作　　者： (何晟）; (夏鲲）;

机构地区： 上海理工大学光电信息与计算机工程学院,上海200072

出　　处： 《电子测量技术》 2017年第8期1-5,共5页

摘　　要： 对于一类基于T-S模糊模型描述的非线性不确定系统,滑模控制算法的稳态误差和动态品质与T-S模糊算法描述模型的准确度相关,利用最小二乘支持向量机算法(LSSVM)学习T-S模糊模型可以很好的逼近实际系统。但是由于LSSVM算法对数据量有一定要求,而且学习速度比较慢,对要求动态响应较高或者内存较小的系统不适用。提出了一种基于改进共轭梯度在线学习算法学习T-S模型,可以实时逼近实际模型,配合滑模控制算法可以达到控制系统的渐进稳定。在不同误差条件下对该控制算法进行仿真实验,在随机误差幅值100以内,系统稳态误差为0.01,同时对时变误差表现出快速的稳定特性,显示了该控制算法较强的鲁棒性。最后,实验还对随机误差幅值为500的系统验证了T-S模型对于系统学习数据的随机误差具有去噪能力。 For a class of uncertain systems based on nonlinear T-S fuzzy model, the steady state error and dynamic quality of sliding mode control algorithm are related to the accuracy of the description model o{ T-S fuzzy algorithm. Using the least squares support vector machine （LSSVM） learning algorithm of T-S fuzzy model can approximate the actual system well. However, due to the LSSVM algorithm has a certain amount of data requirements, and learning speed is relatively slow. The requirements of the algorithm is not suitable for higher dynamic response or less memory. In this paper, an improved conjugate gradient online learning algorithm is proposed to study T-S model, which can approach the actual model in real time, and the algorithm can achieve the asymptotic stability of the control system. In this paper, the control algorithm is simulated under different error conditions. In the random error is less than 100, the steady-state error is 0, 01, and the system showed stable characteristics of fast with time-varying error, which showed the robustness of the control algorithm is strong. Finally, the experimental results whose random error amplitude is 500 show that the T-S model has the ability to de noise the random error of the system.

关 键 词： 模糊模型 滑模控制 共轭梯度法 最小二乘支持向量机 鲁棒性