Low order controller with regional pole placement

This paper focuses on the regional pole placement via static output feedback. Under proper state coordinate transformation with a free matrix variable, the static output feedback gain may be obtained by solving a linear matrix inequality (LMI). The LMI is feasible only if the poles of a dummy control system are in the given LMI region. The free matrix variable can regulate the dummy system as a state feedback gain matrix. So once the free variable is determined, the static output feedback gain matrix may be obtained by an LMI-based method. The main computations do not concern any reduction or enlargement of matrix inequalities. Numerical examples show the effectiveness of the proposed algorithm.

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Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm

The Scientific World JOURNAL ◽

10.1155/2014/978167 ◽

2014 ◽

Vol 2014 ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Andrej Sarjaš ◽

Rajko Svečko ◽

Amor Chowdhury

Keyword(s):

Genetic Algorithm ◽

Controller Design ◽

Optimization Procedure ◽

Pole Placement ◽

Objective Functions ◽

Motion Controller ◽

Multiobjective Genetic Algorithm ◽

And Performance ◽

Regional Pole Placement ◽

Arbitrary Selection

This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers’ structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers’ stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution.

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