Neuroadaptive Control With Enhanced Stability and Reliability

The performance of neural network (NN)-driven control systems hinges on the reliability and functionality of the NN unit in the controller. Maintaining the compact set condition for NN training signals (inputs) during operation is crucial for preserving the NN's universal learning and approximation capabilities, yet this requirement is often overlooked in existing studies. This article introduces a constraint transformation-based design method that ensures excitation signals always originate from a fixed region, regardless of initial conditions. By meeting the compactness condition required by the universal approximation theorem, this approach safeguards the functionality of the NN-driven control unit. Additionally, a decaying damping rate is employed to enable the tracking error to asymptotically converge to zero, rather than being ultimately uniformly bounded (UUB). To further ensure robust operation even if the NN underperforms due to an insufficient number of neurons or violation of the compact set condition, a new control strategy is developed based on the worst case behavior of NNs. This "fail-secure" mechanism significantly enhances the reliability of the NN-based control scheme. The effectiveness and benefits of the proposed method are confirmed through numerical simulations, demonstrating its potential to substantially improve the robustness and performance of NN-driven control systems.