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Swarm Intelligence Based Model Predictive Control Strategy for Optimal State Control of Discrete Time-varying MIMO Linear Systems

机译:基于群体智能的离散时变MIMO线性系统最优状态控制模型预测控制策略

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摘要

It is a challenging task to effectively control multi-input and multi-output (MIMO) discrete time-varying linear systems. This paper proposes a swarm intelligence based model predictive control (MPC) strategy for addressing the challenge. First, a swarm intelligence based iterative dynamic optimal control solver is proposed to avoid the difficulty in solving the algebraic Riccati equation of finite-horizon optimal state control problem. Then, a swarm intelligence based online optimal controller is designed based on the MPC strategy, which can extend the optimal control problem from the finite-horizon to the infinite-horizon. Finally, the feedback structure of the online optimal state control system for MIMO discrete time-varying linear systems is constructed. A real-time simulation and a practical control experiment of a first order inverted pendulum system are employed to elborate the proposed method. The results show that the proposed method has the high efficiency, high accuracy, and anti-interference capability.
机译:有效控制多输入多输出 (MIMO) 离散时变线性系统是一项具有挑战性的任务。该文提出了一种基于群体智能的模型预测控制(MPC)策略来应对这一挑战。首先,提出一种基于群体智能的迭代动态最优控制求解器,避免了有限视界最优状态控制问题的代数Riccati方程求解困难的问题。然后,基于MPC策略设计了一种基于群体智能的在线最优控制器,将最优控制问题从有限视界扩展到无限视界;最后,构建了MIMO离散时变线性系统在线最优状态控制系统的反馈结构。采用一阶倒摆系统的实时仿真和实际控制实验对所提方法进行了验证。结果表明,所提方法具有高效率、高精度和抗干扰能力。

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