• Corpus ID: 119172275

Robust affine control of linear stochastic systems

  title={Robust affine control of linear stochastic systems},
  author={Georgios Kotsalis and Guanghui Lan},
  journal={arXiv: Optimization and Control},
In this work we provide a computationally tractable procedure for designing affine control policies, applied to constrained, discrete-time, partially observable, linear systems subject to set bounded disturbances, stochastic noise and potentially Markovian switching over a finite horizon. We investigate the situation when performance specifications are expressed via averaged quadratic inequalities on the random state-control trajectory. Our methodology also applies to steering the density of… 


Design of Affine Controllers via Convex Optimization
This work addresses the problem of designing a general affine causal controller, in which the control input is an affine function of all previous measurements, in order to minimize a convex objective, in either a stochastic or worst-case setting.
Optimization over state feedback policies for robust control with constraints
It is shown that the class of admissible affine state feedback control policies with knowledge of prior states is equivalent to the classOf admissible feedback policies that are affine functions of the past disturbance sequence, which implies that a broad class of constrained finite horizon robust and optimal control problems can be solved in a computationally efficient fashion using convex optimization methods.
Output feedback receding horizon control of constrained systems
A time-invariant control law is developed that can be computed by solving a finite-dimensional tractable optimization problem at each time step that guarantees that the closed-loop system satisfies the constraints for all time.
Finite-horizon covariance control for discrete-time stochastic linear systems subject to input constraints
  • E. Bakolas
  • Mathematics, Computer Science
  • 2018
The main steps for the transcription of the covariance control problem is presented, which is originally formulated as a stochastic optimal control problem into a deterministic nonlinear program (NLP) with a convex performance index and with both convex and non-convex constraints.
Infinite time reachability of state-space regions by using feedback control
In this paper we consider some aspects of the problem of feedback control of a time-invariant uncertain system subject to state constraints over an infinite-time interval. The central question that
Optimal Steering of a Linear Stochastic System to a Final Probability Distribution, Part II
It is shown that covariances of admissible stationary Gaussian distributions are characterized by a certain Lyapunov-like equation and, in fact, they coincide with the class of stationary state covariance that can be attained by a suitable stationary colored noise as input.
A minimax control problem for sampled linear systems
A linear differential system is subject to a bounded control and a bounded disturbance. The controller receives the value of the state at a finite number of fixed sampling times. The cost is a convex
Constrained Stochastic LQC: A Tractable Approach
This paper presents an alternative approach based on results from robust optimization to solve the stochastic linear-quadratic control (SLQC) problem, and considers a tight, second-order cone approximation to the SDP that can be solved much more efficiently when the problem has additional constraints.
Control of linear dynamic systems with set constrained disturbances
A linear dynamic system with input and observation uncertainties is studied. The uncertainties are constrained to be contained in specified sets. No probabilistic structure is assumed. The problem of
Optimal Steering of a Linear Stochastic System to a Final Probability Distribution, Part I
The results provide the first implementable form of the optimal control for a general Gauss-Markov process and establishes directly the property of Schrödinger bridges as the most likely random evolution between given marginals to the present context of linear stochastic systems.