The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of trea...The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of treatment. How enterprises can make reasonable decisions on their water environment behavior based on the external environment and their own factors is of great significance for scientifically and effectively designing water environment regulation mechanisms. Based on optimal control theory, this study investigates the design of contractual mechanisms for water environmental regulation for small and medium-sized enterprises. The enterprise is regarded as an independent economic entity that can adopt optimal control strategies to maximize its own interests. Based on the participation of multiple subjects including the government, enterprises, and the public, an optimal control strategy model for enterprises under contractual water environmental regulation is constructed using optimal control theory, and a method for calculating the amount of unit pollutant penalties is derived. The water pollutant treatment cost data of a paper company is selected to conduct empirical numerical analysis on the model. The results show that the increase in the probability of government regulation and public participation, as well as the decrease in local government protection for enterprises, can achieve the same regulatory effect while reducing the number of administrative penalties per unit. Finally, the implementation process of contractual water environmental regulation for small and medium-sized enterprises is designed.展开更多
In the dynamics analysis and synthesis of a controlled system, it is important to know for what feedback gains can the controlled system decay to the demanded steady state as fast as possible. This article presents a ...In the dynamics analysis and synthesis of a controlled system, it is important to know for what feedback gains can the controlled system decay to the demanded steady state as fast as possible. This article presents a systematic method for finding the optimal feedback gains by taking the stability of an inverted pendulum system with a delayed proportional-derivative controller as an example. First, the condition for the existence and uniqueness of the stable region in the gain plane is obtained by using the D-subdivision method and the method of stability switch. Then the same procedure is used repeatedly to shrink the stable region by decreasing the real part of the rightmost characteristic root. Finally, the optimal feedback gains within the stable region that minimizes the real part of the rightmost root are expressed by an explicit formula. With the optimal feedback gains, the controlled inverted pendulum decays to its trivial equilibrium at the fastest speed when the initial values around the origin are fixed. The main results are checked by numerical simulation.展开更多
As the extension of the linear inverted pendulum(LIP) and planar inverted pendulum(PIP), this paper proposes a novel spatial inverted pendulum(SIP). The SIP is the most general inverted pendulum(IP) than any existing ...As the extension of the linear inverted pendulum(LIP) and planar inverted pendulum(PIP), this paper proposes a novel spatial inverted pendulum(SIP). The SIP is the most general inverted pendulum(IP) than any existing IP. The model of the SIP is presented for the first time. The SIP inherits all the characteristics of the LIP and the PIP, which is a nonlinear,unstable and underactuated system. The SIP has five degrees of motion freedom and three control forces. Thus, it is a multipleinput and multiple-output(MIMO) system with nonlinear dynamics. To realize the spatial trajectory tracking of the SIP,the control structure with five PID controllers will be designed.The parameter tuning of the multiple PIDs is a challenging work for the proposed SIP model. To alleviate the difficulties of the parameter tuning for the multiple PID controllers, optimal PIDs can be achieved with the help of Big Bang – Big Crunch(BBBC) optimization. The BBBC algorithm can successfully optimize the parameters of the multiple PID controllers with high convergence speed. The optimization performance index of the BBBC algorithm is compared with that of the particle swarm optimization(PSO). Simulation results certify the rightness and effectiveness of the proposed control and optimization methods.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
This article presents a distributed periodic eventtriggered(PET)optimal control scheme to achieve generation cost minimization and average bus voltage regulation in DC microgrids.In order to accommodate the generation...This article presents a distributed periodic eventtriggered(PET)optimal control scheme to achieve generation cost minimization and average bus voltage regulation in DC microgrids.In order to accommodate the generation constraints of the distributed generators(DGs),a virtual incremental cost is firstly designed,based on which an optimality condition is derived to facilitate the control design.To meet the discrete-time(DT)nature of modern control systems,the optimal controller is directly developed in the DT domain.Afterward,to reduce the communication requirement among the controllers,a distributed event-triggered mechanism is introduced for the DT optimal controller.The event-triggered condition is detected periodically and therefore naturally avoids the Zeno phenomenon.The closed-loop system stability is proved by the Lyapunov synthesis for switched systems.The generation cost minimization and average bus voltage regulation are obtained at the equilibrium point.Finally,switch-level microgrid simulations validate the performance of the proposed optimal controller.展开更多
