基于模糊神经网络的MIMO系统自适应解耦控制

针对一类具有不确定性的多输入多输出(MIMO)非线性系统控制问题,提出了基于模糊神经网络的自适应解耦控制方法.根据分散控制理论和反馈线性化方法设计了MIMO非线性系统的分通道解耦控制律,然后把通道耦合项和不确定性项归结为总的系统扰动项,利用模糊神经网络观测器得到其估计值,并作为补偿信号加入到解耦控制律中.证明了所设计的解耦控制律、模糊神经网络观测器以及模糊神经网络权值向量自适应律可以保证控制误差、扰动估计误差和权值向量误差一致最终收敛.仿真中将本文的方法与传统的输出反馈控制律进行了对比,结果表明加入的补偿控制信号消除了通道耦合和不确定性带来的不利影响,验证了该方法的有效性和稳定性.

Adaptive decoupling control of a MIMO system based on fuzzy neural networks

According to the control problem of a class of uncertain multiple-input multiple-output (MIMO) nonlinear systems, an adaptive decoupling control approach based on fuzzy neural networks was proposed. Firstly, a sub-channel decoupling control law of MIMO nonlinear systems was designed using decentralized control theory and feedback linearization approach. Secondly, the approximation of the system coupling terms and uncertainty terms were obtained by a fuzzy neural networks observer and compensated into the control law as compensation signal. It was proved that the control law, the observer and the weighted vector adaptive law could guarantee the uniform convergence of the errors of the output variable, the observer variable and the weighted vector finally. Simulations were carried on a typical uncertain MIMO system. The proposed method was compared with a traditional output feedback control method without adding compensation control signal. The simulation results show that the influence caused by coupling among the channels and uncertainty is eliminated by the compensation control and the observer errors can converge. The results validate the effectiveness and stability of the proposed control approach.