一类多重时滞非线性系统无模型学习自适应控制

对于一类常见多重时滞非线性离散系统，提出了基于动态线性逼近的增量型最小化模型，递推预测模型，无模型学习自适应控制律和带有参数限定时域长度的参数自适应预报递推算法，实现了对存在较在的多重时滞非线性系统的无模型学习自适应控制，该算法不需要受控系统的结构信息，数学,外部实验信号和训练过程，不用解Diophantine方程，无需矩阵运算，在线计算量很小，实时性好，仅用受控系统的I／O数据来设计，传统的未能模动态不存在，通过仿真表明，该算法对于一类非线性系统实现无模型自适应控制是正确和有效的。

Model-free learning adaptive control for nonlinear systems with multiple time delays

Presents the model-free learning adaptive control and its parameter adaptive predicting with parameter control length for a class of nonlinear discrete-time systems with multiple time delays based on dynamic approximate linearization increment minimized model and recursive predicting model method and the use of model -free learning adaptive control for nonlinear systems with heavy multiple time delays, and points out that there is no need for structural information, mathematical model, external testing signals, training process, Diophantine equation's solution and matrix operations, little on-line compution, excellent real-time, and design only by using I/O data of the controlled systems, and no unmodelled dynamics at all, and concludes from simulation results that several typical nonlinear systems given to demonstrate the correctness and effectiveness of the approach proposed.