Fast Model Predictive Control Using Online Optimization
Top Cited Papers
- 30 June 2009
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Control Systems Technology
- Vol. 18 (2), 267-278
- https://doi.org/10.1109/tcst.2009.2017934
Abstract
A widely recognized shortcoming of model predictive control (MPC) is that it can usually only be used in applications with slow dynamics, where the sample time is measured in seconds or minutes. A well-known technique for implementing fast MPC is to compute the entire control law offline, in which case the online controller can be implemented as a lookup table. This method works well for systems with small state and input dimensions (say, no more than five), few constraints, and short time horizons. In this paper, we describe a collection of methods for improving the speed of MPC, using online optimization. These custom methods, which exploit the particular structure of the MPC problem, can compute the control action on the order of 100 times faster than a method that uses a generic optimizer. As an example, our method computes the control actions for a problem with 12 states, 3 controls, and horizon of 30 time steps (which entails solving a quadratic program with 450 variables and 1284 constraints) in around 5 ms, allowing MPC to be carried out at 200 Hz.Keywords
This publication has 42 references indexed in Scilit:
- Dynamic hedging of basket options under proportional transaction costs using receding horizon controlInternational Journal of Control, 2009
- An online active set strategy to overcome the limitations of explicit MPCInternational Journal of Robust and Nonlinear Control, 2007
- Fast implementations and rigorous models: Can both be accommodated in NMPC?International Journal of Robust and Nonlinear Control, 2007
- Efficient robust optimization for robust control with constraintsMathematical Programming, 2007
- Suboptimal control of constrained nonlinear systems via receding horizon constrained control Lyapunov functionsInternational Journal of Robust and Nonlinear Control, 2003
- A survey of industrial model predictive control technologyControl Engineering Practice, 2002
- A primal-dual interior-point method for robust optimal control of linear discrete-time systemsIEEE Transactions on Automatic Control, 2000
- SDPT3 — A Matlab software package for semidefinite programming, Version 1.3Optimization Methods and Software, 1999
- Receding horizon control of nonlinear systemsIEEE Transactions on Automatic Control, 1990
- Internal Model Control: extension to nonlinear systemIndustrial & Engineering Chemistry Process Design and Development, 1986