21 research outputs found
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. The
system is subject to an additive disturbance, and the goal is to learn to
satisfy state and input constraints robustly. Using disturbance measurements
after each iteration, we construct Confidence Support sets, which contain the
true support of the disturbance distribution with a given probability. As more
data is collected, the Confidence Supports converge to the true support of the
disturbance. This enables design of an MPC controller that avoids conservative
estimate of the disturbance support, while simultaneously bounding the
probability of constraint violation. The efficacy of the proposed approach is
then demonstrated with a detailed numerical example.Comment: Added GitHub link to all source code
Stochastic MPC with Realization-Adaptive Constraint Tightening
This paper presents a stochastic model predictive controller (SMPC) for
linear time-invariant systems in the presence of additive disturbances. The
distribution of the disturbance is unknown and is assumed to have a bounded
support. A sample-based strategy is used to compute sets of disturbance
sequences necessary for robustifying the state chance constraints. These sets
are constructed offline using samples of the disturbance extracted from its
support. For online MPC implementation, we propose a novel reformulation
strategy of the chance constraints, where the constraint tightening is computed
by adjusting the offline computed sets based on the previously realized
disturbances along the trajectory. The proposed MPC is recursive feasible and
can lower conservatism over existing SMPC approaches at the cost of higher
offline computational time. Numerical simulations demonstrate the effectiveness
of the proposed approach.Comment: Submitted to ACC 202
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. At each
iteration of a repetitive task, the method constructs an estimate of the
unknown environment constraints using collected closed-loop trajectory data.
This estimated constraint set is improved iteratively upon collection of
additional data. An MPC controller is then designed to robustly satisfy the
estimated constraint set. This paper presents the details of the proposed
approach, and provides robust and probabilistic guarantees of constraint
satisfaction as a function of the number of executed task iterations. We
demonstrate the safety of the proposed framework and explore the safety vs.
performance trade-off in a detailed numerical example.Comment: Long version of the final paper for IEEE-CDC 2020. First two authors
contributed equall
Output Feedback Stochastic MPC with Hard Input Constraints
We present an output feedback stochastic model predictive controller (SMPC)
for constrained linear time-invariant systems. The system is perturbed by
additive Gaussian disturbances on state and additive Gaussian measurement noise
on output. A Kalman filter is used for state estimation and an SMPC is designed
to satisfy chance constraints on states and hard constraints on actuator
inputs. The proposed SMPC constructs bounded sets for the state evolution and a
tube-based constraint tightening strategy where the tightened constraints are
time-invariant. We prove that the proposed SMPC can guarantee an infeasibility
rate below a user-specified tolerance. We numerically compare our method with a
classical output feedback SMPC with simulation results which highlight the
efficacy of the proposed algorithm.Comment: IEEE American Control Conference (ACC) 2023, May 31 - June 2, San
Diego, CA, US