Jerry Sweafford Jr. and Farbod Fahimi
Data-driven system identification, discrete sliding mode control, neu-ral networks, nonlinear model-based control, robotic manipulators,trajectory tracking
State-space model-based controllers are generally preferred over neu- ral network model-based controllers, in part because many neural network model-based controllers lack proof of stability. However, the physics-based modelling needed for the derivation of state-space models, and the identification and verification of the model pa- rameters are challenging tasks that are easier when neural network models are used. A model-based state-space controller with neural network modelling would combine the existence of proof of perfor- mance with a less difficult modelling task. We present a simple method of system identification and control based on a discrete, nonlinear model of the plant, in which a neural network state predictor is used to estimate the terms of the state-space model for a discrete sliding mode controller. The proposed approach was demonstrated and validated by effective trajectory tracking in a simulated two-degree-of-freedom (DOF) robotic arm and the actual robotic arm on which the simulation was based.
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