A novel intelligent multiobjective simulated annealing algorithm for designing robust PID controllers

Ming Hao Hung*, Li Sun Shu, Shinn Jang Ho, Shiow Fen Hwang, Shinn-Ying Ho

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


This paper proposes an intelligent multiobjective simulated annealing algorithm (IMOSA) and its application to an optimal proportional integral derivative (PID) controller design problem. A well-designed PID-type controller should satisfy the following objectives: 1) disturbance attenuation; 2) robust stability; and 3) accurate setpoint tracking. The optimal PID controller design problem is a large-scale multiobjective optimization problem characterized by the following: 1) nonlinear multimodal search space; 2) large-scale search space; 3) three tight constraints; 4) multiple objectives; and 5) expensive objective function evaluations. In contrast to existing multiobjective algorithms of simulated annealing, the high performance in IMOSA arises mainly from a novel multiobjective generation mechanism using a Pareto-based scoring function without using heuristics. The multiobjective generation mechanism operates on a high-score nondominated solution using a systematic reasoning method based on an orthogonal experimental design, which exploits its neighborhood to economically generate a set of well-distributed nondominated solutions by considering individual and overall objectives. IMOSA is evaluated by using a practical design example of a super-maneuverable fighter aircraft system. An efficient existing multiobjective algorithm, the improved strength Pareto evolutionary algorithm, is also applied to the same example for comparison. Simulation results demonstrate high performance of the IMOSA-based method in designing robust PID controllers.

Original languageEnglish
Pages (from-to)319-330
Number of pages12
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
Issue number2
StatePublished - 1 Mar 2008


  • Evolutionary computation
  • Genetic algorithm (GA)
  • Multiobjective optimization
  • Pareto solution
  • Proportional integral derivative (PID) controller
  • Simulated annealing

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