基于复合自适应律的直线电机自适应鲁棒控制

自适应鲁棒控制(ARC)能克服参数不确定性与扰动对系统的影响, 具有良好的输出跟踪性能. 然而常规ARC的参数估计值难以逼近真值. 为实现高性能的控制与准确的参数估计, 本文提出了基于复合自适应律的自适应鲁棒控制(CAARC). 该方法同时采用了输出误差与参数估计误差的相关信息构造自适应律, 具有比常规ARC更好的参数估计效果. 本文在理论上证明了CAARC的闭环稳定性与参数估计误差的收敛性, 并通过分析表明CAARC具有比常规ARC更好的输出跟踪性能, 最后通过仿真验证了该方法的有效性.

An adaptive robust control for linear motors based on composite adaptation

The adaptive robust control(ARC) not only reduces the influence of parametric uncertainties and disturbance to the system, but also provides a good performance in output tracking. However, the parameter estimates of traditional ARC do not give good approximations to the true values of the corresponding parameters. To achieve a high performance control and accurate parameter estimation, we propose a novel composite-adaptation-based adaptive robust control method(CAARC). This method combines the information of output tracking error with the parameter estimate error in constructing the adaptation law, which gives a better result in parameter estimation than that from the traditional ARC. The close-loop stability and the convergence of parameter estimates are also proved. It has been shown by analysis that CAARC has a better tracking performance than the traditional ARC. Finally, the effectiveness of the proposed method is demonstrated by simulation.