The inherited instabilities in the Inverted Pendulum (IP) system make it one of the most difficult nonlinear problems in the control theory. In this research work, Proportional –Integral and Derivative (PID) Controller with a feed forward gain is used with Reduced Linear Quadratic Regulator (RLQR) for stabilizing the Cart Position and Swinging-up the Pendulum angle. Tuning the Controllers' gains is achieved by using Particle Swarm Optimization (PSO) Technique. Obtaining the combined PID controllers' gains with a feed forward gain and RLQR is a multi-dimensions control problem. The Proposed Controllers give minimum Settling Time, Rise Time, Undershoot and Over shoot for both the Cart Position and the Pendulum angle. A disturbance with different amplitudes is applied to the system, and the results showed the robustness of the systems based on the tuned controllers. The overall results are promising.
The inverted pendulum system (IPS) is considered the milestone of many robotic-based industries. In this paper, a new variant of variable structure adaptive fuzzy (VSAF) is used with new reduced linear quadratic regulator (RLQR) and feedforward gain for enhancing the stability of IPS. The optimal determining of VSAF parameters as well as Q and R matrices of RLQR are obtained by using a modified grey wolf optimizer with adaptive constants property via particle swarm optimization technique (GWO/PSO-AC). A comparison between the hybrid GWO/PSO-AC and classical GWO/PSO based on multi-objective function is provided to justify the superiority of the proposed technique. The IPS equipped with the hybrid GWO/PSO-AC-based controllers has minimum settling time, rise time, undershoot, and overshoot results for the two system outputs (cart position and pendulum angle). The system is subjected to robustness tests to ensure that the system can cope with small as well as significant disturbances.
Inverted Pendulum system is one of the most exciting problems in control theory. In this research work, a new variant of Grey Wolf optimizer (GWO) via Particle Swarm Optimization (PSO) based on Adaptive Constants (AC) is proposed. The proposed technique (GWO/PSO-AC) is tested via twenty-three benchmark functions and compared to GWO based on PSO without adaptive constants (GWO/PSO). The suggested technique shows superiority in determining the optimal solutions for the well-established benchmark test functions with high computing performance compared to alternative techniques. The proposed GWO/PSO-AC technique, is employed to tune the parameters of the Variable Structure Adaptive Fuzzy (VSAF) controller in addition to the Reduced Linear Quadratic Regulator (RLQR) suggested by the authors. Both controllers are used to stabilize the cart position and to swing up the pendulum angle. The RLQR has an advantage over regular LQR, which is, the numberof the required parameters to obtain the required LQR gains is reduced. The proposed technique is compared with two optimization techniques. The proposed technique achieves high performance for both the cart position and the pendulum angle. The attained results are very promising.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.