Abstract
Rotary Inverted Pendulum (RIP) mimics the behavior of many practical control systems like crane mechanism, segway, unicycle robot, traction control in vehicles, rocket stabilization, and launching. RIP is a fourth-order nonlinear open-loop unstable dynamical system and is widely used for testing the effectiveness of the newly developed control algorithms. In this paper, a Hybrid Control Scheme (HCS) based on energy balance and fuzzy logic controllers is proposed to implement the swing up and stabilization control of RIP. In the proposed control scheme, the fuzzy logic-based state feedback gains are dynamically tuned in real-time by minimizing the absolute error between the desired and actual states to get robust control performance. The proposed HCS is also compared with the conventional Linear Quadratic Controller (LQR) for this application. The comparative results show that the proposed fuzzy logic-based hybrid control scheme gives the optimal control performance in terms of achieving satisfactory transient, steady-state, and robust responses from a given RIP system, as compared to the conventional LQR based control scheme. The proposed control scheme is also relatively less complex with a low computational cost and provides desired response characteristics as compared to the existing ones in the literature.
Highlights
A Rotary Inverted Pendulum (RIP) is a multivariable nonlinear control system typically of fourth order and is inherently an unstable system
The readings of angles u and a obtained from the optical encoders mounted on the RIP during the practical implementation are accessed in LabVIEW using the NI ELVIS II board
The experimental results obtained from the proposed Hybrid Control Scheme (HCS) using Energy balance controller (EBC) and Linear Quadratic Controller (LQR) controller are plotted in Figures 9 to 11
Summary
A Rotary Inverted Pendulum (RIP) is a multivariable nonlinear control system typically of fourth order and is inherently an unstable system. In the proposed control scheme, the fuzzy logic-based state feedback gains are dynamically tuned in real-time by minimizing the absolute error between the desired and actual states to get robust control performance.
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