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Event-triggered adaptive dynamic programming for multi-player zero-sum games with unknown dynamics

SOFT COMPUTING 2021年第3期25卷 2237-2251页

作者： Zhang, Yongwei Zhao, Bo Liu, Derong Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China

In this paper, a novel event-triggered optimal control approach is developed to solve zero-sum game problems for continuous-time multi-player nonlinear systems with unknown dynamics. To begin with, a model neural network (NN) is employed to reconstruct the unknown multi-player nonlinear system by measured input and output data. Then, a critic NN is used to solve the event-triggered Hamilton-Jacobi-Isaacs (HJI) equation for multi-player zero-sum game. Meanwhile, the optimal control law and the worst disturbance law are approximated with the help of critic NN only, respectively. Compared with time-triggered method, the developed control law and the disturbance law are updated only when the triggering condition is violated;thus, the computational and communication burden are reduced. The Lyapunov stability analysis shows that the closed-loop system can be guaranteed to be stable. Finally, two simulation examples are provided to validate the effectiveness of the proposed method.

关键词： adaptive dynamic programming Event-triggered control Neural network Multi-player zero-sum game

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Event-triggered decentralized optimal fault tolerant control for mismatched interconnected nonlinear systems through adaptive dynamic programming

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OPTIMAL CONTROL APPLICATIONS & METHODS 2021年第5期42卷 1365-1384页

作者： Luo, Fangchao Zhao, Bo Liu, Derong Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Beijing Normal Univ Sch Syst Sci 19 Xinjiekouwai St Beijing 100875 Peoples R China

In this paper, we propose an event-triggered decentralized optimal fault tolerant control (ETDOFTC) scheme based on adaptive dynamic programming for mismatched interconnected nonlinear systems with actuator failures. For fault-free dynamic models, the decentralized control problem is addressed by developing a set of decentralized optimal control strategies for isolated subsystems with modified value functions, which are approximated by critic neural networks. Meanwhile, the neural network-based decentralized observer is established to approximate actuator failures and mismatched unknown interconnections. The weights of the neural networks are aperiodically updated at the designed triggering instants. Then, the proposed ETDOFTC scheme is obtained by combining the event-triggered decentralized optimal control strategies with the adaptive fault compensators. Furthermore, it is proved that all the signals of the closed-loop system are uniformly ultimately bounded via the Lyapunov stability analysis. Finally, simulation results of two examples are presented to confirm the effectiveness of the proposed ETDOFTC scheme.

关键词： adaptive dynamic programming decentralized control event‐ triggered control fault tolerant control mismatched interconnected nonlinear systems neural networks reinforcement learning

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Single-network based adaptive dynamic programming optimal control for robotic manipulator with joint limitation under input saturation

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2024年第16期55卷 3355-3370页

作者： Li, Bin Zhan, Hong Zhang, Xiuyu Yang, Chenguang South China Univ Technol Sch Automat Sci & Engn Guangzhou Peoples R China Northeast Elect Power Univ Sch Automat Engn Jilin Peoples R China Univ Liverpool Dept Comp Sci Liverpool L69 3BX England

This paper proposes an optimal control method based on adaptive dynamic programming (ADP) for manipulators with joint limitation under input saturation. The asymmetric input saturation problem is solved by introducing a smooth function, by which the saturation and nonsmooth issues of the input caused by the actuators can be handled. Joint limitation of the manipulator is transformed through a transfer function, which is incorporated into the cost function of the whole robotic system, to guarantee the joint limitation will not be violated. Meanwhile, due to the difficulty in solving Hamilton-Jacobi-Bellman (HJB) equation, the critic network is constructed utilising radial basis function neural networks (RBFNNs) to approximate the cost function. This approach effectively overcomes the curse of dimensionality problem associated with dynamic programming. Subsequently, an ADP-based optimal controller is designed for the robotic system. Lyapunov synthesis is used in the stability analysis to prove all the signals are convergent and ultimately uniformly bounded (UUB). Simulations are conducted on a planar 3-DOF manipulator to validate the effectiveness of the proposed method. The results demonstrate the improved performance and feasibility of the developed ADP-based optimal control strategy in handling joint limitations and input saturation challenges in robotic systems.

