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检索条件"主题词=Adaptive Dynamic Programming"

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adaptive Droop-Based Active Power Curtailment Method for Overvoltage Prevention in Low Voltage Distribution Network

Adaptive Droop-Based Active Power Curtailment Method for Ove...

引用

IEEE International Conference on Electro Information Technology (EIT)

作者： Maharjan, Manisha Tamrakar, Ujjwol Malla, Naresh dos Reis, Fernando B. Ni, Zhen Hansen, Timothy M. Tonkoski, Reinaldo South Dakota State Univ Dept Elect Engn & Comp Sci Brookings SD 57007 USA

ISBN: (纸本)9781509047673

Clean energy incentives and the continuous fall in the cost of photovoltaic (PV) installations have led to a steady growth in residential PV systems. One of the main consequences of this high PV penetration in low voltage (LV) distribution networks is the overvoltage issue. Active power curtailment of PV inverters has been previously used to curtail the output power of the inverters below its operating point to prevent such overvoltages. This technique, however, uses a constant droop-based approach to curtail the power, based on the difference between the measured voltage and a critical voltage level. In this paper, this technique is implemented in a typical LV distribution network in North America with high PV penetration level. The simulation results show that the system undergoes excessive curtailment resulting in unnecessary energy loss. An adaptive droop-based approach using adaptive dynamic programming (ADP) is proposed as a possible solution to minimize the total energy loss in the system while keeping the system voltage under the critical operating limits. The energy loss due to curtailment has decreased by 25% after implementing the adaptive-droop based approach using ADP.

关键词： Low voltage distribution network active power curtailment overvoltage prevention adaptive dynamic programming

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adaptive-Learning-Based H_∞ Optimal Tracking Control Design for a Class of Constrained Nonlinear Systems 37

Adaptive-Learning-Based <i>H</i><sub>∞</sub> Optimal Tracki...

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37th Chinese Control Conference (CCC)

作者： Hou, Jiaxu Wang, Ding Liu, Derong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9789881563958

This paper, considering constrained inputs, focuses on designing the optimal controller for discrete-time H-infinity tracking control problems. The neural-network-based iterative learning algorithm is deemed to be an excellent method to work. The iterative heuristic dynamic programming algorithm is employed for developing the formulated regulation of the tracking error. Strict convergence guarantees are given to support the optimality of the adaptive algorithm. A neural-network-based training scheme is adopted to implement the learning algorithm and stabilize the system in the optimal manner. Two numerical examples are provided to illustrate the applicability and good performance of the designed tracking control scheme.

关键词： adaptive dynamic programming H-infinity tracking control constraints convergence proof

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Optimal Tracking Control for Reconfigurable Manipulators Based on Critic-only Policy Iteration Algorithm 36

Optimal Tracking Control for Reconfigurable Manipulators Bas...

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第36届中国控制会议

作者： Hongbing Xia Bo Zhao Yuanchun Li Department of Control Science and Engineering Changchun University of Technology The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper tackles the optimal tracking control problem for reconfigurable manipulators based on critic-only policy iteration（Co PI） algorithm. By system transformation, the optimal tracking control problem is transformed into an optimal regulation problem. The optimal tracking controller is composed of the desired controller and the approximate optimal feedback one. The desired controller is developed to maintain the desired tracking performance at the steady-state, while the approximate optimal feedback controller is designed to stabilize the tracking error dynamics in an optimal manner. Then, a critic neural network is used to estimate the optimal performance index function, and the optimal feedback control is obtained by the Co PI algorithm. The convergence of the proposed method is analyzed and it is shown that the closed-loop system based on Co PI is uniformly ultimately bounded by using the Lyapunov approach. Finally, simulation studies are given to show the effectiveness of the developed method.

关键词： adaptive dynamic programming Reconfigurable manipulators Optimal tracking control Policy iteration Neural networks

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Incremental Model-Based Global Dual Heuristic programming for Flight Control 13th

Incremental Model-Based Global Dual Heuristic Programming fo...

