In this paper, we investigate a model-free identifier-critic-based optimal adaptive controller for multiplayer games with the input disturbances. Specifically, we first adopt the identifier neural network to identify ...
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In this paper, we investigate a model-free identifier-critic-based optimal adaptive controller for multiplayer games with the input disturbances. Specifically, we first adopt the identifier neural network to identify the system dynamics. Simultaneously, we use the critic neural network to estimate the optimal cost function thereby obtaining the estimated optimal controller. Further taking the input disturbances into consideration, we add a feedback gain into the estimated optimal controller so as to obtain the controller. The learning of the identifier and critic network is online and simultaneous. Then, we analyze the stability of the proposed approach. Eventually, the simulation results illustrate the validity of the proposed controller.
This article investigates the guaranteed cost robust tracking control problem of nonlinear systems subjected to input constraint and unmatched uncertainty. The event -based adaptive dynamic programming (ADP) approach ...
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This article investigates the guaranteed cost robust tracking control problem of nonlinear systems subjected to input constraint and unmatched uncertainty. The event -based adaptive dynamic programming (ADP) approach is utilized to address this problem. First, the tracking error and reference trajectory are combined to form an augmented uncertain system. Then, by decomposing the uncertainty into the matched and unmatched parts, the original tracking problem is converted into the optimal regulation problem of an auxiliary system. The cost function for the auxiliary system is defined, and the associated Hamilton-Jacobi-Bellman (HJB) equation is solved using a single critic neural network (NN). Moreover, a novel event -triggering rule is formulated, and it is shown that the designed event -based controller guarantees that the tracking error is uniformly ultimately bounded. The derivation of event -based guaranteed cost and its relation with the time -based counterpart is presented. The exclusion of the infamous Zeno behavior is guaranteed. The uniform ultimate boundedness of the critic weight estimation error is shown. Finally, the effectiveness of the proposed event -triggered ADP method is illustrated through simulations of the spring-mass-damper system and Van der Pol's oscillator with unmatched uncertainty.
This work investigates the adaptive output consensus problem for uncertain discrete-time linear multi-agent systems on directed graphs when both Laplacian matrices for communication graphs and agent system matrices ar...
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This work investigates the adaptive output consensus problem for uncertain discrete-time linear multi-agent systems on directed graphs when both Laplacian matrices for communication graphs and agent system matrices are not available. Firstly, a fully distributed algorithm is proposed to estimate the Laplacian matrix for each agent. Then, based on the proposed estimation algorithm, two fully distributed adaptive control algorithms, one for state feedback and the other for output feedback, are developed to acquire the desired controller parameters by utilizing the so-called adaptive dynamic programming techniques. It is shown that the output consensus is achieved for the resulting closedloop multi-agent system. Simulation results demonstrate the efficacy of the proposed fully distributed adaptive controllers resulting from those adaptive control algorithms. (c) 2024 Elsevier Ltd. All rights reserved.
This work presents an output-feedback policy learning algorithm underlining input-output system data for distributed robust optimal synchronization of heterogeneous multi-agent systems. The output -feedback synchroniz...
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This work presents an output-feedback policy learning algorithm underlining input-output system data for distributed robust optimal synchronization of heterogeneous multi-agent systems. The output -feedback synchronization problem in the context of this work is formulated via robust output regulation and reinforcement learning modeling the interactions among agents by a zero-sum game. The proposed learning and control structure only requires the local system data for each agent and distributed output data among communicating neighbors. We utilize system-level synchysis for the continuous-time state reconstruction for the distributed learning with convergence and stability proofs under the proposed output-feedback policy for solving the zero-sum game. We further show that policy learning is assured under the proposed data criteria relating to input-output data only rather than any inter-immediate gains from policy iterations. Based on the cooperative robust output regulation, this work gains robustness after the learning is complete and establishes an output data-driven distributed optimal robust synchronization without knowing accurate system dynamics. A numerical example shows the effectiveness of the proposed learning algorithm. (c) 2023 Elsevier Ltd. All rights reserved.
This article investigates the optimal containment control problem for a class of heterogeneous multi-agent systems with time-varying actuator faults and unmatched disturbances based on adaptive dynamic programming. Si...
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This article investigates the optimal containment control problem for a class of heterogeneous multi-agent systems with time-varying actuator faults and unmatched disturbances based on adaptive dynamic programming. Since there exist unknown input signals in each leader, distributed observers are utilized to estimate trajectories in the convex hull spanned by leaders. The containment control problem is then transformed into an optimal tracking problem. To compensate for the actuator faults and unmatched disturbances, a novel performance index function is designed. We prove that the optimal control policy can ensure that the tracking error system of each follower is uniformly ultimately bounded. The online policy iteration algorithm is implemented using critic neural networks to obtain the optimal control policy. A numerical example is provided to demonstrate the effectiveness of the proposed control policy.
This paper focuses on the problem of H ,,, optimal tracking control for a class of nonlinear state constrained systems with input delay and disturbances. With the aid of Pade approximation, an auxiliary variable is de...
