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学者姓名：王鼎
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摘要 ：
In this work, the generalized value iteration with a discount factor is developed for optimal control of discrete-time nonlinear systems, which is initialized with a positive definite value function rather than zero. The convergence analysis of the discounted value function sequence is provided. The condition for the discount factor is given to guarantee the stability of the controlled plants. With this operation, the iterative control policy that asymptotically stabilizes the closed-loop system can be determined. The introduction of a discount factor has eased some conditions that the system dynamics and the initialization of the generalized value iteration need to fulfill. It is not required that the initial control policy is stabilizing. In the iteration process, under some conditions, if the system is asymptotically stable at the current iteration, then it can be guaranteed that the iterative control policies after this current iteration step also are stabilizing. It is convenient and practical to evaluate the asymptotic stability of the closed-loop system using the iterative control policy. A numerical example with physical background is carried out to validate the present results. © 2020 Elsevier B.V.
关键词 ：
Asymptotic stability Asymptotic stability Closed loop systems Closed loop systems Discrete time control systems Discrete time control systems
引用：
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| GB/T 7714 | Ha, Mingming , Wang, Ding , Liu, Derong . Generalized value iteration for discounted optimal control with stability analysis [J]. | Systems and Control Letters , 2021 , 147 . |
| MLA | Ha, Mingming 等. "Generalized value iteration for discounted optimal control with stability analysis" . | Systems and Control Letters 147 (2021) . |
| APA | Ha, Mingming , Wang, Ding , Liu, Derong . Generalized value iteration for discounted optimal control with stability analysis . | Systems and Control Letters , 2021 , 147 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
A data-based value iteration algorithm with the bidirectional approximation feature is developed for discounted optimal control. The unknown nonlinear system dynamics is first identified by establishing a model neural network. To improve the identification precision, biases are introduced to the model network. The model network with biases is trained by the gradient descent algorithm, where the weights and biases across all layers are updated. The uniform ultimate boundedness stability with a proper learning rate is analyzed, by using the Lyapunov approach. Moreover, an integrated value iteration with the discounted cost is developed to fully guarantee the approximation accuracy of the optimal value function. Then, the effectiveness of the proposed algorithm is demonstrated by carrying out two simulation examples with physical backgrounds. (C) 2021 Elsevier Ltd. All rights reserved.
关键词 ：
Adaptive dynamic programming Adaptive dynamic programming Data-based discounted optimal control Data-based discounted optimal control Lyapunov method Lyapunov method Uniformly ultimately bounded stability Uniformly ultimately bounded stability Value iteration Value iteration
引用：
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| GB/T 7714 | Ha, Mingming , Wang, Ding , Liu, Derong . Neural-network-based discounted optimal control via an integrated value iteration with accuracy guarantee [J]. | NEURAL NETWORKS , 2021 , 144 : 176-186 . |
| MLA | Ha, Mingming 等. "Neural-network-based discounted optimal control via an integrated value iteration with accuracy guarantee" . | NEURAL NETWORKS 144 (2021) : 176-186 . |
| APA | Ha, Mingming , Wang, Ding , Liu, Derong . Neural-network-based discounted optimal control via an integrated value iteration with accuracy guarantee . | NEURAL NETWORKS , 2021 , 144 , 176-186 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The wastewater treatment is an important avenue of resources cyclic utilization when coping with the modern urban diseases. However, there always exist obvious nonlinearities and uncertainties within wastewater treatment systems, such that it is difficult to accomplish proper optimization objectives toward these complex unknown platforms. In this article, a data-driven iterative adaptive critic (IAC) strategy is developed to address the nonlinear optimal control problem. The iterative algorithm is constructed with a general framework, followed by convergence analysis and neural network implementation. Remarkably, the derived IAC control policy with an additional steady control input is also applied to a typical wastewater treatment plant, rendering that the dissolved oxygen concentration and the nitrate level are maintained at desired setting points. When compared with the incremental proportional-integral-derivative method, it is found that faster response and less oscillation can be obtained during the IAC control process.
