An application-oriented, optimization-based methodology for interactive design of engineering systems†

Motivated by a control system example, a new methodology is proposed for tackling optimal design of engineering systems. This methodology emphasizes designer's intuition and man-machine interaction. It includes a classification of specifications into various types and a scaling of specification space and parameter spaces based on the designer's knowledge of the particular application. An algorithm is proposed for solving the resulting constrained ‘minimax’ optimization problem and its convergence is proved. Finally, an application-oriented user front-end is presented. The methodology discussed in this paper has been implemented in the DELIGHT system and has been successfully used in various types of applications.

[1]  F. Clarke Generalized gradients and applications , 1975 .

[2]  M. Lightner,et al.  Multiple criterion optimization for the design of electronic circuits , 1981 .

[3]  A.L. Sangiovanni-Vincentelli,et al.  A survey of optimization techniques for integrated-circuit design , 1981, Proceedings of the IEEE.

[4]  T. Scott,et al.  An interactive linear programming approach to model parameter fitting and worst case circuit design , 1980 .

[5]  G. Hachtel The simplicial approximation approach to design centering , 1977 .

[6]  D. Mayne,et al.  An algorithm for optimization problems with functional inequality constraints , 1976 .

[7]  J. Dyer Interactive Goal Programming , 1972 .

[8]  V. Zakian,et al.  Design of dynamical and control systems by the method of inequalities , 1973 .

[9]  C. C. Gonzaga,et al.  An improved algorithm for optimization problems with functional inequality constraints , 1980 .

[10]  Lotfi A. Zadeh,et al.  Optimality and non-scalar-valued performance criteria , 1963 .

[11]  Charles A. Desoer,et al.  Controller design for linear multivariable feedback systems with stable plants, using optimization with inequality constraints† , 1983 .

[12]  A. Deczky Synthesis of recursive digital filters using the minimum p-error criterion , 1972 .

[13]  E. Polak,et al.  Delight. MIMO: An interactive, optimization-based multivariable control system design package , 1982, IEEE Control Systems Magazine.

[14]  Milan Zelany,et al.  A concept of compromise solutions and the method of the displaced ideal , 1974, Comput. Oper. Res..

[15]  David Q. Mayne,et al.  Combined phase I—phase II methods of feasible directions , 1979, Math. Program..

[16]  D. Agnew,et al.  Improved minimax optimization for circuit design , 1981 .

[17]  H. Parsa,et al.  Nonuniform, dynamically adapted discretization for functional constraints in engineering design problems , 1983, The 22nd IEEE Conference on Decision and Control.

[18]  G. Stein,et al.  Multivariable feedback design: Concepts for a classical/modern synthesis , 1981 .

[19]  E. Polak,et al.  An adaptive precision gradient method for optimal control. , 1973 .

[20]  Philip E. Gill,et al.  The Design and Structure of a Fortran Program Library for Optimization , 1979, TOMS.

[21]  W. Nye,et al.  The design of digital filters using interactive optimization , 1981, 1981 20th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[22]  Y. Haimes,et al.  Multiobjectives in water resource systems analysis: The Surrogate Worth Trade Off Method , 1974 .

[23]  Elijah Polak,et al.  Nondifferentiable optimization algorithm for designing control systems having singular value inequalities , 1982, Autom..

[24]  Robert K. Brayton,et al.  Sensitivity and optimization , 1980 .

[25]  Yacov Y. Haimes,et al.  Approach to performance and sensitivity multiobjective optimization: The goal attainment method , 1975 .

[26]  D. Mayne,et al.  On the Extension of Constrained Optimization Algorithms from Differentiable to Nondifferentiable Problems , 1983 .