A Priori Guaranteed Evolution Within the Neural Network Approximation Set and Robustness Expansion via Prescribed Performance Control
A Priori Guaranteed Evolution Within the Neural Network Approximation Set and Robustness Expansion via Prescribed Performance Control
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DOI：
10.1109/tnnls.2012.2186152
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发表时间：
2012-02
影响因子：
10.4
通讯作者：
Charalampos P. Bechlioulis;G. Rovithakis
中科院分区：
文献类型：
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作者：
Charalampos P. Bechlioulis;G. Rovithakis
设计了一种严格反馈系统的神经自适应控制方案，该方案能够在存在未知系统非线性和外部干扰的情况下，对输出误差实现规定的性能保证，同时保持所有闭环信号有界。上述性质不需要特殊的初始化过程或棘手的控制增益选择，而是通过建设性的方法解决神经网络控制中长期存在的问题，即先验地保证系统状态在神经网络的逼近能力所保持的紧域内严格进化。此外，还证明了对外部干扰的鲁棒性得到了显著扩展，唯一的实际约束是所需控制努力的大小。对比仿真研究阐明并验证了该方法。
A neuroadaptive control scheme for strict feedback systems is designed, which is capable of achieving prescribed performance guarantees for the output error while keeping all closed-loop signals bounded, despite the presence of unknown system nonlinearities and external disturbances. The aforementioned properties are induced without resorting to a special initialization procedure or a tricky control gains selection, but addressing through a constructive methodology the longstanding problem in neural network control of a priori guaranteeing that the system states evolve strictly within the compact region in which the approximation capabilities of neural networks hold. Moreover, it is proven that robustness against external disturbances is significantly expanded, with the only practical constraint being the magnitude of the required control effort. A comparative simulation study clarifies and verifies the approach.