Direct adaptive control for underactuated mechatronic systems using fuzzy systems and neural networks: A Pendubot case

This thesis describes the implementation of a vertical motion and position control scheme for a mechatronic system, specifically the Pendubot robot. The Pendubot is a non-linear, underactuated and unstable two-link planar robot arm that is frequently used as a benchmark in research studies involving nonlinear control theory and underactuated systems. Control of the Pendubot poses two challenging tasks: (i) to swing the two links from their stable hanging position to unstable vertical equilibrium positions, and (ii) to balance the links about the desired equilibrium positions. PD fuzzy controller is formulated and employed to meet challenges associated with swing-up control. Vertical balance control employs fuzzy systems and radial Gaussian neural networks. As such, an adaptive neural network and fuzzy controller is further analyzed, where the balance stability depends on a controller weight that is determined using Lyapunov theory. This approach is proven to be globally stable, with errors converging to a neighbourhood of zero. Then, the proposed swing-up and the balancing controllers are coupled together to achieve the motion objective in a stable manner, while resisting the external disturbances. The simulation results show that both the swing-up and balancing control schemes can be realized using 25 and 5 If-Then-rules, respectively. The simulation results confirm the results attained from the theoretical analysis.