- 期刊
Robust Controller Design via Neural Network Computing
類神經網路在強韌控制器設計上之應用
摘要
本文考慮具參數不確定性程序系統之強韌極點配置問題(RCPA)。依據本文所提出的不確定性指標，吾人巧妙地將RCPA問題轉換為相應的Kennedy-Chua類神經網路模型。由Lyapunov穩定性原理的分析，證實所形成的類神經網路模型對於強韌控制器之求解，具有穩定性，且求解的過程不會產生振盪。由模擬的結果顯示，利用類神經網路來設計強韌控制器，為相當直接、方便且易於實現的可行技術，其對不明確化工程序之強韌控制器設計將有實質的助益。
關鍵字
無資料並列摘要
This paper deals with the problem of robust characteristic polynomial assignment (RCPA) for process systems having parametric uncertainties. A novel computation technique, which is mainly based on the idea of converting the RCPA problem into an equivalent Kennedy and Chua neural network model, is proposed. A measure of uncertain constraint violation is introduced in the solution scheme, which reflects the degree of violation of the uncertain constraints and thus makes the resulting neural model having no uncertain terms and easy to solve. With the Lyapunov-based analysis, the new solution model for the RCPA problem is proven to be stable and without oscillation. To demonstrate the effectiveness and applicability of the presented scheme, two illustrative examples are provided. Extensive simulation results show that the presented approach to the robust controller design is simple, effective and applicable.