Abstract:
Optimizing a performance objective during control operation while also ensuring constraint satisfactions at all times is important in practical applications. Existing wor...Show MoreMetadata
Abstract:
Optimizing a performance objective during control operation while also ensuring constraint satisfactions at all times is important in practical applications. Existing works on solving this problem usually require a complicated and time-consuming learning procedure by employing neural networks, and the results are only applicable for simple or time-invariant constraints. In this work, these restrictions are removed by a newly proposed adaptive neural inverse approach. In our approach, a new universal barrier function, which is able to handle various dynamic constraints in a unified manner, is proposed to transform the constrained system into an equivalent one with no constraint. Based on this transformation, a switched-type auxiliary controller and a modified criterion for inverse optimal stabilization are proposed to design an adaptive neural inverse optimal controller. It is proven that optimal performance is achieved with a computationally attractive learning mechanism, and all the constraints are never violated. Besides, improved transient performance is obtained in the sense that the bound of the tracking error could be explicitly designed by users. An illustrative example verifies the proposed methods.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 35, Issue: 8, August 2024)
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- IEEE Keywords
- Index Terms
- Optimal Control ,
- Nonlinear Systems ,
- Adaptive Control ,
- Adaptive Neural Control ,
- Inverse Optimization ,
- Adaptive Optimal Control ,
- Inverse Optimal Control ,
- Neural Network ,
- Barrier Function ,
- Adaptive Approach ,
- Tracking Error ,
- Learning Procedure ,
- Inverse Approach ,
- Transient Performance ,
- Universal Function ,
- Constraint Satisfaction ,
- Constrained System ,
- Estimation Error ,
- System State ,
- Control Method ,
- Barrier Lyapunov Function ,
- Optimal Control Problem ,
- Feasibility Conditions ,
- Control Input ,
- Unknown Function ,
- Limited Conditions ,
- Output Control ,
- Design Parameters ,
- Tracking Performance ,
- Control Objective
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Optimal Control ,
- Nonlinear Systems ,
- Adaptive Control ,
- Adaptive Neural Control ,
- Inverse Optimization ,
- Adaptive Optimal Control ,
- Inverse Optimal Control ,
- Neural Network ,
- Barrier Function ,
- Adaptive Approach ,
- Tracking Error ,
- Learning Procedure ,
- Inverse Approach ,
- Transient Performance ,
- Universal Function ,
- Constraint Satisfaction ,
- Constrained System ,
- Estimation Error ,
- System State ,
- Control Method ,
- Barrier Lyapunov Function ,
- Optimal Control Problem ,
- Feasibility Conditions ,
- Control Input ,
- Unknown Function ,
- Limited Conditions ,
- Output Control ,
- Design Parameters ,
- Tracking Performance ,
- Control Objective
- Author Keywords