关键词： adaptive dynamic programming manipulator constrained control joint limitation

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Event-Triggered Model Predictive adaptive dynamic programming for Road Intersection Path Planning of Unmanned Ground Vehicle

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第11期70卷 11228-11243页

作者： Hu, Chaofang Zhao, Lingxue Qu, Ge Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

Autonomous driving of unmanned ground vehicle (UGV) at road intersection is a challenging task due to the complicated traffic conditions. In this paper, an event-triggered model predictive adaptive dynamic programming (MPADP) algorithm is proposed for path planning of UGV at road intersection. Following the critic-actor scheme of adaptive dynamic programming (ADP), cost function approximation and control policy generation are combined to formulate MPADP. The infinite horizon cost function of ADP is stacked over predictive horizon of model predictive control (MPC), and then the infinite horizon cost function is converted to the finite horizon-stacked cost function in MPADP. By minimizing the approximation error within predictive horizon, the approximation accuracy is enhanced. Considering the limitation of energy consumption, the event-triggered mechanism is designed based on the mismatch of cost function approximation. Three triggering conditions are designed, and the corresponding boundedness of approximation error is proved. Simulation results illustrate the effectiveness, efficiency and feasibility in application of the event-triggered MPADP method for path planning at road intersection.

关键词： Roads Path planning Cost function dynamic programming Safety Predictive models Kinematics Unmanned ground vehicle path planning event-triggered adaptive dynamic programming model predictive control

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adaptive Optimal Control of CVCF Inverters With Uncertain Load: An adaptive dynamic programming Approach

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IEEE ACCESS 2021年 9卷 89276-89286页

作者： Wang, Zhongyang Yu, Yunjun Fuzhou Inst Technol Sch Appl Sci & Engn Fuzhou 350506 Peoples R China Nanchang Univ Sch Informat Engn Nanchang 330031 Jiangxi Peoples R China Nanchang Univ AI Inst Nanchang 330031 Jiangxi Peoples R China

This paper proposed a data-driven adaptive optimal control approach for CVCF (constant voltage, constant frequency) inverter based on reinforcement learning and adaptive dynamic programming (ADP). Different from existing literature, the load is treated as a dynamic uncertainty and a robust optimal state-feedback controller is proposed. The stability of the inverter-load system has been strictly analyzed. In order to obtain accurate output current differential signal, this paper designs a tracking differentiator. It is ensured that the tracking error asymptotically converges to zero through the proposed output-feedback controllers. A standard proportional integral controller and linear active disturbance rejection control strategy are also designed for the purpose of comparison. The simulation results show that the proposed controller has inherent robustness and does not require retuning with different applications.

关键词： Inverters Voltage control Mathematical model Reinforcement learning Optimal control Load modeling Licenses adaptive optimal control CVCF inverter reinforcement learning adaptive dynamic programming

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Flexible Switching Control of Aircraft Skin Inspection Robot via adaptive dynamic programming

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IEEE ACCESS 2024年 12卷 158407-158421页

作者： Wu, Xuewei Wang, Congqing Wang, Wanjun Zong, Siheng Nanjing Vocat Coll Informat Technol Sch Intelligent Mfg Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Jiangsu Peoples R China

This article considers the flexible switching control problem of a two-frame aircraft skin inspection robot (TFASIR) with full-state time-varying constraints, input saturation, uncertainty, and unknown disturbance. Initially, this control problem is also treated as a tracking control problem of the dual-coupled adsorption system (DCAS). A novel nonlinear time-varying state-dependent function (NTVSDF) is first designed to tackle the full-state constraint problem. Subsequently, a feedforward tracking control method is designed, that uses the command-filtered backstepping technique, to transform the tracking control problem into an equivalent differential game problem (DGP) of closed-loop systems. Then, a zero-sum game strategy is presented, that uses the idea of adaptive dynamic programming (ADP) algorithm, to determine the DGP. The whole control method ensures that the closed-loop signals are uniformly ultimately bounded (UUB). Furthermore, another problem is that the partial system states are not accessible. To overcome this problem, a high-gain observer is utilized to reconstruct the state vector, and an output feedback controller is developed. The feasibility of the proposed control scheme is demonstrated in simulation.

关键词： Adsorption Switches Control systems Robots Force Aircraft Skin Inspection Uncertainty Multi-agent systems Robotics adaptive control full-state time-varying constraint adaptive dynamic programming differential game

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Hamiltonian-driven adaptive dynamic programming for mixed H₂/H_∞ performance using sum-of-squares

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2021年第6期31卷 1941-1963页

作者： Yang, Yongliang Mazouchi, Majid Modares, Hamidreza Univ Sci & Technol Beijing Sch Automat & Elect Engn Key Lab Knowledge Automat Ind Proc Minist Educ Beijing Peoples R China Michigan State Univ Dept Mech Engn E Lansing MI 48824 USA

In this article, the mixed H-2/H-infinity performance optimization is first formulated as a nonzero-sum game, of which the sufficient condition guaranteeing the existence of the Nash equilibrium is derived using the Hamilton-Jacobi (HJ) theory. Then, Hamiltonian-driven inequalities are presented to evaluate the H-2 and H-infinity performances. Using this Hamiltonian-inequality driven approach, the coupled HJ equations arising from finding the Nash equilibrium are relaxed to the HJ inequality constraints. A novel mixed policy iteration (PI) algorithm is developed that uses sum-of-squares (SOS) program in policy evaluation step, and consists of an H-2 performance improvement step and an H-infinity performance guarantee step. This constrained-driven approach allows us to present a PI algorithm that takes into account both robustness and performance objectives. Finally, a numerical simulation is carried out to highlight the efficacy of the proposed framework.

