结合免疫优化和LS-SVRM观测器的非线性系统自适应鲁棒控制

针对一类单输入单输出不确定非线性控制系统提出了一种自适应鲁棒控制算法. 由于最小均方支持向量回归机(LS-SVRM)的最终解可以化为一个具有线性约束的二次规划问题, 不存在局部极小, 所以该算法在不要求假设系统的状态向量是可测的条件下通过设计基于LS-SVRM的观测器来估计系统的状态向量; 同时在算法中假设LS-SVRM的最优逼近参数向量和标称参数向量之差的范数和逼近误差的界限是未知的, 因此可通过对未知界限估计的调节来提高系统的鲁棒性. 考虑到LS-SVRM本身参数对LS-SVRM性能的影响, 本文应用一种新的免疫优化算法对LS-SVRM的参数进行优化, 从而提高LS-SVRM的逼近能力. 理论研究和仿真例子证实了所提方法的可行性和有效性.

Adaptive robust control based on immune optimization and LS-SVRM for nonlinear systems

An adaptive robust control algorithm is proposed for a class of SISO uncertain nonlinear control systems. Because the problem of the least squares support vector regression machines(LS-SVRM) is transformed to a quadratic programming problem with linear constraints and the ultimate solution is not a local minimum, the system state vector can be estimated by using an observer based on LS-SVRM, when the system state vector is not completely available. Meanwhile, both the norm of the difference between the optimal approximation parameter vector and the nominal parameter vector, and the bounds of the approximation errors are unknown hypothetically; therefore, we can improve the robustness of the systems by adjusting the estimations of the unknown bounds in the algorithm. Considering the effect of parameters of LS-SVRM upon the performance, we present a new immune algorithm for optimizing the parameters of LS-SVRM to improve the approximation ability of LS-SVRM. The theoretical analysis and a simulation example demonstrate the feasibility and validity of the proposed approach.