In this paper, an efficient computational approach is proposed to solve the discrete time nonlinear stochastic optimal control problem. For this purpose, a linear quadratic regulator model, which is a linear dynamical...In this paper, an efficient computational approach is proposed to solve the discrete time nonlinear stochastic optimal control problem. For this purpose, a linear quadratic regulator model, which is a linear dynamical system with the quadratic criterion cost function, is employed. In our approach, the model-based optimal control problem is reformulated into the input-output equations. In this way, the Hankel matrix and the observability matrix are constructed. Further, the sum squares of output error is defined. In these point of views, the least squares optimization problem is introduced, so as the differences between the real output and the model output could be calculated. Applying the first-order derivative to the sum squares of output error, the necessary condition is then derived. After some algebraic manipulations, the optimal control law is produced. By substituting this control policy into the input-output equations, the model output is updated iteratively. For illustration, an example of the direct current and alternating current converter problem is studied. As a result, the model output trajectory of the least squares solution is close to the real output with the smallest sum squares of output error. In conclusion, the efficiency and the accuracy of the approach proposed are highly presented.展开更多
In this paper, a practical analysis of stability by simulation for the effect of incorporating a Kalman estimator in the control loop of the inverted pendulum with a neurocontroller is presented. The neurocontroller i...In this paper, a practical analysis of stability by simulation for the effect of incorporating a Kalman estimator in the control loop of the inverted pendulum with a neurocontroller is presented. The neurocontroller is calculated by approximate optimal control, without considering the Kalman estimator in the loop following the Theorem of the separation. The results are compared with a time-varying linear controller, which in noiseless conditions in the state or in the measurement has an acceptable performance, but when it is under noise conditions its operation closes into a state space range more limited than the one proposed here.展开更多
This paper presents a new solution to the inverse problem of linear optimal regulators to minimize a cost function and meet the requirements of relative stability in the presence of a constant but unknown disturbance....This paper presents a new solution to the inverse problem of linear optimal regulators to minimize a cost function and meet the requirements of relative stability in the presence of a constant but unknown disturbance. A state feedback matrix is developed using Lyapunov’s second method. Moreover, the relationships between the state feedback matrix and the cost function are obtained, and a formula to solve the weighting matrices is suggest- ed. The developed method is applied successfully to design the horizontal loops in the inertial navigation system.展开更多
The dynamics of 2DOF spherical inverted pendulum system is analyzed. The motion of the pendulum may be projected onto the orthogonal planes in the Cartesian Space. In this way the system can be decoupled into two clas...The dynamics of 2DOF spherical inverted pendulum system is analyzed. The motion of the pendulum may be projected onto the orthogonal planes in the Cartesian Space. In this way the system can be decoupled into two classical cart-pendulum systems and the design of controllers aimed at each subsystem separately are proposed. The linear quadratic optimal control strategy is applied in order to balance the pendulum system at the 'inverted' status. The method proposed is verified by the simulation and actual system experiments and the performance of the controller is discussed.展开更多
This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems(SPSs) with unknown dynamics based on reinforcement learning(RL).Taking into account the sl...This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems(SPSs) with unknown dynamics based on reinforcement learning(RL).Taking into account the slow and fast characteristics among system states,the interconnected SPS is decomposed into the slow time-scale dynamics and the fast timescale dynamics through singular perturbation theory.For the fast time-scale dynamics with interconnections,we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost function.For the slow time-scale dynamics with unknown system parameters,an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement data.By combining the slow and fast controllers,we establish the composite decentralized adaptive optimal output regulator,and rigorously analyze the stability and optimality of the closed-loop system.The proposed decomposition design not only bypasses the numerical stiffness but also alleviates the high-dimensionality.The efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.展开更多
