基于参数辨识的自适应解耦控制算法的研究

多变量模型的复杂结构、强耦合性、被控对象参数的未知、慢时变等问题要求控制器必须具有良好的自适应性,针对以上问题提出了一种基于改进的广义最小方差闭环自适应解耦控制器实现更好的自适应,其由参数可调的控制器和自适应控制律组成,此控制器通过将闭环系统方程的传递函数矩阵等于期望的对角矩阵来实现解耦,同时改进的辨识算法可进行在线辨识控制器的参数实现同步自适应解耦。通过以CARMA为多变量控制模型,采用该方法进行仿真有效的解决了多变量之间的耦合性。结果表明该方法能够适应相应的变化,跟踪性能较好,且具备良好的解耦能力,进而保证了闭环系统的稳定性,从而验证了此方法能够效提高控制系统的稳定性和鲁棒性。

Research on Adaptive Decoupling Control Algorithm Based on Parameter Identification

The complex structure of the multivariable model, the strong coupling, the unknown and slow time-varying parameters of the controlled object parameters require that the controller must have good adaptability. Aiming at the above problem, an improved generalized minimum variance closed-loop adaptive The decoupling controller achieves better adaptation by a parameter-adjustable controller and an adaptive control law that decouples the transfer function matrix of the closed-loop system equation by the desired diagonal matrix The improved identification algorithm can be used to identify the parameters of the on-line controller to achieve synchronous adaptive decoupling. By using CARMA as a multivariable control model, this method is used to solve the coupling between multivariable variables. The results show that the proposed method can adapt to the corresponding changes, the tracking performance is better, and the decoupling ability is good, and the stability of the closed - loop system is ensured, which proves that the method can improve the stability and robustness of the control system.