basis function（RBF） neural network control strategy is proposed for the trajectory tracking control of supercavitating vehicles（SV）.A nominal model is built firstly with the unknown disturbance.Next, the control scheme is established consisting of a computed torque controller（CTC） for the practical vehicle and an RBF neural network controller to estimate model error between the practical vehicle and the nominal model. The network weights are adapted by employing a Lyapunov-based design. Then it is shown by the Lyapunov theory that the trajectory tracking errors asymptotically converge to a small neighborhood of zero. The control performance of the proposed controller is illustrated by simulation.
基金partially supported by the National Natural Science Foundation of China(61703402,61374048)
文摘This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively.The uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives(CMD) system, which satisfies the structure of nonlinear system,is taken for simulation to confirm the effectiveness of the method.Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.
基金This project is financially supported by the National High Technology Research Development Programof China(863Program)(Grant No.2002AA401003)
文摘This paper thoroughly studies a control system with control allocation for a manned submersible in deep sea being developed in China. The proposed control system consists of a neural-network-based direct adaptive controller and a dynamic control allocation module. A control energy cost function is used as the optimization criteria of the control allocation module, and weighted pseudo-inverse is used to find the solution of the control allocation problem. In the presence of bounded unknown disturbance and neural networks approximation error, stability of the closed-loop control system of manned submersible is proved with Lyaponov theory. The feasibility and validity of the proposed control system is further verified through experiments conducted on a semi-physical simulation platform for the manned submersible in deep sea.
基金China Postdoctoral Science Foundation and Natural Science of Heibei Province!698004
文摘In this paper, an adaptive dynamic control scheme based on a fuzzy neural network is presented, that presents utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neural network with both identification and control role, and the latter is a fuzzy neural algorithm, which is introduced to provide additional control enhancement. The feedforward controller provides only coarse control, whereas the feedback controller can generate on-line conditional proposition rule automatically to improve the overall control action. These properties make the design very versatile and applicable to a range of industrial applications.
基金This project was supported by the National Natural Science Foundation (60074013 &10371106)the Natural ScienceFoundation of Education Bureau of Jiangsu (KK0310067) the Foundation of Information Science Subject Group of YangzhouUniversity (ISG030606)
文摘The problem of direct adaptive neural network control for a class of large-scale systems with unknown function control gains and the high-order interconneetions is studied in this paper. Based on the principle of sliding mode control and the approximation capability of multilayer neural networks, a design scheme of decentralized di- rect adaptive sliding mode controller is proposed. The plant dynamic uncertainty and modeling errors are adaptively compensated by adjusted the weights and sliding mode gains on-line for each subsystem using only local informa- tion. According to the Lyapunov method, the closed-loop adaptive control system is proven to be globally stable, with tracking errors converging to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.
基金This project is supported by National Natural Science Foundation of China (No.59975003).
文摘Combining with the characteristic of the fuzzy control and the neural networkcontrol(NNC), a new kind of the fuzzy neural network controller is proposed, and the synthesisdesign method of the control law and fast speed learning algorithm of the parameters of networks areput forward. The output of the controller is composed of two parts, part one is derived on basis ofthe principle of sliding control, the lower order model and the estimated parameters of the plantare only required, part two is derived on basis FNN, it is used to compensate the uncertainties ofthe systems. Because new type of FNN controller extracts from the advantages of the intelligentcontrol and model based sliding mode control, the numbers of adjusting parameters and the structureof FNN are simplified at large, and the practical significance and variation range are attached toeach layer of the network and its connected weights, the control performance and learning speed areincreased at large. The Tightness of the conclusions is verified by the experiment of anelectro-hydraulic position servo system of the mold of the continuous casting machinery.
文摘This paper proposed a robust adaptive neural network control for an XY table. The XY table composes of two AC servo drives controlled independently. The neural network with radial basis function is employed for velocity and position tracking control of AC servo drives to improve the system’s dynamic performance and precision. A robust adaptive term is applied to overcome the external disturbances. The stability and the convergence of the system are proved by Lyapunov theory. The proposed controller is implemented in a DSP-based motion board. The validity and robustness of the controller are verified through experimental results.
基金Project supported by the Key Project(No.60934001)the General Projects(Nos.51175181and90505014)of the National Natural Science Foundation of Chinaby the Fundamental Research Funds for the Central Universities,SCUT(No.2012ZZ0060)
文摘This paper aims at modeling and developing vibration control methods for a flexible piezoelectric beam. A collocated sensor/actuator placement is used. Finite element analysis （FEA） method is adopted to derive the dynamics model of the system. A back propagation neural network （BPNN） based proportional-derivative （PD） algorithm is applied to suppress the vibration. Simulation and experiments are conducted using the FEA model and BPNN-PD control law. Experimental results show good agreement with the simulation results using finite element modeling and the neural network control algorithm.
