Fuzzy autopilot design using a multiobjective evolutionary algorithm

This paper details a Fuzzy-Feedback Linearisation controller applied to a non-linear missile. The design uses an evolutionary algorithm optimisation approach to a multiple model description of the airframe aerodynamics. A set of convex models is produced that map the vertex points in a high order parameter space (of the order of 16 variables). These are used to determine the membership function distribution within the outer loop control system by using a multi-objective evolutionary algorithm. This produces a design that meets objectives related to closed loop performance such as: steady state error, overshoot, settling and rising time. The evolutionary algorithm uses non-dominated sorting for forming a Pareto front of possible solutions. This paper shows that fuzzy controllers can be produced for engineering problems, with the multiobjective algorithm allowing the designer the freedom to choose solutions and investigate the properties of the system.

[1]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[2]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[3]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[4]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[5]  Heinz Roland Weistroffer.  A combined over- and under-achievement programming approach to multiple objectives decision-making , 1984 .

[6]  B. A. White,et al.  Design of robust fuzzy controllers for aerospace applications , 1999, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397).

[7]  Subhash Narula,et al.  The Current State of Nonlinear Multiple Criteria Decision Making , 1991 .

[8]  B. A. White,et al.  Robust output tracking for MIMO nonlinear systems: an adaptive fuzzy systems approach , 1997 .

[9]  Peter J. Fleming,et al.  Genetic Algorithms in Engineering Systems , 1997 .

[10]  P. W. J. Ledington,et al.  Short report: Creating an information management perspective: The experience at the Royal Military College of Science , 1986 .

[11]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. II. Application example , 1998, IEEE Trans. Syst. Man Cybern. Part A.