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中文摘要：
针对一类模型未知的具有不确定性和外部干扰的多输入多输出（MIMO）非线性最小相位系统提出了鲁棒自适应输出反馈跟踪控制方案。用高斯径向基函数（RBF）神经网络逼近对象未知非线性，用高增益观测器估计系统不可测量状态。所设计的鲁棒自适应控制器不仅能使闭环系统稳定，所有状态有界，而且跟踪误差一致最终有界，并保证最终边界足够小。仿真结果表明了所提出方法的有效性。
英文摘要：
A robust adaptive tracking control scheme is presented for a class of multi-input multi-output （MIMO） nonlinear minimum phase systems with unknown mathematical models, uncertainties and external disturbances. Gaussian based radial basis function （RBF） neural networks are used to approximate the plant＇s unknown nonlinearities, and a high-gain observer is used to estimate the unmeasured states of the system. The proposed robust adaptive controller can guarantee that： the closed-loop system is stable; all the states are bounded; the tracking errors are uniformly ultimately bounded; and the ultimate bound can be made arbitrarily small. Simulation results demonstrate the effectiveness of the proposed method.
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