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Offline and Online Adaptive Critic Control Designs With Stability Guarantee Through Value Iteration
IEEE Transactions on Cybernetics
|September 13, 2021
Summary
This study develops new algorithms for closed-loop system stability using value iteration (VI) and adaptive dynamic programming (ADP). The online ADP algorithm ensures system state trajectory convergence to the origin, enhancing control policy stability.
Area of Science:
- Control Systems Engineering
- Artificial Intelligence
- Optimization Theory
Background:
- Ensuring closed-loop system stability is crucial for reliable control.
- Traditional methods may struggle with complex systems or suboptimal policies.
- Value iteration (VI) and adaptive dynamic programming (ADP) offer powerful frameworks for control policy generation.
Purpose of the Study:
- To investigate stability properties of closed-loop systems under various control policies.
- To develop novel offline and online algorithms for generating stable control policies.
- To enhance the stability guarantees of adaptive dynamic programming for linear systems.
Main Methods:
- An offline integrated value iteration (VI) scheme combining VI and policy iteration was developed.
- An online adaptive dynamic programming (ADP) algorithm was designed using the concept of attraction domain.
- Theoretical analysis focused on admissibility criteria and domain of attraction for stability guarantees.
Main Results:
- The integrated VI scheme provides stability guarantees and facilitates admissible control policies.
- The online ADP algorithm ensures convergence of the state trajectory to the origin.
- For linear systems, the online ADP algorithm demonstrates enhanced stability, tolerating finite unstable policy elements.
Conclusions:
- The developed offline and online algorithms effectively ensure closed-loop system stability.
- The online ADP algorithm offers robust stability performance, particularly for linear systems.
- These advancements contribute to more reliable and predictable control system design.
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