The Study Of Multi-Object Optimal Control Application In Vehicle Control

The optimization of nonlinear systems with single or multiple objects can be devided into two kinds: one that can be depicted by precise mathematical model and the other with no system model. Many methods to solve the control problem of nonlinear systems have been put forward in many literatures. And many scholars have devoted much of their efforts to this problem. Yet the optimization control problem of nonlinear systems still remains a very hard problem, especially in the mutiple case. Up to now there is no explicit effective method available to solve the optimization control problem of nonlinear systems with multiple objects. The reason lies in three aspects: First, nonlinear systems have no precise mathematical models, and can only be acquainted from experience or the relationship between their input and output datum. Second, their control objects can not be depicted by precise input control functions. Third, there are much uncertainties in their running process. The key to solve this sort of problems is to find a valid method to describe the relation between the objects and the output response functions of the plant. And the most important thing is to find the relationship between the control functions or plant states and their control inputs, so as to make every object as satisfied as possible, or reach to a compromise whole maximum.As a basis, the definations of object and multi-object are firstly syudied in this paper, as well as their relations. Then the definition of the supporting degree for a rule to one object put forawrd in some literatures is extended. It is announced that the supporting degree for a rule to one object should be the favoring degree of the system response function to certain object during a time period instead of a time instant. Then the definitions of Pareto rule and Pareto rule base put forward in some literatures are also extended, following the conclusion that the to solve the optimization control problem equals to build a Pareto rule base. Any rule in this Pareto rule base satisfies that if any change in the control inptut is made to make one object more desirable, it will cause decline contentment to at least one other objects. So a Pareto rule is a locally preferable solution to the control problem with multi-objects. And a Pareto rule base is a collection of such Pareto rules. The solution of the optimization control problem can be approached by a fuzzy control algorithm based on the Pareto rule base. A method to obtain the Pareto rule base is put forward. Supporting degree of different rules with same response region to each object should be firstly obtained. Then different weight should be endowed to each object mannually according to different importance or priority of different objects , next a compromise supporting degree can be reached. After all Pareto rules in thecollection are considered, the Pareto rule with best supporting degree should be chosen as a best Pareto rule toward the collection. If all possible circumstances are considered, a Pareto rule base composed of such best Pareto rules can is set up. A fuzzy control algorithm base on such a Pareto rule base is an approximate best control algorithm. And last, a car model is studied. A controller using thoeries and method established in this paper is bulit to control the car model. Finally the control result is given, as well as the resource code implemented in Matlab.