Global optimization of second-order sliding mode controller parameters using a new sliding surface: An experimental...

The chattering effect, robustness and stability are the major issues in a typical Sliding Mode Control (SMC). The selection of sliding manifold parameters are complicated, time-consuming and tedious. The parameter convergence is also a challenging work. Hence, the paper accords a global optimization of Second-Order Sliding Mode Controller (SOSMC) parameters with new sliding surface using the Jaya algorithm for non-linear uncertain process tank systems. The controller (SOSMC) role is to direct the sliding surface and it's first order time derivative to zero in a finite time. The Jaya algorithm uses 'optimal features' by updating the solutions in populations. The effectiveness of proposed strategy is evaluated for five objective functions and compared with Artificial Bee Colony (ABC) based SOSMC, classical SOSMC, typical SMC, Non-dominated Sorting Genetic Algorithm based First-order SMC (NSGA-II FOSMC) and Proportional+Integral+Derivative (PID) controller which is validated through real-time experimentation. The asymptotic stability is guaranteed through direct Lyapunov candidate function. The statistical significance of algorithms has been verified by using Friedman, Dunn, critical time efficiency and p-value tests. The Jaya algorithm shows a better performance over reported methods concerning process speed, settling time, rise time, reaching time, overshoot, response time and parameter convergence as explored from simulation, statistical, and experimental results. Furthermore, the elevated dead-time and oscillatory processes have been presented to show the efficacy of proposed strategy.