Advanced control systems are required to maintain bicycle stability due to its unstable open-loop behaviour. This work is aimed at designing an optimal state feedback control system for bicycle stabilization. The performance index of the optimal control system is minimi More
Advanced control systems are required to maintain bicycle stability due to its unstable open-loop behaviour. This work is aimed at designing an optimal state feedback control system for bicycle stabilization. The performance index of the optimal control system is minimized using the newly developed adaptive particularly tunable fuzzy particle swarm optimization algorithm. The states of the system are estimated using a state observer. The obtained results are compared with those of the linear–quadratic regulator (LQR). The main advantage of the developed control system is that, unlike the LQR controller that is limited to linear systems, it can be extended to nonlinear control systems.
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