The random delays in a networked control system (NCS) degrade control performance and can even destabilize the control system.To deal with this problem,the time-stamped predictive functional control (PFC) algorithm is...The random delays in a networked control system (NCS) degrade control performance and can even destabilize the control system.To deal with this problem,the time-stamped predictive functional control (PFC) algorithm is proposed,which generalizes the standard PFC algorithm to networked control systems with random delays.The algorithm uses the time-stamp method to estimate the control delay,predicts the future outputs based on a discrete time delay state space model,and drives the control law that applies to an NCS from the idea of a PFC algorithm.A networked control system was constructed based on TrueTime simulator,with which the time-stamped PFC algorithm was compared with the standard PFC algorithm.The response curves show that the proposed algorithm has better control performance.展开更多
In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） f...In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.展开更多
The control of gas fractionation unit(GFU) in petroleum industry is very difficult due to multivariable characteristics and a large time delay.PID controllers are still applied in most industry processes.However,the t...The control of gas fractionation unit(GFU) in petroleum industry is very difficult due to multivariable characteristics and a large time delay.PID controllers are still applied in most industry processes.However,the traditional PID control has been proven not sufficient and capable for this particular petro-chemical process.In this work,an incremental multivariable predictive functional control(IMPFC) algorithm was proposed with less online computation,great precision and fast response.An incremental transfer function matrix model was set up through the step-response data,and predictive outputs were deduced with the theory of single-value optimization.The results show that the method can optimize the incremental control variable and reject the constraint of the incremental control variable with the positional predictive functional control algorithm,and thereby making the control variable smoother.The predictive output error and future set-point were approximated by a polynomial,which can overcome the problem under the model mismatch and make the predictive outputs track the reference trajectory.Then,the design of incremental multivariable predictive functional control was studied.Simulation and application results show that the proposed control strategy is effective and feasible to improve control performance and robustness of process.展开更多
In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive fun...In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive functional control(AFPFC) scheme for multivariable nonlinear systems was proposed.Firstly,multivariable nonlinear systems were described based on Takagi-Sugeno(T-S) fuzzy models;assuming that the antecedent parameters of T-S models were kept,the consequent parameters were identified on-line by using the weighted recursive least square(WRLS) method.Secondly,the identified T-S models were linearized to be time-varying state space model at each sampling instant.Finally,by using linear predictive control technique the analysis solution of the optimal control law of AFPFC was established.The application results for pH neutralization process show that the absolute error between the identified T-S model output and the process output is smaller than 0.015;the tracking ability of the proposed AFPFC is superior to that of non-AFPFC(NAFPFC) for pH process without disturbances,the overshoot of the effluent pH value of AFPFC with disturbances is decreased by 50% compared with that of NAFPFC;when the process parameters of AFPFC vary with time the integrated absolute error(IAE) performance index still retains to be less than 200 compared with that of NAFPFC.展开更多
The predictive model is built according to the characteristics of the impulse response of integrating process. In order to eliminate the permanent offset between the setpoint and the process output in the presence of ...The predictive model is built according to the characteristics of the impulse response of integrating process. In order to eliminate the permanent offset between the setpoint and the process output in the presence of the load disturbance, a novel error compensation method is proposed. Then predictive functional control of integrating process is designed. The method given generates a simple control structure, which can significandy reduce online computation. Furthermore, the tuning of the controller is fairly straightforward. Simulation results indicate that the designed control system is relatively robust to the parameters variation of the process.展开更多
Reaction control system(RCS) is a powerful and efficient actuator for space vehicles attitude control, which is typically characterized as a pulsed unilateral effector only with two states(off/on). Along with inevitab...Reaction control system(RCS) is a powerful and efficient actuator for space vehicles attitude control, which is typically characterized as a pulsed unilateral effector only with two states(off/on). Along with inevitable internal uncertainties and external disturbances in practice, this inherent nonlinear character always hinders space vehicles autopilot from pursuing precise tracking performance. Compared to most of pre-existing methodologies that passively suppress the uncertainties and disturbances, a design based on predictive functional control(PFC) and generalized extended state observer(GESO) is firstly proposed for three-axis RCS control system to actively reject that with no requirement for additional fuel consumption. To obtain a high fidelity predictive model on