非线性反馈控制优化的Lyapunov函数模型

Lyapunov Function Model Based on Nonlinear Feedback Control Optimization

针对标准Lyapunov函数在工业复杂系统预测控制的应用中还存在控制约束力较差等问题,本文提出了一种基于非线性反馈控制优化的Lyapunov函数模型。首先采用GB公式引入Lyapunov函数,通过其变分方程来构造Lyapunov函数,同时可以将线性方程化为不同的等价系统,然后采用连续映射对原函数的反馈控制能力进行优化,将其转换为线性赋范空间,扩大非线性仿射控制系统的镇定问题,控制Ly Paunov函数的寻找范围,最后将改进算法对某工业复杂系统进行预测控制的实例仿真。仿真实验结果表明,本文提出的基于非线性反馈控制优化的Lyapunov函数具有更好的约束性,能很好地对复杂工业系统进行预测控制。

Lyapunov function for a standard in the industrial complex predictive control applications also exist problems such as poor control binding. This paper presents a nonlinear feedback control Lyapunov function optimization model, first introduced the use of formula GB Lyapunov function, through its variational equations to construct Lyapunov function, while linear equations can be translated into different equivalent system, then using a continuous mapping of feedback control to optimize the original function, to convert it to a linear normed space, expand the stabilization problem of nonlinear affine control systems, the control range LyPaunov search function, and finally the improved algorithm for an industrial complex systems predictive control examples of simulation. Simulation results show that optimization of nonlinear feedback control based on Lyapunov function proposed in this paper has a better binding, it can be good for complex industrial systems predictive control.