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In modern industrial engineering such as space robots, assembly systems and transportation vehicles, a Gear Bearing Drive (GBD) transmission, with the potential to produce up to 5000:1 torque ratio in a compact size, can be of great interest. However, in a practical actuation system consisting of actuator, GBD and workload, ever-present nonlinearities such as disturbances and perturbations can increase the complexity of the modeling process as well as difficulty of the control design. To reduce such complexities, a simplified model of the actuation plant is developed by linearizing all nonlinearities and numerically estimating the disturbances and perturbations inside the plant. For this, a robust-adaptive control platform, especially designed to deal with system uncertainties and unmodeled dynamics, is selected, analyzed and implemented in real-time. In addition, due to the initially unknown parameters, the adaptive part of the controller must adapt to the system parameters variations and changes. Finally, to guarantee asymptotic convergence, a projection operator is developed and combined with the adaptation law. Furthermore, an optimal combination of proportional-integral-derivative (PID) and robust-adaptive sliding mode control (RASMC) is selected and a novel controller is developed and experimentally implemented and verified. The experimental results of system response are compared to the simulation results to assess control performance and response characteristics. This research suggests that RASMC can ensure its effectiveness in a nonlinear plant with uncertain parameters and unmodeled dynamics and produce a satisfactory experimental performance with minimum settling time and overshoot. Future work is required to explain unpredictable piecewise behavior of the open-loop speed response and other ignored modeling aspects and nonlinearities.

robust-adaptive control strategy for gear bearing drive systems