Abstract
This paper introduces a rotary double inverted pendulum (RDIP) systems. The model is derived by using Euler–Lagrange. Linear quadratic regulator (LQR) controller is applied as the main controller to stabilise the rotary double. However, LQR alone cannot control RDIP efficiently because the plant derived in linear model is not exact model of the real plant. Practically, controller design aiming to guarantee robustness has to consider these uncertainties. In this paper, neural network predictive control is proposed to improve control performance of the conventional LQR controller. Results on control techniques from computer simulation are evaluated and compared.
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