Linear quadratic regulator(LQR) and proportional-integral-derivative(PID) control methods, which are generally used for control of linear dynamical systems, are used in this paper to control the nonlinear dynamical sy...Linear quadratic regulator(LQR) and proportional-integral-derivative(PID) control methods, which are generally used for control of linear dynamical systems, are used in this paper to control the nonlinear dynamical system. LQR is one of the optimal control techniques, which takes into account the states of the dynamical system and control input to make the optimal control decisions.The nonlinear system states are fed to LQR which is designed using a linear state-space model. This is simple as well as robust. The inverted pendulum, a highly nonlinear unstable system, is used as a benchmark for implementing the control methods. Here the control objective is to control the system such that the cart reaches a desired position and the inverted pendulum stabilizes in the upright position. In this paper, the modeling and simulation for optimal control design of nonlinear inverted pendulum-cart dynamic system using PID controller and LQR have been presented for both cases of without and with disturbance input. The Matlab-Simulink models have been developed for simulation and performance analysis of the control schemes. The simulation results justify the comparative advantage of LQR control method.展开更多
In this paper,the problem of inverse quadratic optimal control over fnite time-horizon for discrete-time linear systems is considered.Our goal is to recover the corresponding quadratic objective function using noisy o...In this paper,the problem of inverse quadratic optimal control over fnite time-horizon for discrete-time linear systems is considered.Our goal is to recover the corresponding quadratic objective function using noisy observations.First,the identifability of the model structure for the inverse optimal control problem is analyzed under relative degree assumption and we show the model structure is strictly globally identifable.Next,we study the inverse optimal control problem whose initial state distribution and the observation noise distribution are unknown,yet the exact observations on the initial states are available.We formulate the problem as a risk minimization problem and approximate the problem using empirical average.It is further shown that the solution to the approximated problem is statistically consistent under the assumption of relative degrees.We then study the case where the exact observations on the initial states are not available,yet the observation noises are known to be white Gaussian distributed and the distribution of the initial state is also Gaussian(with unknown mean and covariance).EM-algorithm is used to estimate the parameters in the objective function.The efectiveness of our results are demonstrated by numerical examples.展开更多
In this paper, the inverse linear quadratic(LQ) problem over finite time-horizon is studied.Given the output observations of a dynamic process, the goal is to recover the corresponding LQ cost function. Firstly, by co...In this paper, the inverse linear quadratic(LQ) problem over finite time-horizon is studied.Given the output observations of a dynamic process, the goal is to recover the corresponding LQ cost function. Firstly, by considering the inverse problem as an identification problem, its model structure is shown to be strictly globally identifiable under the assumption of system invertibility. Next, in the noiseless case a necessary and sufficient condition is proposed for the solvability of a positive semidefinite weighting matrix and its unique solution is obtained with two proposed algorithms under the condition of persistent excitation. Furthermore, a residual optimization problem is also formulated to solve a best-fit approximate cost function from sub-optimal observations. Finally, numerical simulations are used to demonstrate the effectiveness of the proposed methods.展开更多
This paper presents LQR sliding surface-based Sliding Mode Controller(LQR-SMC)for balancing control of a Rotary Double Inverted Pendulum(RDIP)system.It is a challenging research topic in control engineering due to its...This paper presents LQR sliding surface-based Sliding Mode Controller(LQR-SMC)for balancing control of a Rotary Double Inverted Pendulum(RDIP)system.It is a challenging research topic in control engineering due to its nonlinearity and instability.The RDIP system uses only a motor to control two serially connected pendulums to stand at the upright position.The sliding surface is designed based on the LQR optimal gain.Nonsingular gain matrix is obtained by using the left inverse of the input matrix in the state space form of the system dynamics.The Lyapunov stability theory is used to determine the stability of the controller.To evaluate the performance of LQR-SMC,some performance indices,including the Integral Absolute Error(IAE),Integral Time Absolute Error(ITAE),and the Integrated Square Error(ISE),are used.System stability can be maintained by LQR-SMC under external disturbances as well as model and parameter uncertainties.展开更多
In this paper, at first, the single input rule modules(SIRMs) dynamically connected fuzzy inference model is used to stabilize a double inverted pendulum system. Then, a multiobjective particle swarm optimization(MOPS...In this paper, at first, the single input rule modules(SIRMs) dynamically connected fuzzy inference model is used to stabilize a double inverted pendulum system. Then, a multiobjective particle swarm optimization(MOPSO) is implemented to optimize the fuzzy controller parameters in order to decrease the distance error of the cart and summation of the angle errors of the pendulums, simultaneously. The feasibility and efficiency of the proposed Pareto front is assessed in comparison with results reported in literature and obtained from other algorithms.Finally, the Java programming with applets is utilized to simulate the stability of the nonlinear system and explain the internetbased control.展开更多