which the performance of PFC greatly depends, the nonlinear coupling multiple-input multiple-output(MIMO) flight dynamics model is parameterized as a state-dependent coefficient form. And based on that, a MIMO PFC algorithm in state space domain for a plant of arbitrary orders is deduced in this paper.The internal uncertainties and external disturbances are lumped as a total disturbance, which is estimated and cancelled timely to further enhance the robustness. The continuous control command synthesised by above controller-rejector tandem is finally modulated by pulse width pulse frequency modulator(PWPF) to on-off signals to meet RCS requirement. The robustness and feasibility of the proposed design are validated by a series of performance comparison simulations with some prominent methods in the presence of significant perturbations and disturbances, as well as measurement noise.展开更多
By extending the system's state variables,a novel predictive functional controller has been developed.The structure of this controller is similar to that of classical proportional integral(PI)optimal controller an...By extending the system's state variables,a novel predictive functional controller has been developed.The structure of this controller is similar to that of classical proportional integral(PI)optimal controller and in-cludes a control block that can perform a feed-forward control of future P-step set points.It considers both the state variables and the output errors in its cost function,which results in enhanced control performance compared with traditional state space predictive functional control(TSSPFC)methods that consider only the predictive output er-rors.The predictive functional controller(PFC)has been compared with TSSPFC in terms of tracking ability,dis-turbance rejection,and also based on its application to heavy oil coking equipment.The results obtained show the effectiveness of the controller.展开更多
The use of living, alkyllithium-initiated anionic polymerization to prepare chain-end functionalized polymers and heteroarm, star-branched polymers is discussed. The scope and limitations of specific termination react...The use of living, alkyllithium-initiated anionic polymerization to prepare chain-end functionalized polymers and heteroarm, star-branched polymers is discussed. The scope and limitations of specific termination reactions with a variety of electrophilic species are illustrated for carbonation, hydroxyethylation, amination, and sulfonation. The methodology of using substituted 1,1-diphenylethylenes to provide a general, quantitative functionalization procedure is outlined and illustrated with examples of amine and phenol end-functionalization. A methodology is described for the synthesis of functionalized, star-branched copolymers with compositionally heterogeneous arms of controlled molecular weight and narrow molecular weight distribution using 1, 3-bis(1-pbenylethenyl) benzene.展开更多
The main principle and the characteristic of Predictive Functional Control (PFC) strategy are presented in this paper and the corresponding control system aid design software APC-PFC is also introduced. For a chlorina...The main principle and the characteristic of Predictive Functional Control (PFC) strategy are presented in this paper and the corresponding control system aid design software APC-PFC is also introduced. For a chlorinated polyethylene (CPE) process, a design scheme of cascade predictive functional control system is described and the control performance is improved obviously.展开更多
The objective of this paper is to present a robust safety-critical control system based on the active disturbance rejection control approach, designed to guarantee safety even in the presence of model inaccuracies, un...The objective of this paper is to present a robust safety-critical control system based on the active disturbance rejection control approach, designed to guarantee safety even in the presence of model inaccuracies, unknown dynamics, and external disturbances. The proposed method combines control barrier functions and control Lyapunov functions with a nonlinear extended state observer to produce a robust and safe control strategy for dynamic systems subject to uncertainties and disturbances. This control strategy employs an optimization-based control, supported by the disturbance estimation from a nonlinear extended state observer. Using a quadratic programming algorithm, the controller computes an optimal, stable, and safe control action at each sampling instant. The effectiveness of the proposed approach is demonstrated through numerical simulations of a safety-critical interconnected adaptive cruise control system.展开更多
In this work,a self-healing predictive control method for discrete-time nonlinear systems is presented to ensure the system can be safely operated under abnormal states.First,a robust MPC controller for the normal cas...In this work,a self-healing predictive control method for discrete-time nonlinear systems is presented to ensure the system can be safely operated under abnormal states.First,a robust MPC controller for the normal case is constructed,which can drive the system to the equilibrium point when the closed-loop states are in the predetermined safe set.In this controller,the tubes are built based on the incremental Lyapunov function to tighten nominal constraints.To deal with the infeasible controller when abnormal states occur,a self-healing predictive control method is further proposed to realize self-healing by driving the system towards the safe set.This is achieved by an auxiliary softconstrained recovery mechanism that can solve the constraint violation caused by the abnormal states.By extending the discrete-time robust control barrier function theory,it is proven that the auxiliary problem provides a predictive control barrier bounded function to make the system asymptotically stable towards the safe set.The theoretical properties of robust recursive feasibility and bounded stability are further analyzed.The efficiency of the proposed controller is verified by a numerical simulation of a continuous stirred-tank reactor process.展开更多
