In the design of robust Model Predictive Control (MPC) algorithms, data can be used for primarily two purposes: (A) shrinking the feasible domain of the system uncertainty, and (B) enlarging the safe operating region of the system. In modern literature (A) is often referred to with model learning, or model adaptation, and (B) can be interpreted as using data to learn the model of the surrounding agents in the environment, or to learn environment constraints. Both (A) and (B) can enlarge the region of attraction of the MPC policy and improve its performance measured in terms of the closed-loop cost. However, the majority of the existing MPC algorithms that tackle (A) and (B) suffer from at least one the following deficiencies: (i) do not pro...
A robust adaptive model predictive control (MPC) algorithm is presented for linear, time invariant s...
This paper presents stabilizing Model Predictive Controllers (MPC) to be applied to blackbox systems...
We propose Kernel Predictive Control (KPC), a learning-based predictive control strategy that enjoys...
The increasing impact of data-driven technologies across various industries has sparked renewed inte...
Controller design faces a trade-off between robustness and performance, and the reliability of linea...
In control design, the goal is to synthesize policies which map observations to controlactions. Two ...
The topic of learning in control has garnered much attention in recent years, with many researchers ...
For dynamical systems with uncertainty, robust controllers can be designed by assuming that the unce...
A comprehensive approach addressing identification and control for learning-based Model Predictive C...
In this paper, we address the safety and efficiency of data-driven model predictive controllers (DD-...
Model Predictive Control (MPC) repeatedly solves a finite horizon optimal control problem subject to...
Despite the success of reinforcement learning (RL) in various research fields, relatively few algori...
Robust constrained control of linear systems with parametric uncertainty and additive disturbance is...
This paper presents a robust learning-based predictive control strategy for nonlinear systems subjec...
A data-based predictive controller is proposed, offering both robust stability guarantees and online...
A robust adaptive model predictive control (MPC) algorithm is presented for linear, time invariant s...
This paper presents stabilizing Model Predictive Controllers (MPC) to be applied to blackbox systems...
We propose Kernel Predictive Control (KPC), a learning-based predictive control strategy that enjoys...
The increasing impact of data-driven technologies across various industries has sparked renewed inte...
Controller design faces a trade-off between robustness and performance, and the reliability of linea...
In control design, the goal is to synthesize policies which map observations to controlactions. Two ...
The topic of learning in control has garnered much attention in recent years, with many researchers ...
For dynamical systems with uncertainty, robust controllers can be designed by assuming that the unce...
A comprehensive approach addressing identification and control for learning-based Model Predictive C...
In this paper, we address the safety and efficiency of data-driven model predictive controllers (DD-...
Model Predictive Control (MPC) repeatedly solves a finite horizon optimal control problem subject to...
Despite the success of reinforcement learning (RL) in various research fields, relatively few algori...
Robust constrained control of linear systems with parametric uncertainty and additive disturbance is...
This paper presents a robust learning-based predictive control strategy for nonlinear systems subjec...
A data-based predictive controller is proposed, offering both robust stability guarantees and online...
A robust adaptive model predictive control (MPC) algorithm is presented for linear, time invariant s...
This paper presents stabilizing Model Predictive Controllers (MPC) to be applied to blackbox systems...
We propose Kernel Predictive Control (KPC), a learning-based predictive control strategy that enjoys...