Model Reference Adaptive Inverse Control of Nonlinear Systems: A Deep Learning Approach

Abstract In this work, deep learning (DL) is incorporated in the form of deep neural networks (DNN) into the design of a class of neuroadaptive control systems, namely model reference adaptive inverse control (MRAIC) systems. As inverse control is essentially feedforward control, it is much simpler to design and analyze than most current control methods especially when considering the control of nonlinear plants. Using the filtered-ε adaptation method with deep adaptive controller, it is demonstrated that the nonlinear plant output tracks the reference model output in the all-deep MRAIC system much more efficiently than the MRAIC system with a shallow adaptive controller. First- and second-order reference models have been experimented with. The proposed deep MRAIC system is also robust to plant parameter change.