关键词 ：
Adaptive systems Adaptive systems Cost function Cost function Data-driven control Data-driven control iterative adaptive critic (IAC) iterative adaptive critic (IAC) Iterative methods Iterative methods learning systems learning systems Optimal control Optimal control optimal regulation optimal regulation Recycling Recycling Wastewater Wastewater wastewater treatment wastewater treatment Wastewater treatment Wastewater treatment
引用：
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| GB/T 7714 | Wang, Ding , Ha, Mingming , Qiao, Junfei . Data-Driven Iterative Adaptive Critic Control Toward an Urban Wastewater Treatment Plant [J]. | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2021 , 68 (8) : 7362-7369 . |
| MLA | Wang, Ding 等. "Data-Driven Iterative Adaptive Critic Control Toward an Urban Wastewater Treatment Plant" . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 68 . 8 (2021) : 7362-7369 . |
| APA | Wang, Ding , Ha, Mingming , Qiao, Junfei . Data-Driven Iterative Adaptive Critic Control Toward an Urban Wastewater Treatment Plant . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2021 , 68 (8) , 7362-7369 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this paper, we aim to solve the optimal tracking control problem for a class of nonaffine discrete-time systems with actuator saturation. First, a data-based neural identifier is constructed to learn the unknown system dynamics. Then, according to the expression of the trained neural identifier, we can obtain the steady control corresponding to the reference trajectory. Next, by involving the iterative dual heuristic dynamic programming algorithm, the new costate function and the tracking control law are developed. Two other neural networks are used to estimate the costate function and approximate the tracking control law. Considering approximation errors of neural networks, the stability analysis of the proposed algorithm for the specific systems is provided by introducing the Lyapunov approach. Finally, via conducting simulation and comparison, the superiority of the developed optimal tracking method is confirmed. Moreover, the trajectory tracking performance of the wastewater treatment application is also involved for further verifying the proposed approach. (C) 2021 Elsevier Ltd. All rights reserved.
关键词 ：
Actuator saturation Actuator saturation Adaptive critic Adaptive critic Neural networks Neural networks Optimal tracking control Optimal tracking control Wastewater treatment Wastewater treatment
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| GB/T 7714 | Wang, Ding , Zhao, Mingming , Ha, Mingming et al. Neural optimal tracking control of constrained nonaffine systems with a wastewater treatment application [J]. | NEURAL NETWORKS , 2021 , 143 : 121-132 . |
| MLA | Wang, Ding et al. "Neural optimal tracking control of constrained nonaffine systems with a wastewater treatment application" . | NEURAL NETWORKS 143 (2021) : 121-132 . |
| APA | Wang, Ding , Zhao, Mingming , Ha, Mingming , Ren, Jin . Neural optimal tracking control of constrained nonaffine systems with a wastewater treatment application . | NEURAL NETWORKS , 2021 , 143 , 121-132 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this paper, a hybrid intelligent tracking control approach is developed to address optimal tracking problems for a class of nonlinear discrete-time systems. The generalized value iteration algorithm is utilized to attain the admissible tracking control with off-line training, while the on-line near-optimal control method is established to enhance the control performance. It is emphasized that the value iteration performance is improved by introducing the acceleration factor. By collecting the input-output data of the unknown system plant, the model neural network is constructed to provide the partial derivative of the system state with respect to the control law as the approximate control matrix. A novel computational strategy is introduced to obtain the steady control of the reference trajectory. The critic and action neural networks are utilized to approximate the cost function and the tracking control, respectively. Considering approximation errors of neural networks, the stability analysis of the specific systems is provided via the Lyapunov approach. Finally, two numerical examples with industrial application backgrounds are involved for verifying the effectiveness of the proposed approach.
