Stabilization of a single rotary inverted pendulum (SRIP) system is a benchmark nonlinear control problem. Inherent instability of SRIP in upright position and the presence of modelling uncertainty pose a challenge for control engineers. Sliding-mode control (SMC) is the most widely accepted robust control technique. Various versions of SMCs to stabilize SRIP have been reported in literature. Advantages of SMC come at the cost of chattering of input, which inhibits its usage in many practical applications. Several works have been done to reduce the chattering in SMC. Application of fuzzy logic in SMC to reduce chattering has been analysed extensively in the literature. Even though implementation of fuzzy SMC (FSMC) in theory/simulations completely eliminates chattering, practical implementation of FSMC does not reduce chattering to zero. In this paper, a filtered FSMC is proposed, which filters the high frequency component in the system input. Experimental results of SMC, FSMC and a fuzzy sliding-mode controller with filter (FSMCF) are given in this paper. Results indicate that filtering the output of FSMCF reduces the chattering effect in the system without affecting the closed loop dynamics.

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This paper shows two optimization methods that are built on a spider optimization algorithm to enhance the proportional integral and derivative (PID) gain values for multiple-input-multiple-output (MIMO) arrangement which is automated with SIMATIC PCS7 Distributed Control System (SDCS). The leading methodologies are the Spider Search Algorithm (SSA) and Social Spider Optimization (SSO) which is meant primarily for optimizing PID gain values. The SSA is based on foraging strategy of colonial spiders and SSO works on the combined plan of the male and female spiders that removes the episodes of local optimization and exploration elusion. Thus, SSA and SSO are contrived for the ideal fine-tuning of PID conditions in the benchmark MIMO procedure. The system performance is understood by minimizing the integral absolute error (IAE) and the integral square error (ISE) as its objective functions. The time-domain features are examined for the aforesaid methods and thereafter compared with the previous genetic algorithm (GA). The settling time is 60s for the proposed method which is lesser than the other techniques. For illustrating the implemented controller's strength, interference is manually presented in the real-time system. Findings indicate that the SSO surpasses output measures and performance indices beyond the presupposed SSA and GA intervals.