This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances,state constraints,and input constraints in complex ocean environments with multiple obstacles.A safety-certifie...This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances,state constraints,and input constraints in complex ocean environments with multiple obstacles.A safety-certified parallel model predictive control scheme with collision-avoiding capability is proposed for autonomous surface vehicles in the framework of parallel control.Specifically,an extended state observer is designed by leveraging historical and real-time data for concurrent learning to map the motion of autonomous surface vehicles from its physical system to its artificial counterpart.A parallel model predictive control law is developed on the basis of the artificial system for both physical and artificial autonomous surface vehicles to realize virtual-physical tracking control of vehicles subject to state and input constraints.To ensure safety,highorder discrete control barrier functions are encoded in the parallel model predictive control law as safety constraints such that collision avoidance with obstacles can be achieved.A recedinghorizon constrained optimization problem is constructed with the safety constraints encoded by control barrier functions for parallel model predictive control of autonomous surface vehicles and solved via neurodynamic optimization with projection neural networks.The effectiveness and characteristics of the proposed method are demonstrated via simulations for the safe trajectory tracking and automatic berthing of autonomous surface vehicles.展开更多
The control and data planes are decoupled in software-defined networking(SDN),which enables both planes to evolve independently,and brings about many advantages such as high flexibility,programmability,and rapid imple...The control and data planes are decoupled in software-defined networking(SDN),which enables both planes to evolve independently,and brings about many advantages such as high flexibility,programmability,and rapid implementation of new network protocols.However,in order to improve the scalability of the control plane at present,some control functionalities are added to the data plane,which is probably to impact on the generality of the data plane.The key challenge of adding control functionalities to the data plane is to strike a careful balance between the generality of the data plane and the scalability of the control plane.We propose some basic principles that both control and data planes should comply with,based on the evolutionary trend of SDN.Moreover,we take two approaches for reference according to the principles,viewed from the control messages in OpenFlow-based SDN.Our evaluations demonstrate that the approaches can maintain the generality of the data plane and improve the scalability of the control plane.展开更多
This survey provides a brief overview on the control Lyapunov function(CLF)and control barrier function(CBF)for general nonlinear-affine control systems.The problem of control is formulated as an optimization problem ...This survey provides a brief overview on the control Lyapunov function(CLF)and control barrier function(CBF)for general nonlinear-affine control systems.The problem of control is formulated as an optimization problem where the optimal control policy is derived by solving a constrained quadratic programming(QP)problem.The CLF and CBF respectively characterize the stability objective and the safety objective for the nonlinear control systems.These objectives imply important properties including controllability,convergence,and robustness of control problems.Under this framework,optimal control corresponds to the minimal solution to a constrained QP problem.When uncertainties are explicitly considered,the setting of the CLF and CBF is proposed to study the input-to-state stability and input-to-state safety and to analyze the effect of disturbances.The recent theoretic progress and novel applications of CLF and CBF are systematically reviewed and discussed in this paper.Finally,we provide research directions that are significant for the advance of knowledge in this area.展开更多
The problem of track control is studied for a class of strict-feedback stochastic nonlinear systems in which unknown virtual control gain function is the main feature.First,the so-called stochastic LaSalle theory is e...The problem of track control is studied for a class of strict-feedback stochastic nonlinear systems in which unknown virtual control gain function is the main feature.First,the so-called stochastic LaSalle theory is extended to some extent,and accordingly,the results of global ultimate boundedness for stochastic nonlinear systems are developed.Next,a new design scheme of fuzzy adaptive control is proposed.The advantage of it is that it does not require priori knowledge of virtual control gain function sign,which is usually demanded in many designs.At the same time,the track performance of closed-loop systems is improved by adaptive modifying the estimated error upper bound.By theoretical analysis,the signals of closed-loop systems are globally ultimately bounded in probability and the track error converges to a small residual set around the origin in 4th-power expectation.展开更多