关键词 ：
Accelerated generalized value iteration Accelerated generalized value iteration Adaptive critic Adaptive critic Industrial applications Industrial applications Intelligent optimal tracking control Intelligent optimal tracking control Neural networks Neural networks
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| GB/T 7714 | Wang, Ding , Zhao, Mingming , Ha, Mingming et al. Adaptive-critic-based hybrid intelligent optimal tracking for a class of nonlinear discrete-time systems [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2021 , 105 . |
| MLA | Wang, Ding et al. "Adaptive-critic-based hybrid intelligent optimal tracking for a class of nonlinear discrete-time systems" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 105 (2021) . |
| APA | Wang, Ding , Zhao, Mingming , Ha, Mingming , Hu, Lingzhi . Adaptive-critic-based hybrid intelligent optimal tracking for a class of nonlinear discrete-time systems . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2021 , 105 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this article, a generalized value iteration algorithm is developed to address the discounted near-optimal control problem for discrete-time systems with control constraints. The initial cost function is permitted to be an arbitrary positive semi-definite function without being zero. First, a nonquadratic performance functional is utilized to overcome the challenge caused by saturating actuators. Then, the monotonicity and convergence of the iterative cost function sequence with the discount factor are analyzed. For facilitating the implementation of the iterative algorithm, two neural networks with Levenberg-Marquardt training algorithm are constructed to approximate the cost function and the control law. Furthermore, the initial control law is obtained by employing the fixed point iteration approach. Finally, two simulation examples are provided to validate the feasibility of the present strategy. It is emphasized that the established control laws are successfully constrained for randomly given initial state vectors.
关键词 ：
adaptive critic adaptive critic control constraints control constraints convergence analysis convergence analysis discounted optimal control discounted optimal control generalized value iteration generalized value iteration
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| GB/T 7714 | Wang, Ding , Zhao, Mingming , Ha, Mingming et al. Discounted near-optimal regulation of constrained nonlinear systems via generalized value iteration [J]. | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL , 2021 , 31 (17) : 8481-8503 . |
| MLA | Wang, Ding et al. "Discounted near-optimal regulation of constrained nonlinear systems via generalized value iteration" . | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 31 . 17 (2021) : 8481-8503 . |
| APA | Wang, Ding , Zhao, Mingming , Ha, Mingming , Qiao, Junfei . Discounted near-optimal regulation of constrained nonlinear systems via generalized value iteration . | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL , 2021 , 31 (17) , 8481-8503 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The wastewater treatment is an effective method for alleviating the shortage of water resources. In this article, a data-driven iterative adaptive tracking control approach is developed to improve the control performance of the dissolved oxygen concentration and the nitrate nitrogen concentration in the nonlinear wastewater treatment plant. First, the model network is established to obtain the steady control and evaluate the new system state. Then, a nonquadratic performance functional is provided to handle asymmetric control constraints. Moreover, the new costate function and the tracking control policy are derived by using the dual heuristic dynamic programming algorithm. In the present control scheme, two neural networks are constructed to approximate the costate function and the tracking control law. Finally, the feasibility of the proposed algorithm is confirmed by applying the designed strategy to the wastewater treatment plant.