Designing advanced design techniques for feedback stabilization and optimization of complex systems is important to the modern control field. In this paper, a near-optimal regulation method for general nonaffine dynam...Designing advanced design techniques for feedback stabilization and optimization of complex systems is important to the modern control field. In this paper, a near-optimal regulation method for general nonaffine dynamics is developed with the help of policy learning. For addressing the nonaffine nonlinearity, a pre-compensator is constructed, so that the augmented system can be formulated as affine-like form. Different cost functions are defined for original and transformed controlled plants and then their relationship is analyzed in detail. Additionally, an adaptive critic algorithm involving stability guarantee is employed to solve the augmented optimal control problem. At last, several case studies are conducted for verifying the stability, robustness, and optimality of a torsional pendulum plant with suitable cost.展开更多
This study proposes a two-stage photovoltaic(PV)voltage control strategy for centralized control that ignores short-term load fluctuations.In the first stage,a deterministic power flow model optimizes the 15-minute ac...This study proposes a two-stage photovoltaic(PV)voltage control strategy for centralized control that ignores short-term load fluctuations.In the first stage,a deterministic power flow model optimizes the 15-minute active cycle of the inverter and reactive outputs to reduce network loss and light rejection.In the second stage,the local control stabilizes the fluctuations and tracks the system state of the first stage.The uncertain interval model establishes a chance constraint model for the inverter voltage-reactive power local control.Second-order cone optimization and sensitivity theories were employed to solve the models.The effectiveness of the model was confirmed using a modified IEEE 33 bus example.The intraday control outcome for distributed power generation considering the effects of fluctuation uncertainty,PV penetration rate,and inverter capacity is analyzed.展开更多
Two novel improved variants of reptile search algorithm(RSA),RSA with opposition-based learning(ORSA)and hybrid ORSA with pattern search(ORSAPS),are proposed to determine the proportional,integral,and derivative(PID)c...Two novel improved variants of reptile search algorithm(RSA),RSA with opposition-based learning(ORSA)and hybrid ORSA with pattern search(ORSAPS),are proposed to determine the proportional,integral,and derivative(PID)controller parameters of an automatic voltage regulator(AVR)system using a novel objective function with augmented flexibility.In the proposed algorithms,the opposition-based learning technique improves the global search abilities of the original RSA algorithm,while the hybridization with the pattern search(PS)algorithm improves the local search abilities.Both algorithms are compared with the original RSA algorithm and have shown to be highly effective algorithms for tuning the PID controller parameters of an AVR system by getting superior results.Several analyses such as transient,stability,robustness,disturbance rejection,and trajectory tracking are conducted to test the performance of the proposed algorithms,which have validated the good promise of the proposed methods for controller designs.The performances of the proposed design approaches are also compared with the previously reported PID controller parameter tuning approaches to assess their success.It is shown that both proposed approaches obtain excellent and robust results among all compared ones.That is,with the adjustment of the weight factorα,which is introduced by the proposed objective function,for a system with high bandwitdh(α=1),the proposed ORSAPS-PID system has 2.08%more bandwidth than the proposed ORSA-PID system and 5.1%faster than the fastest algorithm from the literature.On the other hand,for a system where high phase and gain margins are desired(α=10),the proposed ORSA-PID system has 0.53%more phase margin and 2.18%more gain margin than the proposed ORSAPS-PID system and has 0.71%more phase margin and 2.25%more gain margin than the best performing algorithm from the literature.展开更多
文摘The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of treatment. How enterprises can make reasonable decisions on their water environment behavior based on the external environment and their own factors is of great significance for scientifically and effectively designing water environment regulation mechanisms. Based on optimal control theory, this study investigates the design of contractual mechanisms for water environmental regulation for small and medium-sized enterprises. The enterprise is regarded as an independent economic entity that can adopt optimal control strategies to maximize its own interests. Based on the participation of multiple subjects including the government, enterprises, and the public, an optimal control strategy model for enterprises under contractual water environmental regulation is constructed using optimal control theory, and a method for calculating the amount of unit pollutant penalties is derived. The water pollutant treatment cost data of a paper company is selected to conduct empirical numerical analysis on the model. The results show that the increase in the probability of government regulation and public participation, as well as the decrease in local government protection for enterprises, can achieve the same regulatory effect while reducing the number of administrative penalties per unit. Finally, the implementation process of contractual water environmental regulation for small and medium-sized enterprises is designed.
