Adaptive and optimal output feedback control of linear systems: An adaptive dynamic programming approach
Abstract
This paper proposes a computational adaptive optimal output feedback control method for continuous-time linear systems. By periodic sampling, we use measurable input/output data to reconstruct the unmeasurable state, and then utilize adaptive dynamic programming (ADP) technique to iteratively solve the discrete-time algebraic Riccati equation. An exploration noise is introduced for online learning purpose without compromising accuracy of the proposed iterative algorithm. The stability and the optimality of the sampled-data system in close-loop with the proposed control policy are also analyzed. The feasibility of the output feedback ADP scheme is validated by simulation on a third-order linear system.
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The convergence of the online learning control using output feedback is guaranteed under the rank condition stated in Lemma 3.3.
“The convergence of the online learning control using output feedback is guaranteed under the rank condition stated in the form of Lemma 3.3 [47].”
From Data-driven control of hydraulic servo actuator: An event-triggered adaptive dynamic programming approach (2023) · cites this paper as [47]
If the condition of Lemma 3.3 is fulfilled, with some initial stabilizing feedback gain matrix K0, then the sequences {Pj} and {Kj} received from this algorithm converge to their optimal values P and K, respectively.
“Theorem 3.4. If the condition of Lemma 3.3 is fulfilled, with some initial stabilizing feedback gain matrix K 0 , then the sequences n Pj o 1 j = 0 and n Kj o 1 j = 0 received from this algorithm, converge to their optimal values P and K , respectively [46, 47].”
From Data-driven control of hydraulic servo actuator: An event-triggered adaptive dynamic programming approach (2023) · cites this paper as [47]
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References 21 · Cited by 35
- Authors
- Gao, W., Jiang, Y., Jiang, Z.-P., & Chai, T.
- Affiliations
- New York University (US)
- State Key Laboratory of Synthetical Automation for Process Industries (CN)
- Venue
- Proceeding of the 11th World Congress on Intelligent Control and Automation
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 2085-2090
- OpenAlex
- W2058627792
- Cited by
- 35
- References
- 21
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 5 · 2022: 2 · 2021: 5 · 2020: 3 · 2019: 4 · 2018: 1 · 2016: 6 · 2015: 3 · 2014: 1
- FWCI
- 2.80
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- World Congress on Intelligent Control and Automation (publishedVersion)
- Funders
- Division of Electrical, Communications and Cyber Systems
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Adaptive Dynamic Programming Control
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- adaptive optimal control, output feedback control, linear systems, adaptive dynamic programming, state reconstruction, periodic sampling, discrete-time algebraic Riccati equation, exploration noise, online learning, closed-loop stability, continuous-time systems
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