关键词： adaptive dynamic programming Hamiltonian-driven framework mix H-2/H-infinity performance nonzero-sum game sum-of-squares programming

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Event-triggered guaranteed cost fault-tolerant optimal tracking control for uncertain nonlinear system via adaptive dynamic programming

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2021年第7期31卷 2572-2592页

作者： Guo, Zijie Yao, Deyin Bai, Weiwei Li, Hongyi Lu, Renquan Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Guangdong Univ Technol Guangdong Prov Key Lab Intelligent Decis & Cooper Guangzhou 510006 Peoples R China

In this article, an event-triggered guaranteed cost optimal tracking control problem is investigated for a class of uncertain nonlinear system with partial loss of actuator effectiveness faults. To begin with, an augmented system consisted of error system and reference system is constructed to simplify the tracking controller design. Then, in order to consider both actuator faults and system uncertainties in optimal tracking control, an improved discounted cost function is developed. Furthermore, a single critic neural network adaptive dynamic programming algorithm is utilized to implement the event-based approximate optimal controller design. Different from static event-triggered mechanism, the dynamic event-triggered mechanism proposed in this article with an internal dynamic signal can further improve execution efficiency. A Lyapunov-based stability analysis is given to demonstrate the uniformly ultimately bounded stability of the closed-loop system. Finally, two typical nonlinear simulations are presented to verify the developed control method.

关键词： adaptive dynamic programming dynamic event-triggered mechanism fault-tolerant control guaranteed cost tracking control system uncertainties

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Attitude-Tracking Control for Over-Actuated Tailless UAVs at Cruise Using adaptive dynamic programming

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DRONES 2023年第5期7卷 294-294页

作者： He, Zihou Hu, Jianbo Wang, Yingyang Cong, Jiping Bian, Yuan Han, Linxiao Airforce Enginnering Univ Equipment Management & Unmanned Aerial Vehicle Eng Xian 710072 Peoples R China

Using adaptive dynamic programming (ADP), this paper presents a novel attitude-tracking scheme for over-actuated tailless unmanned aerial vehicles (UAVs) that integrates control and control allocation while accounting for nonlinearity and nonaffine control inputs. The proposed method uses the idea of nonlinear dynamic inversion to create an augmented system and converts the optimal tracking problem into an optimal regulation problem using a discounted performance function. Drawing inspiration from incremental control, this method achieves optimal tracking control for the nonaffine system by simply using a critic-only structure. Moreover, the unique design of the performance function ensures robustness against model uncertainties and external disturbances. The ADP method was found to outperform traditional control architectures that separate control and control allocation, achieving the same level of attitude-tracking performance through a more optimized approach. Furthermore, unlike many recent optimal controllers for nonaffine systems, our method does not require any model identifiers and demonstrates robustness. The superiority of the ADP-based approach is verified through two simulated scenarios, and its internal mechanism is further discussed. The theoretical analysis of robustness and stability is also provided.

关键词： attitude tracking tailless unmanned aerial vehicle adaptive dynamic programming UAV flight simulation nonlinear flight control

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H∞ optimal control of unknown continuous time linear periodic systems by adaptive dynamic programming with applications to magnetic attitude control

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OPTIMAL CONTROL APPLICATIONS & METHODS 2023年第3期44卷 1341-1355页

作者： Jiang, Huaiyuan Zhou, Bin Harbin Inst Technol Ctr Control Theory & Guidance Technol Harbin 150001 Peoples R China

This article studies the data-driven H-infinity controller design of continuous-time linear periodic systems. By treating the H-infinity, optimal control problem as a two-player zero-sum game problem, the adaptive dynamic programming technique is utilized. Based on the Newton's method, a one-loop model-based policy-iteration (PI) method is introduced. Since the initial matrix for starting the proposed model-based PI method is not easy to choose, a data-driven value iteration method without requiring the knowledge of the initial matrix is then proposed. The convergence of the proposed methods is analyzed and the numerical simulation verifies the effectiveness of the proposed scheme.

关键词： adaptive dynamic programming continuous-time systems data-driven control H-infinity control periodic systems

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