基金supported by the National Natural Science Foundation of China (Grant 11372354)the Fund of the State Key Lab of Mechanics and Control of Mechanical Structures (Grant MCMS-0116K01)
文摘In the dynamics analysis and synthesis of a controlled system, it is important to know for what feedback gains can the controlled system decay to the demanded steady state as fast as possible. This article presents a systematic method for finding the optimal feedback gains by taking the stability of an inverted pendulum system with a delayed proportional-derivative controller as an example. First, the condition for the existence and uniqueness of the stable region in the gain plane is obtained by using the D-subdivision method and the method of stability switch. Then the same procedure is used repeatedly to shrink the stable region by decreasing the real part of the rightmost characteristic root. Finally, the optimal feedback gains within the stable region that minimizes the real part of the rightmost root are expressed by an explicit formula. With the optimal feedback gains, the controlled inverted pendulum decays to its trivial equilibrium at the fastest speed when the initial values around the origin are fixed. The main results are checked by numerical simulation.
基金supported by the National Natural Science Foundation of China(61873079)。
文摘As the extension of the linear inverted pendulum(LIP) and planar inverted pendulum(PIP), this paper proposes a novel spatial inverted pendulum(SIP). The SIP is the most general inverted pendulum(IP) than any existing IP. The model of the SIP is presented for the first time. The SIP inherits all the characteristics of the LIP and the PIP, which is a nonlinear,unstable and underactuated system. The SIP has five degrees of motion freedom and three control forces. Thus, it is a multipleinput and multiple-output(MIMO) system with nonlinear dynamics. To realize the spatial trajectory tracking of the SIP,the control structure with five PID controllers will be designed.The parameter tuning of the multiple PIDs is a challenging work for the proposed SIP model. To alleviate the difficulties of the parameter tuning for the multiple PID controllers, optimal PIDs can be achieved with the help of Big Bang – Big Crunch(BBBC) optimization. The BBBC algorithm can successfully optimize the parameters of the multiple PID controllers with high convergence speed. The optimization performance index of the BBBC algorithm is compared with that of the particle swarm optimization(PSO). Simulation results certify the rightness and effectiveness of the proposed control and optimization methods.
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.
基金supported by the U.S.Office of Naval Research(N00014-21-1-2175)。
文摘This article presents a distributed periodic eventtriggered(PET)optimal control scheme to achieve generation cost minimization and average bus voltage regulation in DC microgrids.In order to accommodate the generation constraints of the distributed generators(DGs),a virtual incremental cost is firstly designed,based on which an optimality condition is derived to facilitate the control design.To meet the discrete-time(DT)nature of modern control systems,the optimal controller is directly developed in the DT domain.Afterward,to reduce the communication requirement among the controllers,a distributed event-triggered mechanism is introduced for the DT optimal controller.The event-triggered condition is detected periodically and therefore naturally avoids the Zeno phenomenon.The closed-loop system stability is proved by the Lyapunov synthesis for switched systems.The generation cost minimization and average bus voltage regulation are obtained at the equilibrium point.Finally,switch-level microgrid simulations validate the performance of the proposed optimal controller.
文摘In this paper, an efficient computational approach is proposed to solve the discrete time nonlinear stochastic optimal control problem. For this purpose, a linear quadratic regulator model, which is a linear dynamical system with the quadratic criterion cost function, is employed. In our approach, the model-based optimal control problem is reformulated into the input-output equations. In this way, the Hankel matrix and the observability matrix are constructed. Further, the sum squares of output error is defined. In these point of views, the least squares optimization problem is introduced, so as the differences between the real output and the model output could be calculated. Applying the first-order derivative to the sum squares of output error, the necessary condition is then derived. After some algebraic manipulations, the optimal control law is produced. By substituting this control policy into the input-output equations, the model output is updated iteratively. For illustration, an example of the direct current and alternating current converter problem is studied. As a result, the model output trajectory of the least squares solution is close to the real output with the smallest sum squares of output error. In conclusion, the efficiency and the accuracy of the approach proposed are highly presented.
文摘In this paper, a practical analysis of stability by simulation for the effect of incorporating a Kalman estimator in the control loop of the inverted pendulum with a neurocontroller is presented. The neurocontroller is calculated by approximate optimal control, without considering the Kalman estimator in the loop following the Theorem of the separation. The results are compared with a time-varying linear controller, which in noiseless conditions in the state or in the measurement has an acceptable performance, but when it is under noise conditions its operation closes into a state space range more limited than the one proposed here.
基金Project supported by the Hong Kong Polytechnic University(A/C 350/555)
文摘This paper presents a new solution to the inverse problem of linear optimal regulators to minimize a cost function and meet the requirements of relative stability in the presence of a constant but unknown disturbance. A state feedback matrix is developed using Lyapunov’s second method. Moreover, the relationships between the state feedback matrix and the cost function are obtained, and a formula to solve the weighting matrices is suggest- ed. The developed method is applied successfully to design the horizontal loops in the inertial navigation system.