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13th International-Federation-of-Automatic-Control (IFAC) Workshop on adaptive and Learning Control Systems (ALCOS)

作者： Sun, Bo van Kampen, Erik-Jan Delft Univ Technol Dept Control & Operat NL-2629 HS Delft Netherlands

This paper proposes a novel adaptive dynamic programming method, called Incremental model-based Global Dual Heuristic programming, to generate a self-learning adaptive controller, in the absence of sufficient prior knowledge of system dynamics. An incremental technique is employed for online model identification, instead of the artificial neural networks commonly used in conventional Global Dual Heuristic programming. The incremental model has the capability of tackling nonlinearity and uncertainty of the plant, but can also guarantee high precision of online identification without the requirement of offline training. On the basis of the identified model, two neural networks are adopted to facilitate the implementation of the self-learning controller, by approximating the cost-to-go and its derivatives and the control policy, respectively. Both methods are applied to a tracking control problem of a nonlinear aerospace system and the results show that the proposed method outperforms conventional Global Dual Heuristic programming in online learning speed, tracking precision and robustness to variation of initial system states and network weights. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： adaptive dynamic programming adaptive control incremental technique global dual heuristic programming artificial neural network

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Optimal trajectory tracking control based on reinforcement learning for the deployment process of space tether system 6

Optimal trajectory tracking control based on reinforcement l...

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6th Conference on Advances in Control and Optimization of dynamical Systems (ACODS)

作者： Feng, Yiting Wang, Changqing Li, Aijun Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

Space tether system has a wide application prospect in space mission. Due to the characteristics of strong non-linearity and under-actuation, as well as the interference of complex space environment, it is difficult to model the tethered system accurately. Hence, the controller based on the parameters of the system model will cause large errors in the process of control. In this paper, an adaptive dynamic programming algorithm based on reinforcement learning theory is adopted. By training two Back Propagation (BP) neural networks, namely critic neural network (NN) and actor NN, the perfounance index function and control law of the system approach approximate optimal values respectively. The controller design is independent of the system model, so model-free control of the system is realized by implementing this control method. First, assuming that the out-of-plane motion of the system is stable, the optimal deployment trajectory of the tethered system is obtained by parameter optimization based on Nelder-Mead method. The optimal trajectory is taken as the nominal trajectory and the trajectory tracking is carried out by reinforcement learning controller. The simulation results show that the reinforcement learning algorithm has a good control effect on the in-plane trajectory tracking of the tethered system, which proves the feasibility and robustness of the control method. (C) 2020, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： space tether system deployment trajectory tracking neural network adaptive dynamic programming reinforcement learning

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Improved adaptive Critic for Neural Optimal Control of Constrained Nonlinear Discrete-Time Systems 39

Improved Adaptive Critic for Neural Optimal Control of Const...

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39th Chinese Control Conference (CCC)

作者： Zhao, Mingming Wang, Ding Ha, Mingming Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9789881563903

There always exist approximation errors during neural network control processes, which may cause the estimation value to exceed the control constraint when the optimal control input reaches to a neighborhood of the constraint. In this paper, through a new neural network training approach, the near-optimal control problem for a class of nonlinear discrete-time systems with control constraints is solved. Based on the nonquadratic performance index and the dual heuristic dynamic programming scheme, the iterative algorithm is developed with convergence guarantee and is also implemented by using three neural networks. At last, two examples are given to demonstrate the effectiveness of the proposed optimal control scheme.

关键词： adaptive dynamic programming iterative adaptive critic control constraints neural networks nonlinear discrete-time systems

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Discrete-Time Generalized Policy Iteration ADP Algorithm With Approximation Errors

Discrete-Time Generalized Policy Iteration ADP Algorithm Wit...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Wei, Qinglai Li, Benkai Song, Ruizhuo Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781538627266

This paper concerns with a novel generalized policy iteration (GPI) algorithm with approximation errors. Approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The convergence of the developed algorithm is established to show that the iterative value function is convergent to a finite neighborhood of the optimal performance index function. Finally, numerical examples and comparisons are presented.

