Data-driven optimal tracking control of discrete-time multi-agent systems with two-stage policy iteration algorithm

Peng, Zhinan; Zhao, Yiyi; Hu, Jiangping; Ghosh, Bijoy K.

doi:10.1016/j.ins.2018.12.079

Cited by 77 publications

(18 citation statements)

References 30 publications

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“…The PI algorithm is an effective method to solve the various optimal control problems. It has been proved that the iterative cost function and the iterative control strategy in PI will converge to the optimal values

and

through iterations (Peng et al, 2019 , 2020 ).…”

Section: Policy Iteration Based Controllermentioning

confidence: 99%

Data-Driven Optimal Assistance Control of a Lower Limb Exoskeleton for Hemiplegic Patients

et al. 2020

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More recently, lower limb exoskeletons (LLE) have gained considerable interests in strength augmentation, rehabilitation, and walking assistance scenarios. For walking assistance, the LLE is expected to control the affected leg to track the unaffected leg's motion naturally. A critical issue in this scenario is that the exoskeleton system needs to deal with unpredictable disturbance from the patient, and the controller has the ability to adapt to different wearers. To this end, a novel data-driven optimal control (DDOC) strategy is proposed to adapt different hemiplegic patients with unpredictable disturbances. The interaction relation between two lower limbs of LLE and the leg of patient's unaffected side are modeled in the context of leader-follower framework. Then, the walking assistance control problem is transformed into an optimal control problem. A policy iteration (PI) algorithm is utilized to obtain the optimal controller. To improve the online adaptation to different patients, an actor-critic neural network (AC/NN) structure of the reinforcement learning (RL) is employed to learn the optimal controller on the basis of PI algorithm. Finally, experiments both on a simulation environment and a real LLE system are conducted to verify the effectiveness of the proposed walking assistance control method.

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and

through iterations (Peng et al, 2019 , 2020 ).…”

Section: Policy Iteration Based Controllermentioning

confidence: 99%

Data-Driven Optimal Assistance Control of a Lower Limb Exoskeleton for Hemiplegic Patients

et al. 2020

Self Cite

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“…e ADHDP is based on the state system and does not require a controlled object model. us, the ADHDP is also known as a data-driven control method [23], which enables an online learning and control [24]. us, in this study, the USV model is only used as a simulation object, not for the controller design.…”

Section: The Usv Model For Navigating Controlmentioning

confidence: 99%

Adaptive Navigating Control Based on the Parallel Action-Network ADHDP Method for Unmanned Surface Vessel

Huang

Liu

Wen

et al. 2019

Advances in Materials Science and Engineering

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The feedback PID method was mainly used for the navigating control of an unmanned surface vessel (USV). However, when the intelligent control era is coming now, the USV can be navigated more effectively. According to the USV character in its navigating control, this paper presents a parallel action-network ADHDP method. This method connects an adaptive controller parallel to the action network of the ADHDP. The adaptive controller adopts a RBF neural network approximation based on the Lyapunov stability analysis to ensure the system stability. The simulation results show that the parallel action-network ADHDP method has an adaptive control character and can navigate the USV more accurately and rapidly. In addition, this method can also eliminate the overshoot of the ADHDP controller when navigating the USV in various situations. Therefore, the adaptive stability design can greatly improve the navigating control and effectively overcome the ADHDP algorithm limitation. Thus, this adaptive control can be one of the intelligent ADHDP control methods. Furthermore, this method will be a foundation for the development of an intelligent USV controller.

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“…Since the evaluation of the policy in each iteration is calculated from the start state to the end state, it needs a lot of time to get the best policy and optimal value when the state space is large [11]. The policy iteration algorithm proposed in reference [23] has relatively strong learning ability by adding a sub-iteration for iterative performance index functions.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Model-Free Reinforcement Learning Algorithm to Solve Nash Equilibrium for Multi-Agent Cooperative Game Systems

Jiang

Tan

2020

IEEE Access

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Data-driven optimal tracking control of discrete-time multi-agent systems with two-stage policy iteration algorithm

Cited by 77 publications

References 30 publications

Data-Driven Optimal Assistance Control of a Lower Limb Exoskeleton for Hemiplegic Patients

Data-Driven Optimal Assistance Control of a Lower Limb Exoskeleton for Hemiplegic Patients

Adaptive Navigating Control Based on the Parallel Action-Network ADHDP Method for Unmanned Surface Vessel

An Enhanced Model-Free Reinforcement Learning Algorithm to Solve Nash Equilibrium for Multi-Agent Cooperative Game Systems

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