3 research outputs found
Indirect-adaptive Model Predictive Control for Linear Systems with Polytopic Uncertainty
We develop an indirect-adaptive model predictive control algorithm for
uncertain linear systems subject to constraints. The system is modeled as a
polytopic linear parameter varying system where the convex combination vector
is constant but unknown. Robust constraint satisfaction is obtained by
constraints enforcing a robust control invariant. The terminal cost and set are
constructed from a parameter-dependent Lyapunov function and the associated
control law. The proposed design ensures robust constraint satisfaction and
recursive feasibility, is input-to-state stable with respect to the parameter
estimation error and it only requires the online solution of quadratic
programs
Formal Methods for Adaptive Control of Dynamical Systems
We develop a method to control discrete-time systems with constant but
initially unknown parameters from linear temporal logic (LTL) specifications.
We introduce the notions of (non-deterministic) parametric and adaptive
transition systems and show how to use tools from formal methods to compute
adaptive control strategies for finite systems. For infinite systems, we first
compute abstractions in the form of parametric finite quotient transition
systems and then apply the techniques for finite systems. Unlike traditional
adaptive control methods, our approach is correct by design, does not require a
reference model, and can deal with a much wider range of systems and
specifications. Illustrative case studies are included.Comment: 8 Pages. Submitted to CDC 201
Linear robust adaptive model predictive control: Computational complexity and conservatism -- extended version
In this paper, we present a robust adaptive model predictive control (MPC)
scheme for linear systems subject to parametric uncertainty and additive
disturbances. The proposed approach provides a computationally efficient
formulation with theoretical guarantees (constraint satisfaction and
stability), while allowing for reduced conservatism and improved performance
due to online parameter adaptation. A moving window parameter set
identification is used to compute a fixed complexity parameter set based on
past data. Robust constraint satisfaction is achieved by using a
computationally efficient tube based robust MPC method. The predicted cost
function is based on a least mean squares point estimate, which ensures
finite-gain stability of the closed loop. The overall algorithm
has a fixed (user specified) computational complexity. We illustrate the
applicability of the approach and the trade-off between conservatism and
computational complexity using a numerical example.
This paper is an extended version of~[1], and contains additional details
regarding the theoretical proof of Theorem~1, the numerical example, and the
offline computations in Appendix~A--B.Comment: Extended version of published paper in Proc. Conference on Decision
and Control (CDC), 2019. Contains additional details regarding the theoretial
proofs, the terminal ingredients and the numerical exampl