Adaptive neural network predictive control for nonlinear pure feedback systems with input delay

This paper presents an adaptive neural control design for nonlinear pure-feedback systems with an input time-delay. Novel state variables and the corresponding transform are introduced, such that the state-feedback control of a pure-feedback system can be viewed as the output-feedback control of a canonical system. An adaptive predictor incorporated with a high-order neural network (HONN) observer is proposed to obtain the future system states predictions, which are used in the control design to circumvent the input delay and nonlinearities. The proposed predictor, observer and controller are all online implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed. The conventional backstepping design and analysis for pure-feedback systems are avoided, which renders the developed scheme simpler in its synthesis and application. Practical guidelines on the control implementation and the parameter design are provided. Simulation on a continuous stirred tank reactor (CSTR) and practical experiments on a three-tank liquid level process control system are included to verify the reliability and effectiveness.

► Tracking control of nonlinear pure-feedback system with an input time-delay is studied without using backstepping control methodology.
► Novel state variables and coordinate transform are introduced, such that the state-feedback control of pure-feedback system can be viewed as the output-feedback control of canonical system.
► An adaptive predictor is proposed to account for the input delay and the unknown nonlinearities.
► The proposed schemes are implemented online without iterative predictive calculations so that the computational costs are reduced.