基于有限时间滤波控制的电机驱动系统结构/控制一体化设计

Plant/controller co-design of motor driving systems based on finite-time filtering control

  • 摘要: 针对电机驱动系统进行了基于有限时间控制器的结构/控制一体化设计.针对电机驱动系统跟踪控制问题,采用有限时间收敛方法设计了跟踪控制器.考虑系统状态信息不可测的情况,设计了有限时间滤波控制器,在估计系统速度信息的同时实现了有限时间跟踪控制.为进一步提升系统控制性能,考虑结构与控制之间存在的耦合问题,对电机驱动系统进行结构/控制一体化设计.首先针对电机驱动系统设计了同时考虑结构优化和控制器优化的一体化性能指标.所设计一体化性能指标能够在满足控制性能要求的同时,得到所能驱动的最大负载.同时优化系统的结构参数与控制器参数能够使控制系统达到全局最优,从而取得良好的控制效果.随后,采用嵌套优化策略对电机驱动系统的一体化设计问题进行简化,采用自适应步长的布谷鸟搜索算法对控制器参数优化问题进行求解,得到了一体化最优解.通过数值仿真验证了所提方法的有效性.

     

    Abstract: Recently, motor driving systems have been widely applied in the military and industries. Load tracking control is one of the commonly considered issues in such systems. In this study, a plant/controller co-design based on finite-time control was developed for the motor driving system. A finite-time convergent controller was also presented to address the tracking problem in the motor driving system. Because the system state was unknown, a filter was developed to estimate the velocity of the load. The overall system, including the tracking controller and filter, is proven to be finite-time stable. Hence, the upper bound of the convergence time can be determined. To enhance the control performance of the motor driving system, the coupling between plant and controller is considered and a co-design scheme was developed for the motor driving system. First, a combined performance index, which could indicate the largest load with satisfactory control performance, was established. Both the plant and controller parameters were considered in the developed performance index to simultaneously optimize the plant and controller. Through this optimization, the system-level optimality can be determined and a better control performance can be achieved. Moreover, a nested optimization strategy was adopted to simplify the co-design scheme and an adaptive cuckoo search algorithm was used to achieve the co-design result. Through the nested optimization scheme, the controller parameter is optimized in the inner loop and the plant parameter can be optimized in the outer loop. The cuckoo search algorithm exhibits a superior performance because it has fewer parameters that need to be tuned than most existing algorithms. Hence, the co-design problem can be simplified and resolved reliably using the proposed method. Contrastive simulation results indicates the efficacy of the proposed method.

     

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