| 摘要: |
| This article presents an event-triggered H∞ consensus control scheme using reinforcement learning (RL) for nonlinear
second-order multi-agent systems (MASs) with control constraints. First, considering control constraints, the constrained H∞
consensus problem is transformed into a multi-player zero-sum game with non-quadratic performance functions. Then, an
event-triggered control method is presented to conserve communication resources and a new triggering condition is developed
for each agent to make the triggering threshold independent of the disturbance attenuation level. To derive the optimal controller
that can minimize the cost function in the case of worst disturbance, a constrained Hamilton–Jacobi–Bellman (HJB) equation
is defined. Since it is difficult to solve analytically due to its strongly non-linearity, reinforcement learning (RL) is implemented
to obtain the optimal controller. In specific, the optimal performance function and the worst-case disturbance are approximated
by a time-triggered critic network; meanwhile, the optimal controller is approximated by event-triggered actor network. After
that, Lyapunov analysis is utilized to prove the uniformly ultimately bounded (UUB) stability of the system and that the
network weight errors are UUB. Finally, a simulation example is utilized to demonstrate the effectiveness of the control
strategy provided. |
| 关键词: H∞ optimal control · Input constrains · Multi-agent systems (MASs) · Reinforcement learning (RL) |
| DOI：https://doi.org/10.1007/s11768-023-00177-4 |
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| 基金项目: |
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| Event-triggered H∞ consensus control for input-constrained multi-agent systems via reinforcement learning |
| Jinxuan Zhang1,Chang-E Ren1 |
| (1 College of Information Engineering, Capital Normal University, Beijing 100089, China) |
| Abstract: |
| This article presents an event-triggered H∞ consensus control scheme using reinforcement learning (RL) for nonlinear
second-order multi-agent systems (MASs) with control constraints. First, considering control constraints, the constrained H∞
consensus problem is transformed into a multi-player zero-sum game with non-quadratic performance functions. Then, an
event-triggered control method is presented to conserve communication resources and a new triggering condition is developed
for each agent to make the triggering threshold independent of the disturbance attenuation level. To derive the optimal controller
that can minimize the cost function in the case of worst disturbance, a constrained Hamilton–Jacobi–Bellman (HJB) equation
is defined. Since it is difficult to solve analytically due to its strongly non-linearity, reinforcement learning (RL) is implemented
to obtain the optimal controller. In specific, the optimal performance function and the worst-case disturbance are approximated
by a time-triggered critic network; meanwhile, the optimal controller is approximated by event-triggered actor network. After
that, Lyapunov analysis is utilized to prove the uniformly ultimately bounded (UUB) stability of the system and that the
network weight errors are UUB. Finally, a simulation example is utilized to demonstrate the effectiveness of the control
strategy provided. |
| Key words: H∞ optimal control · Input constrains · Multi-agent systems (MASs) · Reinforcement learning (RL) |
