Search Results for author:
Found 13 papers, 7 papers with code
Output Feedback Stochastic MPC with Hard Input Constraints
We present an output feedback stochastic model predictive controller (SMPC) for constrained linear time-invariant systems.
Stochastic MPC with Realization-Adaptive Constraint Tightening
A sample-based strategy is used to compute sets of disturbance sequences necessary for robustifying the state chance constraints.
A Simple Robust MPC for Linear Systems with Parametric and Additive Uncertainty
We propose a simple and computationally efficient approach for designing a robust Model Predictive Controller (MPC) for constrained uncertain linear systems.
Learning How to Solve Bubble Ball
"Bubble Ball" is a game built on a 2D physics engine, where a finite set of objects can modify the motion of a bubble-like ball.
Learning to Play Cup-and-Ball with Noisy Camera Observations
In this paper, we present a learning model based control strategy for the cup-and-ball game, where a Universal Robots UR5e manipulator arm learns to catch a ball in one of the cups on a Kendama.
Robust MPC for Linear Systems with Parametric and Additive Uncertainty: A Novel Constraint Tightening Approach
We propose a novel approach to design a robust Model Predictive Controller (MPC) for constrained uncertain linear systems.
Learning to Satisfy Unknown Constraints in Iterative MPC
We propose a control design method for linear time-invariant systems that iteratively learns to satisfy unknown polyhedral state constraints.
Exploiting Model Sparsity in Adaptive MPC: A Compressed Sensing Viewpoint
This paper proposes an Adaptive Stochastic Model Predictive Control (MPC) strategy for stable linear time-invariant systems in the presence of bounded disturbances.
Learning Robustness with Bounded Failure: An Iterative MPC Approach
We propose an approach to design a Model Predictive Controller (MPC) for constrained Linear Time Invariant systems performing an iterative task.
Adaptive MPC under Time Varying Uncertainty: Robust and Stochastic
We consider a linear system, in presence of bounded time varying additive uncertainty.
Relaxed Actor-Critic with Convergence Guarantees for Continuous-Time Optimal Control of Nonlinear Systems
The warm-up phase minimizes the square of the Hamiltonian to achieve admissibility, while the generalized policy iteration phase relaxes the update termination conditions for faster convergence.
Safe and Near-Optimal Policy Learning for Model Predictive Control using Primal-Dual Neural Networks
In contrast to most existing approaches, we not only learn the control policy, but also a "certificate policy", that allows us to estimate the sub-optimality of the learned control policy online, during execution-time.
Adaptive Trajectory Planning and Optimization at Limits of Handling
In this paper, we tackle the problem of trajectory planning and control of a vehicle under locally varying traction limitations, in the presence of suddenly appearing obstacles.
Robotics
