This paper presents a new discrete-time sliding-mode control design for multiple-input multi-output (MIMO) systems with tuning parameters by particle swarm optimization (PSO). PSO is a kind of evolutionary algorithm based on a population of individuals and motivated by the simulation of social behavior instead of the survival of the fittest individual. Several control algorithms are presented, two decoupling design and six new approaches of the coupling design of sliding-mode control without the necessity of calculate the process interactor matrix. SMC needs a design tool for parameter configuration and efficient practically to deal with multivariable processes. Simulations are carried out using both decoupling and coupling discrete-time SMC designs. Results shown that the new proposed approach for designing the discrete-time coupling SMC is a powerful tool and it performs better than the decoupling design, usually utilized in MIMO process. The simulations are assessed on a robotic manipulator of two degree-of-freedom (2-DOF), that constitute a MIMO nonlinear coupling dynamic system, with treatment of payload mass and link length variations. Simulation results show that the application of this control strategy effectively improve the trajectory tracking precision of position and velocity variables.
Fuzzy Sliding Mode Control Design and Particle Swarm Optimization Based PSS for Multimachine Power System
