Results 1  10
of
11
Robust Constrained Model Predictive Control using Linear Matrix Inequalities
, 1996
"... The primary disadvantage of current design techniques for model predictive control (MPC) is their inability to deal explicitly with plant model uncertainty. In this paper, we present a new approach for robust MPC synthesis which allows explicit incorporation of the description of plant uncertainty i ..."
Abstract

Cited by 78 (4 self)
 Add to MetaCart
The primary disadvantage of current design techniques for model predictive control (MPC) is their inability to deal explicitly with plant model uncertainty. In this paper, we present a new approach for robust MPC synthesis which allows explicit incorporation of the description of plant uncertainty in the problem formulation. The uncertainty is expressed both in the time domain and the frequency domain. The goal is to design, at each time step, a statefeedback control law which minimizes a "worstcase" infinite horizon objective function, subject to constraints on the control input and plant output. Using standard techniques, the problem of minimizing an upper bound on the "worstcase" objective function, subject to input and output constraints, is reduced to a convex optimization involving linear matrix inequalities (LMIs). It is shown that the feasible receding horizon statefeedback control design robustly stabilizes the set of uncertain plants under consideration. Several extensions...
Receding Horizon Control of Nonlinear Systems: A Control . . .
, 2000
"... n Automatic Control, pages 898 907, 1990. J. Shamma and M. Athans. Guaranteed properties of gain scheduled control for linear parametervarying plants. Automatica, pages 559 564, 1991. J. Shamma and M. Athans. Gainscheduling: Potential hazards and possible remedies. IEEE Control Systems Magazine, ..."
Abstract

Cited by 41 (4 self)
 Add to MetaCart
n Automatic Control, pages 898 907, 1990. J. Shamma and M. Athans. Guaranteed properties of gain scheduled control for linear parametervarying plants. Automatica, pages 559 564, 1991. J. Shamma and M. Athans. Gainscheduling: Potential hazards and possible remedies. IEEE Control Systems Magazine, 12(3):101 107, June 1992. [Sch96] A. Schwartz. Theory and Implementation of Numerical Methods Based on RungeKutta Integration for Optimal Control Problems. PhD Disser tation, University of California, Berkeley, 1996. [SCH+00] M. Sznaier, J. Cloutier, R. Hull, D. Jacques, and C. Mracek. Reced ing horizon control lyapunov function approach to suboptimal regula tion of nonlinear systems. Journal of Guidance, Control, and Dynamics, 23(3):399 405, 2000. [SD90] M. Sznaier and M. J. Damborg. Heuristically enhanced feedback con trol of constrained discretetime linear systems. Automatica, 26:521 532, 1990. [SMR99] P. Scokaert, D. Mayne, and J. Rawlings. Suboptimal model predictive cont
Finite Receding Horizon Linear Quadratic Control: A Unifying Theory for Stability and Performance Analysis
 CDS Technical Memo #CITCDS
, 1997
"... We consider a finite horizon based formulation of receding horizon control for linear discretetime plants with quadratic costs. A framework is developed for analyzing stability and performance of finite receding horizon control for arbitrary terminal weights. Previous stability and performance resul ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
We consider a finite horizon based formulation of receding horizon control for linear discretetime plants with quadratic costs. A framework is developed for analyzing stability and performance of finite receding horizon control for arbitrary terminal weights. Previous stability and performance results, including end constraints, infinite horizon formulations, and the fake algebraic Riccati equation, are all shown to be special cases of the derived results. The unconstrained case is presented, where conditions for finite receding horizon control to be stabilizing and within specified bounds of the optimal infinite horizon performance can be computed from the solution to the Riccati difference equations. Nevertheless, the framework presented is general in that it lays the groundwork for extension to constrained systems. Keywords: predictive control, optimal control, linear systems. 1 Introduction Receding horizon control (RHC), also known as model predictive control (MPC), is a discret...
Robust Control Synthesis In The Time Domain
, 1995
"... This thesis investigates the synthesis of controllers for time varying systems in order to satisfy an induced 2norm closed loop performance bound. This performance criterion is a generalisation of the well known H1 norm criterion used in the frequency domain for analysis and synthesis of linear tim ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
This thesis investigates the synthesis of controllers for time varying systems in order to satisfy an induced 2norm closed loop performance bound. This performance criterion is a generalisation of the well known H1 norm criterion used in the frequency domain for analysis and synthesis of linear time invariant control systems. A number of different time varying system frameworks are considered, for which there are no frequency domain counterparts. One such class is that of aperiodic sampleddata systems, that is continuous time systems connected to a discrete controller via sampling and hold devices. Multiple generalized sampling and hold devices, which may be aperiodic and asynchronous, are permitted within the framework considered in this thesis. Using game theory, necessary and sufficient conditions are given for the existence of controllers satisfying a prespecified performance bound for such multirate sampleddata systems, and expressions for such a controller are given if one exis...