关键词： adaptive critic designs adaptive dynamic programming approximate dynamic programming neuro-dynamic programming generalized policy iteration nonlinear systems optimal control neural networks reinforcement learning

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Action-Dependent Updated Terminal Cost Receding Horizon Control for Discrete-Time Linear Systems

Action-Dependent Updated Terminal Cost Receding Horizon Cont...

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12th International Conference on Control, Automation and Systems (ICCAS)

作者： Moon, Mi Youn Lee, Jae Young Park, Jin Bae Choi, Yoon Ho Yonsei Univ Dept Elect & Elect Engn Seoul 120749 South Korea Kyonggi Univ Dept Elect Engn Kyonggi 443760 South Korea

ISBN: (纸本)9788993215045

In case of linear quadratic regulator (LQR), the updated terminal cost receding horizon control (UTC-RHC) enhances the standard RHC in terms of the stability and the convergence to the optimal solution. Based on the action-dependent (AD) value functions, known as Q-function, this paper proposes two modified RHC methods, AD-RHC and AD-UTC-RHC and proves the stability and convergence by using the matrix equivalence of the established control schemes. Additionally, in case of the horizon size N = 1, as like UTC-RHC is connected with heuristic dynamic programming (HDP), we shows the connection between the AD-UTC-RHC and ADHDP which has the learning abilities of the optimal solution without using the explicit descriptions of the system dynamics.

关键词： Action-dependent updated terminal cost receding horizon control receding horizon control adaptive dynamic programming action-dependent heuristic dynamic programming Q-function

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An Event-Triggered Control Approach with Single-Critic Architecture for a Class of Nonlinear Systems

An Event-Triggered Control Approach with Single-Critic Archi...

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Chinese Automation Congress (CAC)

作者： Zhang, Kun Zhang, Huaguang Wang, Zhiliang Luo, Yanhong Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9781728176871

Efficient control scheme in many large-scale or long-term industrial production systems is always the core research issue for engineers and scholars. Optimal control design, which can stabilize the system dynamic, and optimize the performance index simultaneously. provides an architecture to control the systems with the minimized energy consumption. This paper proposes a novel intelligent control scheme with an event-triggered single-critic architecture, which converts the optimal nonlinear control problem into solving the well-known Hamilton-Jacobi-Bellman (HJB) equation in systems. Compared with existing methods, the event-triggered mechanism is utilized to overcome the crucial and strict requirements of the system states in real time, and the single-critic architecture simplifies the conventional structure by eliminating the actor neural network. Both the stability and convergence of the proposed algorithm have been completely proved, where the cases that event is not triggered or is triggered are considered. Finally, the effectiveness of the novel event based single-critic control approach has been demonstrated by the simulation results.

关键词： event-triggered control optimal control Hamilton-Jacobi-Bellman equation adaptive dynamic programming

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Zero-sum Neuro-optimal Control of Modular and Reconfigurable Robots with External Collisions via Critic Only Policy Iteration

Zero-sum Neuro-optimal Control of Modular and Reconfigurable...

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Chinese Automation Congress (CAC)

作者： Chen, Jingchen An, Tianjiao Wang, Yuexi Zhang, Zhenguo Dong, Bo Changchun Univ Technol Dept Control Sci & Engn Changchun Peoples R China

ISBN: (纸本)9781728176871

In the cause of deal with this problem of optimal position and velocity tracking regarding modular reconfigurable robots (MRRs) with external collisions, a zero-sum neural-optimal control method ground on critical only policy iteration (COPI) scheme is proposed by adaptive dynamic programming (ADP). The MRR dynamics model uncertainty is compensated by adaptive fuzzy control method. Unique the critic neural network (NN) ground on policy iteration (PI) and ADP method is used as work out hamilton-Jacobi -Issacs (HJI) equation, then the approximated optimal control is obtained. Then Lyapunov's theory proved that the MRR system was stable. At length, simulation results make clear the virtue of the proposed method.

关键词： neural network external collisions optimal control adaptive dynamic programming modular and reconfigurable robots

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