The construction of control Lyapunov functions for a class of nonlinear systems is considered. We develop a method by which a control Lyapunov function for the feedback linearizable part can be constructed systematica...The construction of control Lyapunov functions for a class of nonlinear systems is considered. We develop a method by which a control Lyapunov function for the feedback linearizable part can be constructed systematically via Lyapunov equation. Moreover, by a control Lyapunov function of the feedback linearizable part and a Lyapunov function of the zero dynamics, a control Lyapunov function for the overall nonlinear system is established.展开更多
This paper studies the moving path following(MPF)problem for fixed-wing unmanned aerial vehicle(UAV)under output constraints and wind disturbances.The vehicle is required to converge to a reference path moving with re...This paper studies the moving path following(MPF)problem for fixed-wing unmanned aerial vehicle(UAV)under output constraints and wind disturbances.The vehicle is required to converge to a reference path moving with respect to the inertial frame,while the path following error is not expected to violate the predefined boundaries.Differently from existing moving path following guidance laws,the proposed method removes complex geometric transformation by formulating the moving path following problem into a second-order time-varying control problem.A nominal moving path following guidance law is designed with disturbances and their derivatives estimated by high-order disturbance observers.To guarantee that the path following error will not exceed the prescribed bounds,a robust control barrier function is developed and incorporated into controller design with quadratic program based framework.The proposed method does not require the initial position of the UAV to be within predefined boundaries.And the safety margin concept makes error-constraint be respected even if in a noisy environment.The proposed guidance law is validated through numerical simulations of shipboard landing and hardware-in-theloop(HIL)experiments.展开更多
This paper presents learning-enabled barriercertified safe controllers for systems that operate in a shared environment for which multiple systems with uncertain dynamics and behaviors interact.That is,safety constrai...This paper presents learning-enabled barriercertified safe controllers for systems that operate in a shared environment for which multiple systems with uncertain dynamics and behaviors interact.That is,safety constraints are imposed by not only the ego system’s own physical limitations but also other systems operating nearby.Since the model of the external agent is required to impose control barrier functions(CBFs)as safety constraints,a safety-aware loss function is defined and minimized to learn the uncertain and unknown behavior of external agents.More specifically,the loss function is defined based on barrier function error,instead of the system model error,and is minimized for both current samples as well as past samples stored in the memory to assure a fast and generalizable learning algorithm for approximating the safe set.The proposed model learning and CBF are then integrated together to form a learning-enabled zeroing CBF(L-ZCBF),which employs the approximated trajectory information of the external agents provided by the learned model but shrinks the safety boundary in case of an imminent safety violation using instantaneous sensory observations.It is shown that the proposed L-ZCBF assures the safety guarantees during learning and even in the face of inaccurate or simplified approximation of external agents,which is crucial in safety-critical applications in highly interactive environments.The efficacy of the proposed method is examined in a simulation of safe maneuver control of a vehicle in an urban area.展开更多
The problem of adaptive stabilization of a class of multi-input nonlinear systems with unknown parameters both in the state vector-field and the input vector-field has been considered. By employing the control Lyapuno...The problem of adaptive stabilization of a class of multi-input nonlinear systems with unknown parameters both in the state vector-field and the input vector-field has been considered. By employing the control Lyapunov function method, a direct adaptive controller is designed to complete the global adaptive stability of the uncertain system. At the same time, the controller is also verified to possess the optimality. Example and simulations are provided to illustrate the effectiveness of the proposed method.展开更多
In this paper,w e provide a novel scheme to solve the motion planning problem of multi-agent systems under high-level task specifications.First,linear temporal logic is applied to express the global task specification...In this paper,w e provide a novel scheme to solve the motion planning problem of multi-agent systems under high-level task specifications.First,linear temporal logic is applied to express the global task specification.Then an efficient and decentralized algorithm is proposed to decom pose it into local tasks.M oreover,w e use control barrier function to synthesize the local controller for each agent under the linear temporal logic motion plan with safety constraint.Finally,simulation results show the effectiveness and efficiency of our proposed scheme.展开更多
文摘The random delays in a networked control system (NCS) degrade control performance and can even destabilize the control system.To deal with this problem,the time-stamped predictive functional control (PFC) algorithm is proposed,which generalizes the standard PFC algorithm to networked control systems with random delays.The algorithm uses the time-stamp method to estimate the control delay,predicts the future outputs based on a discrete time delay state space model,and drives the control law that applies to an NCS from the idea of a PFC algorithm.A networked control system was constructed based on TrueTime simulator,with which the time-stamped PFC algorithm was compared with the standard PFC algorithm.The response curves show that the proposed algorithm has better control performance.
