Computational intelligence PC tools
Computational intelligence PC tools
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Decoupled fuzzy controller design with single-input fuzzy logic
Fuzzy Sets and Systems - Control and applications
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Decoupled control using neural network-based sliding-mode controller for nonlinear systems
Expert Systems with Applications: An International Journal
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Decoupled sliding-mode with fuzzy-neural network controller for nonlinear systems
International Journal of Approximate Reasoning
Dynamic question generation system for web-based testing using particle swarm optimization
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multi-objective pole placement with evolutionary algorithms
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Decoupled sliding-mode controller based on time-varying sliding surfaces for fourth-order systems
Expert Systems with Applications: An International Journal
An improved vector particle swarm optimization for constrained optimization problems
Information Sciences: an International Journal
Journal of Network and Computer Applications
Information Sciences: an International Journal
Cellular particle swarm optimization
Information Sciences: an International Journal
Enhancing particle swarm optimization using generalized opposition-based learning
Information Sciences: an International Journal
Self-adaptive learning based particle swarm optimization
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
Example-based learning particle swarm optimization for continuous optimization
Information Sciences: an International Journal
Integration of particle swarm optimization and genetic algorithm for dynamic clustering
Information Sciences: an International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
An ART-based fuzzy adaptive learning control network
IEEE Transactions on Fuzzy Systems
Decoupled fuzzy sliding-mode control
IEEE Transactions on Fuzzy Systems
Mathematical and Computer Modelling: An International Journal
A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
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Regulation and tracking of system states to the desired points or trajectories are two common tasks in the field of control engineering. For optimum performance of a controller, the appropriate selection of its parameters is of utmost importance. Furthermore, when the initial conditions of the system change, the controller with the previous parameters would be not optimum in the new conditions. To overcome these obstacles, in this paper, an online optimal Decoupled Sliding Mode Control (DSMC) approach is introduced. Firstly, to determine the optimum parameters of DSMC, an improved Particle Swarm Optimization (PSO) algorithm is applied. Next, to adapt the optimal controller to any initial condition, the Moving Least Squares (MLS) approximation is utilized. Finally, the proposed online optimal DSMC is successfully applied to a ball and beam system. The comparative studies are provided to verify the effectiveness of the proposed control scheme.