文摘In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinearities compensator. Since the actuator nonlinearities are usually included in the robot driving motor, a compensator using radial basis function (RBF) network is proposed to estimate the actuator nonlinearities and eliminate their effects. Subsequently, an adaptive neural network controller that neither requires the evaluation of inverse dynamical model nor the time-consuming training process is given. In addition, GL matrix and its product operator are introduced to help prove the stability of the closed control system. Considering the adaptive neural network controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded (UUB). The whole scheme provides a general procedure to control the robot manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.
基金supported by the National Natural Science Foundation of China(61273190)
文摘This paper proposes an optimization algorithm based on a multi-loop control system with a neural network controller,in which the objective function that is used is the control plant of each sub-control system.To obtain the global optimization solution from a control plant that has many local minimum points,a transformation function is presented.On the one hand,this approach changes a complex objective function into a simple function under the condition of an unchanged globally optimal solution,to find the global optimization solution more easily by using a multi-loop control system.On the other hand,a special neural network(in which the node function can be simply positioned locally)that is composed of multiple transformation functions is used as the controller,which reduces the possibility of falling into local minimum points.At the same time,a filled function is presented as a control law;it can jump out of a local minimum point and move to another local minimum point that has a smaller value of the objective function.Finally,18 simulation examples are provided to show the effectiveness of the proposed method.
文摘The weld pool shape control by intelligent strategy was studied. In order to improve the ability of self-learning and self-adaptation of the ordinary fuzzy control, a self-learning fuzzy neural network controller (FNNC) for backside width of weld pool in pulsed gas tungsten arc welding (GTAW) with wire filler was designed. In FNNC, the fuzzy system was expressed by an equivalence neural network, the membership functions and inference rulers were decided through the learning of the neural network. Then, the FNNC control arithmetic was analyzed, simulating experiment was done, and the validating experiments on varied heat sink workpiece and varied gap workpiece were implemented. The maximum error between the real value and the given one was 0.39mm, the mean error was 0.014mm, and the root-mean-square was 0.14mm. The real backside width was maintained around the given value. The results show that the self-learning fuzzy neural network control strategy can achieve a perfect control effect under different set values and conditions, and is suitable for the welding process with the varied structure and coefficients of control model.
基金the National Natural Science Foundation of China (No.50579007)
文摘A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimization （HPSO） algorithm based on immune theory and nonlinear decreasing inertia weight （NDIW） strategy is proposed. Owing to the restraint factor and NDIW strategy, an HPSO algorithm can effectively prevent premature convergence and keep balance between global and local searching abilities. Meanwhile, the algorithm maintains the ability of handling multimodal and multidimensional problems. The HPSO algorithm has the fastest convergence velocity and finds the best solutions compared to GA, IGA, and basic PSO algorithm in simulation experiments. Experimental results on the AUV simulation platform show that HPSO-based controllers perform well and have strong abilities against current disturbance. It can thus be concluded that the proposed algorithm is feasible for application to AUVs.
基金the National Natural Science Foundation of China (Nos. 10672040 and10372022)the Natural Science Foundation of Fujian Province of China (No. E0410008)
文摘Control of coordinated motion between the base attitude and the arm joints of a free-floating dual-arm space robot with uncertain parameters is discussed. By combining the relation of system linear momentum conversation with the Lagrangian approach, the dynamic equation of a robot is established. Based on the above results, the free-floating dual-arm space robot system is modeled with RBF neural networks, the GL matrix and its product operator. With all uncertain inertial system parameters, an adaptive RBF neural network control scheme is developed for coordinated motion between the base attitude and the arm joints. The proposed scheme does not need linear parameterization of the dynamic equation of the system and any accurate prior-knowledge of the actual inertial parameters. Also it does not need to train the neural network offline so that it would present real-time and online applications. A planar free-floating dual-arm space robot is simulated to show feasibility of the proposed scheme.