基金This work was supported by Young Scientists Fundamental Research Program of Shandong Province of China (No. 031B5147).
文摘In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.
基金Project(61203021)supported by the National Natural Science Foundation of ChinaProject(2011216011)supported by the Scientific and Technological Program of Liaoning Province,China+2 种基金Project(2013020024)supported by the Natural Science Foundation of Liaoning Province,ChinaProject(2012BAF05B00)supported by the National Science and Technology Support Program,ChinaProject(LJQ2015061)supported by the Program for Liaoning Excellent Talents in Universities,China
文摘The control of gas fractionation unit(GFU) in petroleum industry is very difficult due to multivariable characteristics and a large time delay.PID controllers are still applied in most industry processes.However,the traditional PID control has been proven not sufficient and capable for this particular petro-chemical process.In this work,an incremental multivariable predictive functional control(IMPFC) algorithm was proposed with less online computation,great precision and fast response.An incremental transfer function matrix model was set up through the step-response data,and predictive outputs were deduced with the theory of single-value optimization.The results show that the method can optimize the incremental control variable and reject the constraint of the incremental control variable with the positional predictive functional control algorithm,and thereby making the control variable smoother.The predictive output error and future set-point were approximated by a polynomial,which can overcome the problem under the model mismatch and make the predictive outputs track the reference trajectory.Then,the design of incremental multivariable predictive functional control was studied.Simulation and application results show that the proposed control strategy is effective and feasible to improve control performance and robustness of process.
基金Project(2007AA04Z162) supported by the National High-Tech Research and Development Program of ChinaProjects(2006T089, 2009T062) supported by the University Innovation Team in the Educational Department of Liaoning Province, China
文摘In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive functional control(AFPFC) scheme for multivariable nonlinear systems was proposed.Firstly,multivariable nonlinear systems were described based on Takagi-Sugeno(T-S) fuzzy models;assuming that the antecedent parameters of T-S models were kept,the consequent parameters were identified on-line by using the weighted recursive least square(WRLS) method.Secondly,the identified T-S models were linearized to be time-varying state space model at each sampling instant.Finally,by using linear predictive control technique the analysis solution of the optimal control law of AFPFC was established.The application results for pH neutralization process show that the absolute error between the identified T-S model output and the process output is smaller than 0.015;the tracking ability of the proposed AFPFC is superior to that of non-AFPFC(NAFPFC) for pH process without disturbances,the overshoot of the effluent pH value of AFPFC with disturbances is decreased by 50% compared with that of NAFPFC;when the process parameters of AFPFC vary with time the integrated absolute error(IAE) performance index still retains to be less than 200 compared with that of NAFPFC.
基金This work was supported by National Science Fundation of China (No.60274032)Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) (No.20030248040)and Alexander von Humboldt Research Fellowship
文摘The predictive model is built according to the characteristics of the impulse response of integrating process. In order to eliminate the permanent offset between the setpoint and the process output in the presence of the load disturbance, a novel error compensation method is proposed. Then predictive functional control of integrating process is designed. The method given generates a simple control structure, which can significandy reduce online computation. Furthermore, the tuning of the controller is fairly straightforward. Simulation results indicate that the designed control system is relatively robust to the parameters variation of the process.
