Publication

Analysis of Cascade Structure with Predictive Control and Feedback Linearization

Dominique Bonvin, Philippe Müllhaupt, Kahina Guemghar
2005
Journal Articles

Abstract

Predictive control and feedback linearization are two popular approaches for the control of nonlinear systems. In this paper, a cascade combination of the two methods is proposed, where control based on input- output feedback linearization forms the inner lo op, and predictive control the outer lo op. With this scheme, predictive control is applied to the internal dynamics instead of the system dynamics. The proposed cascade scheme is advantageous for unstable minimum-phase systems and for unstable nonminimum-phase systems with slower internal dynamics compared to the system dynamics. A stability analysis of the global scheme is provided based on singular perturbation theory. The approach is illustrated via the simulation of an inverted pendulum on a cart system.

Official source

https://infoscience.epfl.ch/entities/publication/d6eccd37-66e3-4f88-81ea-8451e2c1214c

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Dominique Bonvin, Philippe Müllhaupt, Kahina Guemghar
2005
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/d6eccd37-66e3-4f88-81ea-8451e2c1214c

About this result

Related concepts (23)

Minimum phase

In control theory and signal processing, a linear, time-invariant system is said to be minimum-phase if the system and its inverse are causal and stable. The most general causal LTI transfer function can be uniquely factored into a series of an all-pass and a minimum phase system. The system function is then the product of the two parts, and in the time domain the response of the system is the convolution of the two part responses.

Feedback

Feedback occurs when outputs of a system are routed back as inputs as part of a chain of cause-and-effect that forms a circuit or loop. The system can then be said to feed back into itself. The notion of cause-and-effect has to be handled carefully when applied to feedback systems: Simple causal reasoning about a feedback system is difficult because the first system influences the second and second system influences the first, leading to a circular argument.

Model predictive control

Model predictive control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. In recent years it has also been used in power system balancing models and in power electronics. Model predictive controllers rely on dynamic models of the process, most often linear empirical models obtained by system identification.

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