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Nonlinear Q-Design for Convex Stochastic Control
Convex optimization nonlinear control Q-parameter stochastic control
2015/7/9
In this note we describe a version of the Q-design method that can be used to design nonlinear dynamic controllers for a discrete-time linear time-varying plant, with convex cost and constraint functi...
Shrinking-Horizon Dynamic Programming
dynamic programming model predictive control revenue management
2015/7/9
We describe a heuristic control policy, for a general finite-horizon stochastic control problem, that can be used when the current process disturbance is not conditionally independent of previous dist...
Design of affine controllers via convex optimization
Affi ne controller dynamical system dynamic linear programming
2015/7/9
We consider a discrete-time time-varying linear dynamical system, perturbed by process noise, with linear noise corrupted measurements, over a finite horizon. We address the problem of designing a gen...
Receding Horizon Control: Automatic Generation of High-Speed Solvers
Receding Horizon Control Automatic Generation High-Speed Solvers
2015/7/9
Receding horizon control (RHC), also known as model predictive control (MPC), is a general purpose control scheme that involves repeatedly solving a constrained optimization problem, using predictions...
Fast Evaluation of Quadratic Control-Lyapunov Policy
Fast Evaluation Quadratic Control-Lyapunov Policy
2015/7/9
The evaluation of a control-Lyapunov policy, with quadratic Lyapunov function, requires the solution of a quadratic program (QP) at each time step. For small problems this QP can be solved explicitly;...
Controller Coefficient Truncation Using Lyapunov Performance Certificate
coeffi cient truncation controller design LMI methods
2015/7/9
We describe a method for truncating the coefficients of a linear controller while guaranteeing that a given set of relaxed performance constraints is met. Our method sequentially and greedily truncate...
Load Reduction of Wind Turbines Using Receding Horizon Control
Load Reduction Wind Turbines Receding Horizon Control
2015/7/9
Large scale wind turbines are lightly damped mechanical structures driven by wind that is constantly fluctuating. In this paper, we address the design of a model-based receding horizon control scheme ...
Nonconvex Model Predictive Control for Commercial Refrigeration
Energy management Optimization methods Predictive Control Nonlinear control systems
2015/7/9
We consider the control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms. In each time p...
In this paper we introduce a control policy which we refer to as the iterated approximate value function policy. The generation of this policy requires two stages, the first one carried out off-line, ...
We apply an operator splitting technique to a generic linear-convex optimal control problem, which results in an algorithm that alternates between solving a quadratic control problem, for which there ...
Model Predictive Control for Wind Power Gradients
wind power ramps electrical grid integration disturbance rejection model predictive control convex optimization
2015/7/9
We consider the operation of a wind turbine and a connected local battery or other electrical storage device, taking into account varying wind speed, with the goal of maximizing the total energy gener...
A Perspective-Based Convex Relaxation for Switched-Affine Optimal Control
Switched-affi ne systems optimal control mixed-integer convex programming
2015/7/8
We consider the switched-affine optimal control problem, i.e., the problem of selecting a sequence of affine dynamics from a finite set in order to minimize a sum of convex functions of the system sta...
Some New Perspectives on the Method of Control Variates
Some New Perspectives Method Control Variates
2015/7/8
The method of control variates is one of the most widely used variance reduction techniques associated with Monte Carlo simulation. This paper studies the method of control variates from several diffe...
On Convergence to Stationarity of Fractional Brownian Storage
Convergence to stationarity fractional Brownian motion storage process large deviations.
2015/7/6
With M(t):= sup0≤s≤t A(s)−s denoting the running maximum of a fractional Brownian motion A(·) with negative drift, this paper studies the rate of convergence of P(M(t)>x) to P(M>x). We define tw...