文摘The dynamics of 2DOF spherical inverted pendulum system is analyzed. The motion of the pendulum may be projected onto the orthogonal planes in the Cartesian Space. In this way the system can be decoupled into two classical cart-pendulum systems and the design of controllers aimed at each subsystem separately are proposed. The linear quadratic optimal control strategy is applied in order to balance the pendulum system at the 'inverted' status. The method proposed is verified by the simulation and actual system experiments and the performance of the controller is discussed.
基金supported by the National Natural Science Foundation of China (62073327,62273350)the Natural Science Foundation of Jiangsu Province (BK20221112)。
文摘This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems(SPSs) with unknown dynamics based on reinforcement learning(RL).Taking into account the slow and fast characteristics among system states,the interconnected SPS is decomposed into the slow time-scale dynamics and the fast timescale dynamics through singular perturbation theory.For the fast time-scale dynamics with interconnections,we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost function.For the slow time-scale dynamics with unknown system parameters,an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement data.By combining the slow and fast controllers,we establish the composite decentralized adaptive optimal output regulator,and rigorously analyze the stability and optimality of the closed-loop system.The proposed decomposition design not only bypasses the numerical stiffness but also alleviates the high-dimensionality.The efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.
文摘Linear quadratic regulator(LQR) and proportional-integral-derivative(PID) control methods, which are generally used for control of linear dynamical systems, are used in this paper to control the nonlinear dynamical system. LQR is one of the optimal control techniques, which takes into account the states of the dynamical system and control input to make the optimal control decisions.The nonlinear system states are fed to LQR which is designed using a linear state-space model. This is simple as well as robust. The inverted pendulum, a highly nonlinear unstable system, is used as a benchmark for implementing the control methods. Here the control objective is to control the system such that the cart reaches a desired position and the inverted pendulum stabilizes in the upright position. In this paper, the modeling and simulation for optimal control design of nonlinear inverted pendulum-cart dynamic system using PID controller and LQR have been presented for both cases of without and with disturbance input. The Matlab-Simulink models have been developed for simulation and performance analysis of the control schemes. The simulation results justify the comparative advantage of LQR control method.
基金supported by the National Natural Science Foundation of China under Grant Nos.61074044, 61104038,and 60834004Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No.20090041110003the National 973 Basic Research Program of China under Grant Nos. 2009CB320602 and 2002CB312200
文摘In this paper,the problem of inverse quadratic optimal control over fnite time-horizon for discrete-time linear systems is considered.Our goal is to recover the corresponding quadratic objective function using noisy observations.First,the identifability of the model structure for the inverse optimal control problem is analyzed under relative degree assumption and we show the model structure is strictly globally identifable.Next,we study the inverse optimal control problem whose initial state distribution and the observation noise distribution are unknown,yet the exact observations on the initial states are available.We formulate the problem as a risk minimization problem and approximate the problem using empirical average.It is further shown that the solution to the approximated problem is statistically consistent under the assumption of relative degrees.We then study the case where the exact observations on the initial states are not available,yet the observation noises are known to be white Gaussian distributed and the distribution of the initial state is also Gaussian(with unknown mean and covariance).EM-algorithm is used to estimate the parameters in the objective function.The efectiveness of our results are demonstrated by numerical examples.
文摘In this paper, the inverse linear quadratic(LQ) problem over finite time-horizon is studied.Given the output observations of a dynamic process, the goal is to recover the corresponding LQ cost function. Firstly, by considering the inverse problem as an identification problem, its model structure is shown to be strictly globally identifiable under the assumption of system invertibility. Next, in the noiseless case a necessary and sufficient condition is proposed for the solvability of a positive semidefinite weighting matrix and its unique solution is obtained with two proposed algorithms under the condition of persistent excitation. Furthermore, a residual optimization problem is also formulated to solve a best-fit approximate cost function from sub-optimal observations. Finally, numerical simulations are used to demonstrate the effectiveness of the proposed methods.
