Iterative Learning Predictive Control for Constrained Uncertain Systems
Iterative learning control (ILC) improves the performance of a repetitive system by learning from previous trials. ILC can be combined with Model Predictive Control (MPC) to mitigate non-repetitive disturbances, thus improving overall system performance. However, existing approaches either assume perfect model knowledge or fail to actively learn system uncertainties, leading to conservativeness. To address these limitations we propose a binary mixed-integer ILC scheme, combined with a convex MPC scheme, that ensures robust constraint satisfaction, non-increasing nominal cost, and convergence to optimal performance. Our scheme is designed for uncertain nonlinear systems subject to both bounded additive stochastic noise and additive uncertain components. We showcase the benefits of our scheme in simulation.
Code (0)
등록된 구현이 없습니다.
Tasks
Model Predictive ControlSimilar Papers 제목 키워드 기반
SIT-LMPC: Safe Information-Theoretic Learning Model Predictive Control for Iterative Tasks
Robots executing iterative tasks in complex, uncertain environments require control strategies that balance robustness, safety, and high performance. This paper introduces a safe information-theoretic learning model pred…
Robust Output-Lifted Learning Model Predictive Control
We propose an iterative approach for designing Robust Learning Model Predictive Control (LMPC) policies for a class of nonlinear systems with additive, unmodelled dynamics. The nominal dynamics are assumed to be differen…
modelModel Predictive ControlRobust Learning Model Predictive Control for Linear Systems Performing Iterative Tasks
A robust Learning Model Predictive Controller (LMPC) for uncertain systems performing iterative tasks is presented. At each iteration of the control task the closed-loop state, input and cost are stored and used in the c…
Model Predictive ControlMulti-Rate Planning and Control of Uncertain Nonlinear Systems: Model Predictive Control and Control Lyapunov Functions
Modern control systems must operate in increasingly complex environments subject to safety constraints and input limits, and are often implemented in a hierarchical fashion with different controllers running at multiple …
Model Predictive ControlRobust MPC for Uncertain Linear Systems -- Combining Model Adaptation and Iterative Learning
This paper presents a robust adaptive learning Model Predictive Control (MPC) framework for linear systems with parametric uncertainties and additive disturbances performing iterative tasks. The approach iteratively refi…
Computational EfficiencyModel Predictive Control