Generalized Receding Horizon Control of Fuzzy Systems Based on Numerical Optimization Algorithm
Generalized Receding Horizon Control of Fuzzy Systems Based on Numerical Optimization Algorithm
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DOI：
10.1109/tfuzz.2009.2031560
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发表时间：
2009-12
影响因子：
11.9
通讯作者：
Chonghui Song;Jinchun Ye;Derong Liu;Q. Kang
中科院分区：
文献类型：
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作者：
Chonghui Song;Jinchun Ye;Derong Liu;Q. Kang
带约束的模糊系统的最优控制仍然是一个未解决的问题。我们的重点关注的模糊系统的滚动时域控制（RHC）计划派生的最优控制问题。我们考虑的方法来数值计算一般模糊系统的值函数。利用有限差分和S形变换发展的数值方法是求解Hamilton-Jacobi-Bellman（HJB）方程的稳定和收敛的算法。为了提高计算精度和减少计算时间，提出了一种优化方法.在优化过程中采用了并行处理的方法。最后将优化结果应用于一般模糊动态系统的控制器设计。利用常规RHC方案的原理，对几类模糊动态系统设计了RHC形式的控制器。基本思想如下。首先，用数值方法计算价值函数。然后，将该值函数作为模糊控制器的参数设计参数，利用逆李雅普诺夫函数设计方法，对模糊系统的RHC控制器进行了重新设计。证明了闭环系统是渐近稳定的。讨论了控制器再设计方案的工程实现。同时，还介绍了可以提高闭环性能的并行处理框架。
The optimal control of fuzzy systems with constraints is still an open problem. Our focus concerns the optimal control problem of fuzzy systems derived from receding horizon control (RHC) schemes. We consider methods to numerically compute the value function for general fuzzy systems. The numerical method that is developed using the finite difference with sigmoidal transformation is a stable and convergent algorithm for the Hamilton-Jacobi-Bellman (HJB) equation. An optimization procedure is developed to increase the calculation accuracy with less computation time. A parallel-processing method is employed in the optimization procedure. The optimization results are applied to the controller design of general fuzzy dynamic systems. Employing the principle of conventional RHC schemes, RHC-form controllers are designed for some classes of fuzzy dynamic systems. The basic ideas are as follows. First, the value function is calculated by numerical methods. Then, the value function is used as controller-design parameters to redesign RHC controllers for fuzzy systems, which is motivated by the inverse Lyapunov function design method. It is proven that the closed-loop system is asymptotically stable. An engineering implementation of the controller redesign scheme is discussed. Meanwhile, the parallel-processing framework that can improve the closed-loop performance is also introduced.