文摘Reaction control system(RCS) is a powerful and efficient actuator for space vehicles attitude control, which is typically characterized as a pulsed unilateral effector only with two states(off/on). Along with inevitable internal uncertainties and external disturbances in practice, this inherent nonlinear character always hinders space vehicles autopilot from pursuing precise tracking performance. Compared to most of pre-existing methodologies that passively suppress the uncertainties and disturbances, a design based on predictive functional control(PFC) and generalized extended state observer(GESO) is firstly proposed for three-axis RCS control system to actively reject that with no requirement for additional fuel consumption. To obtain a high fidelity predictive model on which the performance of PFC greatly depends, the nonlinear coupling multiple-input multiple-output(MIMO) flight dynamics model is parameterized as a state-dependent coefficient form. And based on that, a MIMO PFC algorithm in state space domain for a plant of arbitrary orders is deduced in this paper.The internal uncertainties and external disturbances are lumped as a total disturbance, which is estimated and cancelled timely to further enhance the robustness. The continuous control command synthesised by above controller-rejector tandem is finally modulated by pulse width pulse frequency modulator(PWPF) to on-off signals to meet RCS requirement. The robustness and feasibility of the proposed design are validated by a series of performance comparison simulations with some prominent methods in the presence of significant perturbations and disturbances, as well as measurement noise.
基金Supported by the National Creative Research Groups Science Foundation of China (NCRGSFC 60421002)the National High Technology Research and Development Program of China (863 Program,2006AA04Z182).
文摘By extending the system's state variables,a novel predictive functional controller has been developed.The structure of this controller is similar to that of classical proportional integral(PI)optimal controller and in-cludes a control block that can perform a feed-forward control of future P-step set points.It considers both the state variables and the output errors in its cost function,which results in enhanced control performance compared with traditional state space predictive functional control(TSSPFC)methods that consider only the predictive output er-rors.The predictive functional controller(PFC)has been compared with TSSPFC in terms of tracking ability,dis-turbance rejection,and also based on its application to heavy oil coking equipment.The results obtained show the effectiveness of the controller.
文摘The use of living, alkyllithium-initiated anionic polymerization to prepare chain-end functionalized polymers and heteroarm, star-branched polymers is discussed. The scope and limitations of specific termination reactions with a variety of electrophilic species are illustrated for carbonation, hydroxyethylation, amination, and sulfonation. The methodology of using substituted 1,1-diphenylethylenes to provide a general, quantitative functionalization procedure is outlined and illustrated with examples of amine and phenol end-functionalization. A methodology is described for the synthesis of functionalized, star-branched copolymers with compositionally heterogeneous arms of controlled molecular weight and narrow molecular weight distribution using 1, 3-bis(1-pbenylethenyl) benzene.
文摘The main principle and the characteristic of Predictive Functional Control (PFC) strategy are presented in this paper and the corresponding control system aid design software APC-PFC is also introduced. For a chlorinated polyethylene (CPE) process, a design scheme of cascade predictive functional control system is described and the control performance is improved obviously.
基金supported by the Fondo para el Primer Proyecto of the Comitépara el Desarrollo de la Investigación(CODI)at the Universidad de Antioquia(Grant Number PRV2024-78509)。
文摘The objective of this paper is to present a robust safety-critical control system based on the active disturbance rejection control approach, designed to guarantee safety even in the presence of model inaccuracies, unknown dynamics, and external disturbances. The proposed method combines control barrier functions and control Lyapunov functions with a nonlinear extended state observer to produce a robust and safe control strategy for dynamic systems subject to uncertainties and disturbances. This control strategy employs an optimization-based control, supported by the disturbance estimation from a nonlinear extended state observer. Using a quadratic programming algorithm, the controller computes an optimal, stable, and safe control action at each sampling instant. The effectiveness of the proposed approach is demonstrated through numerical simulations of a safety-critical interconnected adaptive cruise control system.
基金supported in part the National Key Research and Development Program of China(2021YFC2902703)Open Foundation of State Key Laboratory of Process Automation in Mining&Metallurgy/Beijing Key Laboratory of Process Automation in Mining&Metallurgy(BGRIMM-KZSKL-2022-6)the National Natural Science Foundation of China(62173078,61873049).
文摘In this work,a self-healing predictive control method for discrete-time nonlinear systems is presented to ensure the system can be safely operated under abnormal states.First,a robust MPC controller for the normal case is constructed,which can drive the system to the equilibrium point when the closed-loop states are in the predetermined safe set.In this controller,the tubes are built based on the incremental Lyapunov function to tighten nominal constraints.To deal with the infeasible controller when abnormal states occur,a self-healing predictive control method is further proposed to realize self-healing by driving the system towards the safe set.This is achieved by an auxiliary softconstrained recovery mechanism that can solve the constraint violation caused by the abnormal states.By extending the discrete-time robust control barrier function theory,it is proven that the auxiliary problem provides a predictive control barrier bounded function to make the system asymptotically stable towards the safe set.The theoretical properties of robust recursive feasibility and bounded stability are further analyzed.The efficiency of the proposed controller is verified by a numerical simulation of a continuous stirred-tank reactor process.