文摘This paper presents LQR sliding surface-based Sliding Mode Controller(LQR-SMC)for balancing control of a Rotary Double Inverted Pendulum(RDIP)system.It is a challenging research topic in control engineering due to its nonlinearity and instability.The RDIP system uses only a motor to control two serially connected pendulums to stand at the upright position.The sliding surface is designed based on the LQR optimal gain.Nonsingular gain matrix is obtained by using the left inverse of the input matrix in the state space form of the system dynamics.The Lyapunov stability theory is used to determine the stability of the controller.To evaluate the performance of LQR-SMC,some performance indices,including the Integral Absolute Error(IAE),Integral Time Absolute Error(ITAE),and the Integrated Square Error(ISE),are used.System stability can be maintained by LQR-SMC under external disturbances as well as model and parameter uncertainties.
文摘In this paper, at first, the single input rule modules(SIRMs) dynamically connected fuzzy inference model is used to stabilize a double inverted pendulum system. Then, a multiobjective particle swarm optimization(MOPSO) is implemented to optimize the fuzzy controller parameters in order to decrease the distance error of the cart and summation of the angle errors of the pendulums, simultaneously. The feasibility and efficiency of the proposed Pareto front is assessed in comparison with results reported in literature and obtained from other algorithms.Finally, the Java programming with applets is utilized to simulate the stability of the nonlinear system and explain the internetbased control.
基金supported in part by the National Natural Science Foundation of China(61773373,U1501251,61533017)in part by the Young Elite Scientists Sponsorship Program by the China Association for Science and Technologyin part by the Youth Innovation Promotion Association of the Chinese Academy of Sciences
文摘Designing advanced design techniques for feedback stabilization and optimization of complex systems is important to the modern control field. In this paper, a near-optimal regulation method for general nonaffine dynamics is developed with the help of policy learning. For addressing the nonaffine nonlinearity, a pre-compensator is constructed, so that the augmented system can be formulated as affine-like form. Different cost functions are defined for original and transformed controlled plants and then their relationship is analyzed in detail. Additionally, an adaptive critic algorithm involving stability guarantee is employed to solve the augmented optimal control problem. At last, several case studies are conducted for verifying the stability, robustness, and optimality of a torsional pendulum plant with suitable cost.
基金supported by the China National Natural Science Foundation(52177082)China National Key R&D Program(2020YFC0827001)Science and Technology Project of Jilin Electric Power Co.,Ltd(2020JBGS-03).
文摘This study proposes a two-stage photovoltaic(PV)voltage control strategy for centralized control that ignores short-term load fluctuations.In the first stage,a deterministic power flow model optimizes the 15-minute active cycle of the inverter and reactive outputs to reduce network loss and light rejection.In the second stage,the local control stabilizes the fluctuations and tracks the system state of the first stage.The uncertain interval model establishes a chance constraint model for the inverter voltage-reactive power local control.Second-order cone optimization and sensitivity theories were employed to solve the models.The effectiveness of the model was confirmed using a modified IEEE 33 bus example.The intraday control outcome for distributed power generation considering the effects of fluctuation uncertainty,PV penetration rate,and inverter capacity is analyzed.
文摘Two novel improved variants of reptile search algorithm(RSA),RSA with opposition-based learning(ORSA)and hybrid ORSA with pattern search(ORSAPS),are proposed to determine the proportional,integral,and derivative(PID)controller parameters of an automatic voltage regulator(AVR)system using a novel objective function with augmented flexibility.In the proposed algorithms,the opposition-based learning technique improves the global search abilities of the original RSA algorithm,while the hybridization with the pattern search(PS)algorithm improves the local search abilities.Both algorithms are compared with the original RSA algorithm and have shown to be highly effective algorithms for tuning the PID controller parameters of an AVR system by getting superior results.Several analyses such as transient,stability,robustness,disturbance rejection,and trajectory tracking are conducted to test the performance of the proposed algorithms,which have validated the good promise of the proposed methods for controller designs.The performances of the proposed design approaches are also compared with the previously reported PID controller parameter tuning approaches to assess their success.It is shown that both proposed approaches obtain excellent and robust results among all compared ones.That is,with the adjustment of the weight factorα,which is introduced by the proposed objective function,for a system with high bandwitdh(α=1),the proposed ORSAPS-PID system has 2.08%more bandwidth than the proposed ORSA-PID system and 5.1%faster than the fastest algorithm from the literature.On the other hand,for a system where high phase and gain margins are desired(α=10),the proposed ORSA-PID system has 0.53%more phase margin and 2.18%more gain margin than the proposed ORSAPS-PID system and has 0.71%more phase margin and 2.25%more gain margin than the best performing algorithm from the literature.