Multiple-criterion control: a convex programming approach
Automatica (Journal of IFAC)
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
An Accelerated Genetic Algorithm
Applied Intelligence
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Genetic algorithms optimization for normalized normal constraint method under Pareto construction
Advances in Engineering Software
Evolutionary algorithms based design of multivariable PID controller
Expert Systems with Applications: An International Journal
Multiobjective evolution based fuzzy PI controller design for nonlinear systems
Engineering Applications of Artificial Intelligence
UAV navigation by an expert system for contaminant mapping with a genetic algorithm
Expert Systems with Applications: An International Journal
Solution of nonconvex and nonsmooth economic dispatch by a new Adaptive Real Coded Genetic Algorithm
Expert Systems with Applications: An International Journal
Covariance matrix adaptation evolution strategy based design of centralized PID controller
Expert Systems with Applications: An International Journal
An incremental genetic algorithm for classification and sensitivity analysis of its parameters
Expert Systems with Applications: An International Journal
FMS scheduling with knowledge based genetic algorithm approach
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Adaptive differential dynamic programming for multiobjective optimal control
Automatica (Journal of IFAC)
Performance analysis of fractional order fuzzy PID controllers applied to a robotic manipulator
Expert Systems with Applications: An International Journal
A Modified micro Genetic Algorithm for undertaking Multi-Objective Optimization Problems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
Hi-index | 12.05 |
Most controllers optimization and design problems are multiobjective in nature, since they normally have several (possibly conflicting) objectives that must be satisfied at the same time. Instead of aiming at finding a single solution, the multiobjective optimization methods try to produce a set of good trade-off solutions from which the decision maker may select one. Several methods have been devised for solving multiobjective optimization problems in control systems field. Traditionally, classical optimization algorithms based on nonlinear programming or optimal control theories are applied to obtain the solution of such problems. The presence of multiple objectives in a problem usually gives rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Recently, Multiobjective Evolutionary Algorithms (MOEAs) have been applied to control systems problems. Compared with mathematical programming, MOEAs are very suitable to solve multiobjective optimization problems, because they deal simultaneously with a set of solutions and find a number of Pareto optimal solutions in a single run of algorithm. Starting from a set of initial solutions, MOEAs use iteratively improving optimization techniques to find the optimal solutions. In every iterative progress, MOEAs favor population-based Pareto dominance as a measure of fitness. In the MOEAs context, the Non-dominated Sorting Genetic Algorithm (NSGA-II) has been successfully applied to solving many multiobjective problems. This paper presents the design and the tuning of two PID (Proportional-Integral-Derivative) controllers through the NSGA-II approach. Simulation numerical results of multivariable PID control and convergence of the NSGA-II is presented and discussed with application in a robotic manipulator of two-degree-of-freedom. The proposed optimization method based on NSGA-II offers an effective way to implement simple but robust solutions providing a good reference tracking performance in closed loop.