基金supported in part by the National Science and Technology Major Project(2022ZD0119902)the National Natural Science Foundation of China(52471372,623B2018,62203015,62233001)+4 种基金the Liaoning Revitalization Leading Talents Program(XLYC2402054)the Key Basic Research of Dalian(2023JJ11CG008)the Fundamental Research Funds for the Central Universities(3132023508)the Collaborative Research Fund of Hong Kong Research Grants Council(C1013-24G)the Cultivation Program for the Excellent Doctoral Dissertation of Dalian Maritime University(2023YBPY005).
文摘This paper addresses the parallel control of autonomous surface vehicles subject to external disturbances,state constraints,and input constraints in complex ocean environments with multiple obstacles.A safety-certified parallel model predictive control scheme with collision-avoiding capability is proposed for autonomous surface vehicles in the framework of parallel control.Specifically,an extended state observer is designed by leveraging historical and real-time data for concurrent learning to map the motion of autonomous surface vehicles from its physical system to its artificial counterpart.A parallel model predictive control law is developed on the basis of the artificial system for both physical and artificial autonomous surface vehicles to realize virtual-physical tracking control of vehicles subject to state and input constraints.To ensure safety,highorder discrete control barrier functions are encoded in the parallel model predictive control law as safety constraints such that collision avoidance with obstacles can be achieved.A recedinghorizon constrained optimization problem is constructed with the safety constraints encoded by control barrier functions for parallel model predictive control of autonomous surface vehicles and solved via neurodynamic optimization with projection neural networks.The effectiveness and characteristics of the proposed method are demonstrated via simulations for the safe trajectory tracking and automatic berthing of autonomous surface vehicles.
文摘The control and data planes are decoupled in software-defined networking(SDN),which enables both planes to evolve independently,and brings about many advantages such as high flexibility,programmability,and rapid implementation of new network protocols.However,in order to improve the scalability of the control plane at present,some control functionalities are added to the data plane,which is probably to impact on the generality of the data plane.The key challenge of adding control functionalities to the data plane is to strike a careful balance between the generality of the data plane and the scalability of the control plane.We propose some basic principles that both control and data planes should comply with,based on the evolutionary trend of SDN.Moreover,we take two approaches for reference according to the principles,viewed from the control messages in OpenFlow-based SDN.Our evaluations demonstrate that the approaches can maintain the generality of the data plane and improve the scalability of the control plane.
基金supported in part by the National Natural Science Foundation of China(U22B2046,62073079,62088101)in part by the General Joint Fund of the Equipment Advance Research Program of Ministry of Education(8091B022114)in part by NPRP(NPRP 9-466-1-103)from Qatar National Research Fund。
文摘This survey provides a brief overview on the control Lyapunov function(CLF)and control barrier function(CBF)for general nonlinear-affine control systems.The problem of control is formulated as an optimization problem where the optimal control policy is derived by solving a constrained quadratic programming(QP)problem.The CLF and CBF respectively characterize the stability objective and the safety objective for the nonlinear control systems.These objectives imply important properties including controllability,convergence,and robustness of control problems.Under this framework,optimal control corresponds to the minimal solution to a constrained QP problem.When uncertainties are explicitly considered,the setting of the CLF and CBF is proposed to study the input-to-state stability and input-to-state safety and to analyze the effect of disturbances.The recent theoretic progress and novel applications of CLF and CBF are systematically reviewed and discussed in this paper.Finally,we provide research directions that are significant for the advance of knowledge in this area.
基金Supported by National Natural Science Foundation of P.R.China(60572070,60325311,60534010)Natural Science Foundation of Liaoning Province(20022030)
文摘The problem of track control is studied for a class of strict-feedback stochastic nonlinear systems in which unknown virtual control gain function is the main feature.First,the so-called stochastic LaSalle theory is extended to some extent,and accordingly,the results of global ultimate boundedness for stochastic nonlinear systems are developed.Next,a new design scheme of fuzzy adaptive control is proposed.The advantage of it is that it does not require priori knowledge of virtual control gain function sign,which is usually demanded in many designs.At the same time,the track performance of closed-loop systems is improved by adaptive modifying the estimated error upper bound.By theoretical analysis,the signals of closed-loop systems are globally ultimately bounded in probability and the track error converges to a small residual set around the origin in 4th-power expectation.
