Neuro-Optimal Tracking Control for Continuous Stirred Tank Reactor With Input Constraints

Zhou, Wei; Liu, Huachao; He, Haibo; Jiang, Yi; Li, Taifu

doi:10.1109/tii.2018.2884214

Cited by 36 publications

(12 citation statements)

References 35 publications

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“…Adaptive dynamic programming (ADP) [1][2][3][4], which integrates the advantages of reinforcement learning (RL) [5][6][7][8] and adaptive control, has become a powerful tool in solving optimal control problems. With decades of development, ADP has also provided many approaches to solve other control problems, such as robust control [9,10], optimal control with input constraints [11,12], optimal tracking control [13,14], zero-sum games [15], and non-zero-sum games [16]. Furthermore, ADP methods have been widely applied to the real-world systems, such as water-gas shift reaction [17], battery management [18], microgrid systems [19,20], and Quanser helicopter [21].…”

Section: Introductionmentioning

confidence: 99%

Neural Network‐Based Intelligent Computing Algorithms for Discrete‐Time Optimal Control with the Application to a Cyberphysical Power System

Jiang

Zhang

et al. 2021

Complexity

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Adaptive dynamic programming (ADP), which belongs to the field of computational intelligence, is a powerful tool to address optimal control problems. To overcome the bottleneck of solving Hamilton–Jacobi–Bellman equations, several state-of-the-art ADP approaches are reviewed in this paper. First, two model-based offline iterative ADP methods including policy iteration (PI) and value iteration (VI) are given, and their respective advantages and shortcomings are discussed in detail. Second, the multistep heuristic dynamic programming (HDP) method is introduced, which avoids the requirement of initial admissible control and achieves fast convergence. This method successfully utilizes the advantages of PI and VI and overcomes their drawbacks at the same time. Finally, the discrete-time optimal control strategy is tested on a power system.

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Section: Introductionmentioning

confidence: 99%

Neural Network‐Based Intelligent Computing Algorithms for Discrete‐Time Optimal Control with the Application to a Cyberphysical Power System

Jiang

Zhang

et al. 2021

Complexity

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“…Recently, adaptive dynamic programming (ADP) has been used extensively in different applications to solve different optimal control problems [25–28]. Usually, there are two neural networks in an ADP design.…”

Section: Introductionmentioning

confidence: 99%

Optimal torque control of permanent magnet synchronous motors using adaptive dynamic programming

Khiabani

Heydari

2020

IET power electron.

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“…By designing a multivariable tracking scheme, a simulation experiment of multivariable tracking control is realized. In [18], a novel data-drive neuro-optimal tracking control algorithm was proposed for unknown nonlinear systems. The proposed ADP controller was applied to a continuous stirred reactor system to verify its effectiveness and performance.…”

Section: Introductionmentioning

confidence: 99%

Optimal Control for Aluminum Electrolysis Process Using Adaptive Dynamic Programming

et al. 2020

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Optimal control of aluminum electrolysis production process (AEPP) has long been a challenging industrial issue due to its inherent difficulty in establishing an accurate dynamic model. In this paper, a novel robust optimal control algorithm based on adaptive dynamic programming (ADP) is proposed for the AEPP, where the system subjects to input constraints. First, to establish an accurate dynamic model for the AEPP system, recursive neural network (RNN) is employed to reconstruct the system dynamic using the input-output production data. To ensure input constraints are not to exceed the bound of the actuator, the optimal control problem of the AEPP is formulated under a new nonquadratic form performance index function. Then, considering the perturbation of the AEPP, the robust control problem is effectively converted to the constrained optimal control problem via system transformation. Furthermore, a single critic network framework is developed to obtain the approximate solution of the Hamilton-Jacobi-Bellman (HJB) equation. Finally, the proposed ADP controller is applied to the AEPP system to validate the effectiveness and performance. INDEX TERMS Adaptive dynamic programming (ADP), optimal control, input constraints, aluminum electrolysis.

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Neuro-Optimal Tracking Control for Continuous Stirred Tank Reactor With Input Constraints

Cited by 36 publications

References 35 publications

Neural Network‐Based Intelligent Computing Algorithms for Discrete‐Time Optimal Control with the Application to a Cyberphysical Power System

Neural Network‐Based Intelligent Computing Algorithms for Discrete‐Time Optimal Control with the Application to a Cyberphysical Power System

Optimal torque control of permanent magnet synchronous motors using adaptive dynamic programming

Optimal Control for Aluminum Electrolysis Process Using Adaptive Dynamic Programming

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