基金funded by the King Salman Center For Disability Research,through Research Group No.KSRG-2024-468。
文摘Disability is defined as a condition that makes it difficult for a person to perform certain vital activities.In recent years,the integration of the concepts of intelligence in solving various problems for disabled persons has become more frequent.However,controlling an exoskeleton for rehabilitation presents challenges due to their nonlinear characteristics and external disturbances caused by the structure itself or the patient wearing the exoskeleton.To remedy these problems,this paper presents a novel adaptive control strategy for upper-limb rehabilitation exoskeletons,addressing the challenges of nonlinear dynamics and external disturbances.The proposed controller integrated a Radial Basis Function Neural Network(RBFNN)with a disturbance observer and employed a high-dimensional integral Lyapunov function to guarantee system stability and trajectory tracking performance.In the control system,the role of the RBFNN was to estimate uncertain signals in the dynamic model,while the disturbance observer tackled external disturbances during trajectory tracking.Artificially created scenarios for Human-Robot interactive experiments and periodically repeated reference trajectory experiments validated the controller’s performance,demonstrating efficient tracking.The proposed controller is found to achieve superior tracking accuracy with Root-Mean-Squared(RMS)errors of 0.022-0.026 rad for all joints,outperforming conventional Proportional-Integral-Derivative(PID)by 73%and Neural-Fuzzy Adaptive Control(NFAC)by 389.47%lower error.These results suggested that the RBFNN adaptive controller,coupled with disturbance compensation,could serve as an effective rehabilitation tool for upper-limb exoskeletons.These results demonstrate the superiority of the proposed method in enhancing rehabilitation accuracy and robustness,offering a promising solution for the control of upper-limb assistive devices.Based on the obtained results and due to their high robustness,the proposed control schemes can be extended to other motor disabilities,including lower limb exoskeletons.
基金Supported by Key R&D Project of Zhejiang(Grant No.2022C02052)。
文摘Tracking control of tendon-driven manipulators has become a prevalent research area.However,the existence of flexible elastic tendons generates substantial residual vibrations,resulting in difficulties for trajectory tracking control of the manipulator.This paper proposes the radial basis function neural network adaptive hierarchical sliding mode control(RBFNNA-HSMC)method,which combines the dynamic model of the elastic tendon-driven manipulator(ETDM)with radial basis neural network adaptive control and hierarchical sliding mode control technology.The aim is to achieve trajectory tracking control of ETDM even under conditions of model inaccuracy and disturbance.The Lyapunov stability theory demonstrates the stability of the proposed RBFNNA-HSM controller.In order to assess the effectiveness and adaptability of the proposed control method,simulations and experiments were performed on a two-DOF ETDM.The RBFNNA-HSM method shows superior tracking accuracy compared to traditional modelbased HSM control.The experiment shows that the maximum tracking error for ETDM double-joint trajectory tracking is below 2.593×10-3rad and 1.624×10-3rad,respectively.
文摘This paper proposes a robust control scheme based on the sequential convex programming and learning-based model for nonlinear system subjected to additive uncertainties.For the problem of system nonlinearty and unknown uncertainties,we study the tube-based model predictive control scheme that makes use of feedforward neural network.Based on the characteristics of the bounded limit of the average cost function while time approaching infinity,a min-max optimization problem(referred to as min-max OP)is formulated to design the controller.The feasibility of this optimization problem and the practical stability of the controlled system are ensured.To demonstrate the efficacy of the proposed approach,a numerical simulation on a double-tank system is conducted.The results of the simulation serve as verification of the effectualness of the proposed scheme.
基金This research was supported by the National Natural Science Foundation of China under Grant No.61973060the Science Center Program of National Natural Science Foundation of China under Grant No.62188101.
文摘Thermoacoustic instability phenomena often encounter in gas turbine combustors,especially for the premixed combustor design,with many possible detrimental results.As a classical experiment,the Rijke tube is the simplest and the most effective illustration to study the thermoacoustic instability.This paper investigates the active control approach of the thermoacoustic instability in a horizontal Rijke tube.What’s more,the radial basis function(RBF)neural network is adopted to estimate the complex unknown continuous nonlinear heat release rate in the Rijke tube.Then,based on the proposed second-order fully actuated system model,the authors present an adaptive neural network controller to guarantee the flow velocity fluctuation and pressure fluctuation to converge to a small region of the origin.Finally,simulation results demonstrate the feasibility of the design method.
基金This project is supported by National Natural Science Foundation of China (No.59990470).
文摘In IBVS (image based visual servoing), the error signal in image space should be transformed into the control signal in the input space quickly. To avoid the iterative adjustment and complicated inverse solution of image Jacobian, CMAC (cerebellar model articulation controller) neural network is inserted into visual servo control loop to implement the nonlinear mapping. Two control schemes are used. Simulation results on two schemes are provided, which show a better tracking precision and stability can be achieved using scheme 2.