基金Supported by Natural Science Foundation of Zhejiang Province P. R. China (Y105141)Natural Science Foundation of Fujian Province P.R.China (A0510025)Technological Project of Zhejiang Education Department,P. R. China(20050291)
文摘The construction of control Lyapunov functions for a class of nonlinear systems is considered. We develop a method by which a control Lyapunov function for the feedback linearizable part can be constructed systematically via Lyapunov equation. Moreover, by a control Lyapunov function of the feedback linearizable part and a Lyapunov function of the zero dynamics, a control Lyapunov function for the overall nonlinear system is established.
基金supported in part by the National Natural Science Foundations of China(62173016,62073019)the Fundamental Research Funds for the Central Universities(YWF-23-JC-04,YWF-23-JC-02)。
文摘This paper studies the moving path following(MPF)problem for fixed-wing unmanned aerial vehicle(UAV)under output constraints and wind disturbances.The vehicle is required to converge to a reference path moving with respect to the inertial frame,while the path following error is not expected to violate the predefined boundaries.Differently from existing moving path following guidance laws,the proposed method removes complex geometric transformation by formulating the moving path following problem into a second-order time-varying control problem.A nominal moving path following guidance law is designed with disturbances and their derivatives estimated by high-order disturbance observers.To guarantee that the path following error will not exceed the prescribed bounds,a robust control barrier function is developed and incorporated into controller design with quadratic program based framework.The proposed method does not require the initial position of the UAV to be within predefined boundaries.And the safety margin concept makes error-constraint be respected even if in a noisy environment.The proposed guidance law is validated through numerical simulations of shipboard landing and hardware-in-theloop(HIL)experiments.
文摘This paper presents learning-enabled barriercertified safe controllers for systems that operate in a shared environment for which multiple systems with uncertain dynamics and behaviors interact.That is,safety constraints are imposed by not only the ego system’s own physical limitations but also other systems operating nearby.Since the model of the external agent is required to impose control barrier functions(CBFs)as safety constraints,a safety-aware loss function is defined and minimized to learn the uncertain and unknown behavior of external agents.More specifically,the loss function is defined based on barrier function error,instead of the system model error,and is minimized for both current samples as well as past samples stored in the memory to assure a fast and generalizable learning algorithm for approximating the safe set.The proposed model learning and CBF are then integrated together to form a learning-enabled zeroing CBF(L-ZCBF),which employs the approximated trajectory information of the external agents provided by the learned model but shrinks the safety boundary in case of an imminent safety violation using instantaneous sensory observations.It is shown that the proposed L-ZCBF assures the safety guarantees during learning and even in the face of inaccurate or simplified approximation of external agents,which is crucial in safety-critical applications in highly interactive environments.The efficacy of the proposed method is examined in a simulation of safe maneuver control of a vehicle in an urban area.
文摘The problem of adaptive stabilization of a class of multi-input nonlinear systems with unknown parameters both in the state vector-field and the input vector-field has been considered. By employing the control Lyapunov function method, a direct adaptive controller is designed to complete the global adaptive stability of the uncertain system. At the same time, the controller is also verified to possess the optimality. Example and simulations are provided to illustrate the effectiveness of the proposed method.
基金This work was partially supported by the National Natural Science Foundation of China(No.51475334)the National Key Research and Development Program of Science and Technology of China(No.2018YFB1305304)the Shanghai Science and Technology Pilot Project(No.19511132100).
文摘In this paper,w e provide a novel scheme to solve the motion planning problem of multi-agent systems under high-level task specifications.First,linear temporal logic is applied to express the global task specification.Then an efficient and decentralized algorithm is proposed to decom pose it into local tasks.M oreover,w e use control barrier function to synthesize the local controller for each agent under the linear temporal logic motion plan with safety constraint.Finally,simulation results show the effectiveness and efficiency of our proposed scheme.
