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中文摘要：
为了研究一类多输入多输出强非线性系统的自适应跟踪问题，采用RBF神经网络逼近模型不确定性，外界干扰和建模误差采用非线性阻尼项进行补偿，并将动态面控制与Nussbaum增益技术结合，提出了一种鲁棒自适应神经网络跟踪控制算法．该算法不仅能够解决系统中控制方向完全未知问题和可能存在的控制器奇异值问题，而且能够避免传统后推方法的计算膨胀问题，从而大大降低了控制器的复杂性，使之易于工程实现．同耐，该算法保证了闭环系统的稳定性，并具有良好的鲁棒性．仿真结果验证了控制器的有效性．
英文摘要：
A problem in adaptive tracking control was considered for a class of highly nonlinear multi-input multi- output （MIMO） systems with both unknown system nonlinearities and unknown virtual control gain nonlinearities. By employing a radial basis function （RBF） neural network （NN） to approximate uncertain functions, and a non- linear damping item to compensate for both external disturbances and modeling errors, a robust adaptive neural net- work control algorithm was developed based on dynamic surface control （DSC） and the Nussbaum gain approach. The proposed algorithm not only both solves problems of unknown control direction and possible controller singulari- ty, but also solves the problem of ＂explosion of complexity＂ in the conventional backstepping method, reducing the computational load of the algorithm and making it convenient to implement in applications. In addition, the algo- rithm has good robustness, guaranteeing stability in a closed-loop system. Simulation results validated its effective- ness and performance.
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同项目期刊论文
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Adaptive neural output feedback controller design with reduced-order observer for a class of uncerta
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