Cooling schedules for optimal annealing
Mathematics of Operations Research
A learning process for fuzzy control rules using genetic algorithms
Fuzzy Sets and Systems
Fleet Scheduling Optimization: A Simulated Annealing Approach
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 05
Genetic fuzzy logic controller: an iterative evolution algorithm with new encoding method
Fuzzy Sets and Systems
Adaptive control of a class of nonlinear systems with fuzzy logic
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Genetic Algorithms and Very Fast Simulated Reannealing: A comparison
Mathematical and Computer Modelling: An International Journal
Simulated annealing: Practice versus theory
Mathematical and Computer Modelling: An International Journal
Evolutionary optimization-based tuning of low-cost fuzzy controllers for servo systems
Knowledge-Based Systems
Robust observer-based output feedback control for fuzzy descriptor systems
Expert Systems with Applications: An International Journal
Novel Adaptive Charged System Search algorithm for optimal tuning of fuzzy controllers
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Nonlinearities present in the systems make their controller design a non-trivial task. The difficulty further increases in case of multi-input-multi-output (MIMO) systems with increased number of variables and interactions between them. In this paper, fuzzy based intelligent control schemes are designed for control of nonlinear single-input-single-output (SISO) and MIMO systems. The comparative study of the designed self tuning fuzzy controller with a standard Takagi-Sugeno (TS) fuzzy controller is discussed with application to a shell and tube heat exchanger (nonlinear SISO system) and a coupled two tank system (nonlinear MIMO system). Online tuning of the membership functions and control rules of fuzzy controller is carried out using simulated annealing (SA) to obtain improved performance by minimizing the error function. Experimental results demonstrate the effectiveness of the control scheme.