塔式光热电站定日镜的鲁棒自适应控制算法

Robust Adaptive Control for Heliostat in Tower Solar Power Plant

塔式光热电站定日镜伺服跟踪控制系统是典型的高阶非线性系统,传统PID控制器无法完成精确的跟踪控制。提出一种基于RBF神经网络的塔式光热电站定日镜鲁棒自适应动态面控制方法。采用自适应RBF神经网络动态面控制技术解决传统反步法中的"微分爆炸"及系统中存在未知参数和不确定项的问题,简化系统控制律设计。通过在控制器设计过程中引入滑模面,在提高系统鲁棒性的同时,加快系统的响应速度。通过设计误差转换函数和跟踪性能指标函数,引入一个新的误差变量,使得系统跟踪误差满足预先设定的性能指标。通过Modeling Tech电力电子仿真实验平台进行半实物实验验证,实验结果表明提出的自适应定日镜控制方案具有良好的跟踪性能,并且能够保证系统跟踪误差始终控制在预先设定的范围之内,满足预先设定的性能指标,验证了控制方案的有效性。最后通过现场测试实验,取得了良好的控制效果。

An adaptive RBF neural networks based dynamic surface sliding mode control method was proposed for heliostat in solar power tower plant.The"explosion of complexity"in the traditional backstepping method was avoided.The RBF NNs was used to approximate the unknown parameters and uncertainties in the system.By introducing the sliding mode method into the controller design procedure,the robustness and the response of the system was improved.By using the error transformation function,a new error variable was introduced to make the output of the system satisfy the prespecified performance.The hardware-in-the-loop experimental results show that the tracking errors are always satisfy the prespecified performance index.Finally,satisfactory control performance is achieved in the heliostat control system.