Multi-dimensional Multi-stage Fuzzy Optimal Control

Along with the digital computer widespread application, the separate system opti-mization problem has become an especially important aspect in the optimal control theory and the application day by day. In 2009, Professor Zhu Yuanguo proposed and studied fuzzy optimal control problem for continuous systems, also has given about the fuzzy optimal control problem on the principle of optimality and the optimal one-dimensional equations, simultaneously and in 2009 year he has studied the one-dimensional separate system fuzzy optimal control question. In this article, we will study the multi-dimensional multistage fuzzy optimal control question, which mainly draw support from the Bellman optimality principle. First, we will establish a multi-dimensional multistage fuzzy opti-mal control model, where the state of each stage is subject to fuzzy events. Then, the establishment model will strive the expected value of the objective function for the best. With the aid of the Bellman optimality principle, its corresponding recursion equation will be available. Multi-stage linear quadratic optimal fuzzy Control problem, is actually a multi-dimensional multistage fuzzy optimal control system, where each stage is affected by a multi-dimensional fuzzy vector (in this fuzzy vector, the component variables are all triangle variable which independent of each other). Through recursion equation ap-plication which infers, we will achieve the best resolution of the system program. In the ordinary circumstances, we first draw support from two numerical methods, namely hybrid intelligent algorithm or finite search method to obtain a series of study samples, then carries on again through the artificial neural networks approaches superiorly, obtains optimal control of the system. Finally, we will give an example to show the feasibility of the above algorithm.