Balance Control and Stability of a Two-Wheeled Robot Utilizing Adaptive LQR Controller based on Evolutionary Algorithms

This paper presents an intelligent control approach for stabilizing a two-wheeled self-balancing robot (TWSBR) by integrating a Linear Quadratic Regulator (LQR) with the Grey Wolf Optimizer (GWO) algorithm. The nonlinear dynamic model of the robot is first derived using the Lagrangian formulation and linearized around the upright equilibrium point. A conventional LQR controller is designed to provide baseline stabilization, however, its performance strongly depends on the manual selection of weighting matrices, which may not guarantee optimal behavior under all conditions. To overcome this limitation, the GWO algorithm is employed to adaptively tune the LQR feedback gains, minimizing a composite cost function that incorporates state deviations, control effort, and overshoot penalties. Simulations are conducted to compare the classical and GWO-optimized LQR controllers in terms of transient response, steady-state accuracy, and robustness. The results demonstrate that the proposed GWO-LQR controller achieves faster settling time, significantly reduced overshoot, smoother control actions, and enhanced stability compared to the conventional LQR scheme.

کلیدواژه ها:

Two-wheeled robot ، intelligent controller ، robot balance ، simulation ، system stability