A New Approach to Stability Analysis for Constrained Finite Receding Horizon Control without End Constraints
, 1997
"... We present a new approach to the stability analysis of finite receding horizon control applied to constrained linear systems. By relating the final predicted state to the current state through a bound on the terminal cost, it is shown that knowledge of upper and lower bounds for the finite horizon c ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
We present a new approach to the stability analysis of finite receding horizon control applied to constrained linear systems. By relating the final predicted state to the current state through a bound on the terminal cost, it is shown that knowledge of upper and lower bounds for the finite horizon costs are sufficient to determine the stability of a receding horizon controller. This analysis is valid for receding horizon schemes with arbitrary positivedefinite terminal weights, and does not rely on the use of stabilizing constraints. The result is a computable test for stability, and two simple examples are used to illustrate its application. Keywords: predictive control, constrained systems, linear systems, discrete time. 1 Introduction Receding horizon control (RHC), also known as model predictive control (MPC) [8], is an online technique in which a new control action is computed at each time step by solving a finite horizon optimization problem that extends from the current time ...
IMPLEMENTATION AND FLIGHT TEST ASSESSMENT OF AN ADAPTIVE, RECONFIGURABLE FLIGHT CONTROL SYSTEM
"... During the summer of 1996 a series of ¯ight tests demonstrated a new indirectadaptive approach to recon®gurable ¯ight control known as the selfdesigning controller (SDC). The SDC achieves improved, appropriately decoupled responses during arbitrary effector or airframe impairment scenarios, and s ..."
Abstract
 Add to MetaCart
During the summer of 1996 a series of ¯ight tests demonstrated a new indirectadaptive approach to recon®gurable ¯ight control known as the selfdesigning controller (SDC). The SDC achieves improved, appropriately decoupled responses during arbitrary effector or airframe impairment scenarios, and successful SDC ¯ight tests culminated with smooth landing of the VISTA/F16 in crosswind conditions with a (simulated) missing primary control surface (left horizontal tail). The SDC couples modelfollowing recedinghorizon optimal control with an online parameter identi®cation (ID) algorithm designed to provide smooth, accurate estimates of possibly timevarying system parameters, even under conditions of low excitation. The adaptive modelfollowing approach is designed to reduce control law development costs and improve system performance in the presence of gradual or abrupt changes, including unforeseen events. This paper provides (1) a brief summary of the SDC algorithms, (2) a discussion of SDC implementation on the VISTA/F16 ¯ight control hardware, (3) a summary of ¯ight test results, and (4) suggestions for further research in recon®gurable/adaptive controls.
Constrained Finite Receding Horizon Linear Quadratic Control
, 1997
"... Issues of feasibility, stability and performance are considered for a finite horizon formulation of receding horizon control (RHC) for linear systems under mixed linear state and control constraints. It is shown that for a sufficiently long horizon, a receding horizon policy will remain feasible and ..."
Abstract
 Add to MetaCart
Issues of feasibility, stability and performance are considered for a finite horizon formulation of receding horizon control (RHC) for linear systems under mixed linear state and control constraints. It is shown that for a sufficiently long horizon, a receding horizon policy will remain feasible and result in stability, even when no end constraint is imposed. In addition, offline finite horizon calculations can be used to determine not only a stabilizing horizon length, but guaranteed performance bounds for the receding horizon policy. These calculations are demonstrated on two examples. Keywords: predictive control, optimal control, linear systems. 1 Introduction Receding horizon control (RHC), also known as model predictive control (MPC), is a discretetime technique in which the control action is obtained by repeatedly solving online open loop optimization problems at each time step. The flexibility of this type of implementation has been useful in addressing various implementatio...
Optimal Control for Biological Movement Systems
, 2006
"... quality and form for publication on microfilm: ..."
Iterative
, 2007
"... linearization methods for approximately optimal control and estimation of nonlinear stochastic system ..."
Abstract
 Add to MetaCart
linearization methods for approximately optimal control and estimation of nonlinear stochastic system