Adaptive Output Feedback MPC with Guaranteed Stability and Robustness
This work proposes an adaptive output feedback model predictive control (MPC) framework for uncertain systems subject to external disturbances. In the absence of exact knowledge about the plant parameters and complete state measurements, the MPC optimization problem is reformulated in terms of their estimates derived from a suitably designed robust adaptive observer. The MPC routine returns a homothetic tube for the state estimate trajectory. Sets that characterize the state estimation errors are then added to the homothetic tube sections, resulting in a larger tube containing the true state trajectory. The two-tier tube architecture provides robustness to uncertainties due to imperfect parameter knowledge, external disturbances, and incomplete state information. Additionally, recursive feasibility and robust exponential stability are guaranteed and validated using a numerical example.
Code (0)
등록된 구현이 없습니다.
Tasks
Model Predictive ControlState EstimationSimilar Papers 제목 키워드 기반
Optimal Output Feedback Learning Control for Discrete-Time Linear Quadratic Regulation
This paper studies the linear quadratic regulation (LQR) problem of unknown discrete-time systems via dynamic output feedback learning control. In contrast to the state feedback, the optimality of the dynamic output feed…
Model Reference-Based Control with Guaranteed Predefined Performance for Uncertain Strict-Feedback Systems
To address the complexities posed by time- and state-varying uncertainties and the computation of analytic derivatives in strict-feedback form (SFF) systems, this study introduces a novel model reference-based control (M…
Composite learning backstepping control with guaranteed exponential stability and robustness
Adaptive backstepping control provides a feasible solution to achieve asymptotic tracking for mismatched uncertain nonlinear systems. However, input-to-state stability depends on high-gain feedback generated by nonlinear…
parameter estimationOutput Feedback Adaptive Optimal Control of Affine Nonlinear systems with a Linear Measurement Model
Real-world control applications in complex and uncertain environments require adaptability to handle model uncertainties and robustness against disturbances. This paper presents an online, output-feedback, critic-only, m…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Control Analysis and Design for Autonomous Vehicles Subject to Imperfect AI-Based Perception
Safety is a critical concern in autonomous vehicle (AV) systems, especially when AI-based sensing and perception modules are involved. However, due to the black box nature of AI algorithms, it makes closed-loop analysis …
Autonomous Vehicles