In this paper, a local tracking control (LTC) scheme is developed via particle swarm optimized neural networks (PSONN) for unknown nonlinear interconnected systems. With the local input–output data, a local neural network identifier is constructed to approximate the local input gain matrix and the mismatched interconnection, which are utilized to derive the LTC. To solve the local Hamilton–Jacobi–Bellman equation, a local critic NN is established to estimate the proper local value function, which reflects the mismatched interconnection. The weight vector of the local critic NN is trained online by particle swarm optimization, thus the success rate of system execution is increased. The stability of the closed-loop unknown nonlinear interconnected system is guaranteed to be uniformly ultimately bounded through Lyapunov’s direct method. Simulation results of two examples demonstrate the effectiveness of the developed PSONN-based LTC scheme.

本文通过粒子群优化神经网络（PSONN）为未知的非线性互连系统开发了一种局部跟踪控制（LTC）方案。利用本地输入输出数据，可以构造一个本地神经网络标识符，以近似本地输入增益矩阵和不匹配的互连，从而得出LTC。为了解决局部哈密尔顿-雅各比-贝尔曼方程，建立了局部注释器NN来估计适当的局部值函数，该函数反映了不匹配的互连。通过粒子群算法在线训练本地评论者NN的权向量，从而提高了系统执行的成功率。通过Lyapunov的直接方法可以保证最终未知的闭环非线性互连系统的稳定性均匀一致。