关键词 ：
adaptive critic adaptive critic asymmetric control constraints asymmetric control constraints intelligent optimal tracking intelligent optimal tracking nonlinear control nonlinear control wastewater treatment wastewater treatment
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| GB/T 7714 | Wang, Ding , Zhao, Mingming , Qiao, Junfei . Intelligent optimal tracking with asymmetric constraints of a nonlinear wastewater treatment system [J]. | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL , 2021 , 31 (14) : 6773-6787 . |
| MLA | Wang, Ding et al. "Intelligent optimal tracking with asymmetric constraints of a nonlinear wastewater treatment system" . | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 31 . 14 (2021) : 6773-6787 . |
| APA | Wang, Ding , Zhao, Mingming , Qiao, Junfei . Intelligent optimal tracking with asymmetric constraints of a nonlinear wastewater treatment system . | INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL , 2021 , 31 (14) , 6773-6787 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
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 ：
iterative adaptive critic iterative adaptive critic control constraints control constraints neural networks neural networks Adaptive dynamic programming Adaptive dynamic programming nonlinear discrete-time systems nonlinear discrete-time systems
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| GB/T 7714 | Zhao, Mingming , Wang, Ding , Ha, Mingming . Improved Adaptive Critic for Neural Optimal Control of Constrained Nonlinear Discrete-Time Systems [C] . 2020 : 1934-1939 . |
| MLA | Zhao, Mingming et al. "Improved Adaptive Critic for Neural Optimal Control of Constrained Nonlinear Discrete-Time Systems" . (2020) : 1934-1939 . |
| APA | Zhao, Mingming , Wang, Ding , Ha, Mingming . Improved Adaptive Critic for Neural Optimal Control of Constrained Nonlinear Discrete-Time Systems . (2020) : 1934-1939 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this paper, a neural-network-based policy learning method is established to solve robust stabilization for a class of continuous-time nonlinear systems with both internal dynamic uncertainties and input matrix uncertainties. First, the robust stabilization problem is converted to an optimal control problem by choosing an appropriate cast function and proving system stability. Then, in order to solve the Hamilton-Jacobi-Bellman equation, a policy iteration algorithm is employed by constructing and training a critic neural network. The approximate optimal control policy can be obtained by this algorithm, and the solution of the robust stabilization can he derived as well. Finally, a numerical example and an experimental simulation are provided to verify the availability of the proposed strategy.
关键词 ：
intelligent control intelligent control Neural critic learning Neural critic learning uncertain nonlinear systems uncertain nonlinear systems robust stabilization robust stabilization adaptive dynamic programming adaptive dynamic programming
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| GB/T 7714 | Xu, Xin , Wang, Ding . A Neural Policy Learning Method for Robust Stabilization of Uncertain Nonlinear Systems [C] . 2020 : 987-992 . |
| MLA | Xu, Xin et al. "A Neural Policy Learning Method for Robust Stabilization of Uncertain Nonlinear Systems" . (2020) : 987-992 . |
| APA | Xu, Xin , Wang, Ding . A Neural Policy Learning Method for Robust Stabilization of Uncertain Nonlinear Systems . (2020) : 987-992 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
In this paper, the versatile value-iteration-based control method, aimed at affine systems with unknown dynamics, is proposed to deal with the optimal tracking control problem. Neural networks are adopted to approximate system dynamics and a novel approach is presented to estimate the steady state control input based on the established identifier. Additionally, two other neural networks, called the critic network and the action network, are used to implement the optimal tracking control algorithm. Finally, based on the proposed method, the tracking controller is designed to control a specific simulation example. It is shown that, for any randomly given initial state vector, the controller is able to make the affine system track the reference trajectory without knowing the system dynamics.
关键词 ：
Adaptive Dynamic Programming Adaptive Dynamic Programming Affine Systems with Unknown Dynamics Affine Systems with Unknown Dynamics Neuro-Optimal Tracking Control Neuro-Optimal Tracking Control Value Iteration Value Iteration
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| GB/T 7714 | Ha, Mingming , Wang, Ding , Liu, Derong . Value-Iteration-Based Neuro-Optimal Tracking Control for Affine Systems with Completely Unknown Dynamics [C] . 2020 : 1951-1956 . |
| MLA | Ha, Mingming et al. "Value-Iteration-Based Neuro-Optimal Tracking Control for Affine Systems with Completely Unknown Dynamics" . (2020) : 1951-1956 . |
| APA | Ha, Mingming , Wang, Ding , Liu, Derong . Value-Iteration-Based Neuro-Optimal Tracking Control for Affine Systems with Completely Unknown Dynamics . (2020) : 1951-1956 . |
| 导入链接 | NoteExpress RIS BibTex |
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