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This paper focuses on the problem of H ,,, optimal tracking control for a class of nonlinear state constrained systems with input delay and disturbances. With the aid of Pade approximation, an auxiliary variable is devised to eliminate the effects of input delay. Combining barrier Lyapunov functions (BLFs) with backstepping design technique, a feedforward adaptive controller is designed to transform the tracking control problem of nonlinear state constrained system into an equivalent H ,,, control problem of input-affine error system without state constraints, wherein neural networks (NNs) are employed to approximate unknown system dynamics. Then based on single -network adaptive dynamic programming (ADP), an H ,,, optimal feedback controller is developed by utilizing a single critic network to learn the Nash equilibrium related to Hamilton-Jacobi-Isaacs (HJI) equation. Therefore, the whole tracking controller can be constructed by integrating the feedforward adaptive controller with the optimal feedback controller. Moreover, it is proven by Lyapunov's theory that all signals within the closed -loop system are uniformly ultimately bounded (UUB), and the tracking error converges to a small neighborhood of the origin without violating any state constraints. Two simulation examples are also presented to validate the effectiveness of the proposed approach.
In this article, we propose a dynamic event-triggered neuro-optimal control scheme (DETNOC) for uncertain nonlinear systems subject to unknown dead-zone and disturbances through the design of a composite control law. ...
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In this article, we propose a dynamic event-triggered neuro-optimal control scheme (DETNOC) for uncertain nonlinear systems subject to unknown dead-zone and disturbances through the design of a composite control law. An integral sliding mode-based discontinuous control law is utilized to compensate for the effects of unknown dead-zone, disturbance, and a component of uncertainties. As a result, a system dynamics that evolves free of these effects during the sliding mode is obtained. Then, an adaptive dynamic programming-based dynamic event-triggered optimal control law is designed to stabilize the sliding mode dynamics with the help of critic- only neural network architecture. Finally, stability analysis of the closed-loop system is provided and the effectiveness of the developed DETNOC scheme is verified.
In this study, the multi-objective optimization and decision-making for optimal positions of actuators and consensus adaptive dynamic programming (CADP) are investigated to mitigate the vibration of large flexible spa...
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In this study, the multi-objective optimization and decision-making for optimal positions of actuators and consensus adaptive dynamic programming (CADP) are investigated to mitigate the vibration of large flexible space structures (LFSS). The optimization of the actuator positions maintains a balance between maximizing actuation efficiency and maximizing input voltage decoupling. Meanwhile, the CADP control method accelerates the attenuation of vibration when agents collaborate in the designed communication topology network. First, the electromechanical coupled dynamic model of the LFSS is built by the finite element method. Subsequently, the multi-objective optimization criteria are proposed, which maximize the actuation efficiency and decoupling of control inputs. Moreover, the multi-objective optimization and decision-making, which is based on the non-dominated sorting differential evolutionary algorithm (NSDE) and technique for order preference by similarity to ideal solution (TOPSIS), respectively, are performed to rapidly find the optimal position of actuators. In addition, the CADP control algorithm is designed and its stability is proven. Finally, for harmonic excitation under multi-frequency superposition, simulation comparisons based on the CADP and adaptive dynamic programming (ADP) are performed. Simulation results verify the effectiveness of the proposed optimization criterion of actuators and the CADP algorithm for vibration mitigation of LFSS.(c) 2023 Elsevier Masson SAS. All rights reserved.
Compared with traditional robot, modular reconfigurable robot (MRR) has the advantages of strong environmental adaptability and flexible task completion. According to the optimal tracking control problem (OTCP) of MRR...
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Compared with traditional robot, modular reconfigurable robot (MRR) has the advantages of strong environmental adaptability and flexible task completion. According to the optimal tracking control problem (OTCP) of MRR under some restricted conditions, this paper puts forward a constrained dynamic event-triggered control (DETC) for MRR system with disturbance through adaptive dynamic programming (ADP), which can minimize the information interaction quantity under the premise of system stability and expected control effect. In view of the uncertainty of model coupling part, the identification network is used to estimate the dynamics of MRR and the estimation error is proved to be uniformly ultimate bounded (UUB). The other three groups of critic, action and disturbance neural networks (NNs) are established by the approximation principle of ADP. The optimal control pair is obtained through policy iteration (PI) with DETC, and the triggering condition is designed based on the asymptotic stability of MRR system. At last, the strengths of the algorithm in this paper are validated through simulation experiments. (C) 2022 Elsevier B.V. All rights reserved.
The infinite-horizon zero-sum game of a linear system can be resorted to solve a Game algebraic Riccati equation (GARE) with indefinite quadratic term. Double-loop policy iteration algorithm is often used to find the ...
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The infinite-horizon zero-sum game of a linear system can be resorted to solve a Game algebraic Riccati equation (GARE) with indefinite quadratic term. Double-loop policy iteration algorithm is often used to find the solution of such GARE, but its calculation is usually time-consuming. In this work, we propose a novel model-based single-loop policy iteration algorithm to solve GARE and the convergence of the algorithm is guaranteed by the boundness of the iterative sequence and the comparison result. Furthermore, we devise a data-driven single-loop policy iteration algorithm for solving linear zero-sum games, without requiring the knowledge of system dynamics. Compared to the existing Newton's single-loop methods, the initialization of our algorithms is significantly relaxed and easier to implement. Two numerical examples are included to illustrate the proposed algorithms. (c) 2024 Published by Elsevier Ltd.
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