Narinder Singh Bhangal

Bio: Narinder Singh Bhangal is an academic researcher from Dr. B. R. Ambedkar National Institute of Technology Jalandhar. The author has contributed to research in topics: Double inverted pendulum & Pendulum. The author has an hindex of 1, co-authored 1 publications receiving 13 citations.

Design and Performance of LQR and LQR based Fuzzy Controller for Double Inverted Pendulum System

Abstract:  Abstract—The objective of this paper is to compare performance between two type of controller for a double inverted pendulum system. Double inverted pendulum is a non-linear, unstable and fast reaction system. DIP is stable when its two pendulums allocated in vertically position and have no oscillation and movement and also inserting force should be zero. The objective is to determine the control strategy that to delivers better performance with respect to pendulum angle’s and cart position. In this paper LQR based fuzzy controller is designed and its performance is compared with Linear Quadratic Regulator controller using Matlab and Simulink. The results shows that LQR based fuzzy controller produced better response as compared to LQR control strategy. 

LQR, double-PID and pole placement stabilization and tracking control of single link inverted pendulum

Abstract: This paper presents the dynamic behaviour of a nonlinear single link inverted pendulum-on-cart system based on Lagrange Equation The nonlinear model linearization was presented based on Taylor series approximation LQR, double-PID and simple pole placement control techniques were proposed for upright stabilization and tracking controls of the system Simulations results for the various control techniques subjected to a unity magnitude pulse input torque with and without disturbance were compared The performances of the proposed controllers were investigated based on response time specifications and level of disturbance rejection Thus, the performance of LQR is more reliable and satisfactory Finally, future work suggestions were made

PSO-based state feedback regulator for stabilizing a two-wheeled wheelchair in balancing mode

Abstract: In this paper, the state feedback control design problem for stabilizing a two-wheeled wheelchair in balancing mode is considered. The system is modeled as double-inverted pendulum on two wheels. The calculation of state feedback control gains is conventionally handled by LQR method via Riccati equation. Unfortunately, the method still possesses trial and error approach when choosing some parameters, in particular tuning the elements of Q and R weighting matrices. Therefore, an intelligent numerical method to resolve this problem is proposed by adopting PSO algorithm. The simulation work is carried out to evaluate the effectiveness of the proposed method. The result shows that the numerical method reduces tuning time and improves the performance of the system.

Two-wheeled wheelchair stabilization control using fuzzy logic controller based particle swarm optimization

Abstract: Designing a control strategy of two-wheeled wheelchair is a very challenging task due to the unstable and highly nonlinear system. In the paper the system is modeled by mimicking a double-link inverted pendulum concept and the mathematical equations is derived using Euler-Lagrange method. Then the state-space representation is applied to the Simulink block diagram in Matlab. The control parameter of the system is compared between trial-and-error method and Particle Swarm Optimization (PSO) algorithm. This strategy is to find the optimal value for the system to get better performance. The system will be simulated using Fuzzy Logic Control (FLC) and FLC-PSO using Matlab/Simulink environment. Simulation results show that the FLC-PSO is better than FLC in terms of overshoot and settling time.

Velocity control of a two-wheeled inverted pendulum mobile robot: a fuzzy model-based approach

Abstract: This paper presents the design of a fuzzy tracking controller for balancing and velocity control of a Two-Wheeled Inverted Pendulum (TWIP) mobile robot based on its Takagi-Sugino (T-S) fuzzy model, fuzzy Lyapunov function and non-parallel distributed compensation (non-PDC) control law. The T-S fuzzy model of the TWIP mobile robot was developed from its nonlinear dynamical equations of motion. Stabilization conditions in a form of linear matrix inequalities (LMIs) were derived based on the T-S fuzzy model of the TWIP mobile robot, a fuzzy Lyapunov function and a non-PDC control law. Based on the derived stabilization conditions and the T-S fuzzy model of the TWIP mobile robot, a state feedback velocity tracking controller was then proposed for the TWIP mobile robot. The balancing and velocity tracking performance of the proposed controller was investigated via simulations. The simulation result shows the effectiveness of the proposed control scheme.