Control of dead-time systems using derivative free local search guided population based incremental learning algorithms. (English) Zbl 1364.93276
Summary: This paper introduces two improved forms of population based incremental learning (PBIL) algorithm applied to proportional integral derivative (PID) controller and Smith predictor design. Derivative free optimization methods, namely simplex derivative pattern search (SDPS) and implicit filtering (IMF) are used to intensify search mechanism in PBIL algorithm with improved convergence than that of the original PBIL. Although the idea of combining local methods and global methods is not new, this paper focuses application of hybrid heuristics to the vast field of control design especially, control of systems having dead-time. The effectiveness of the controller schemes arrived using the developed algorithms namely simplex derivative pattern search guided population based incremental learning (SDPS-PBIL) and implicit filtering guided population based incremental learning (IMF-PBIL) are demonstrated using unit step set point response for a class of dead-time systems. The results are compared with some existing methods of controller tuning.
MSC:
| 93B51 | Design techniques (robust design, computer-aided design, etc.) |
| 90C56 | Derivative-free methods and methods using generalized derivatives |
| 93C80 | Frequency-response methods in control theory |
Keywords:
population based incremental learning (PBIL); dead-time; proportional integral derivative (PID); simplex derivative pattern search (SDPS); implicit filtering (IMF)References:
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