Output-feedback control design for switched nonlinear systems: Adaptive neural backstepping approach

Decentralized Filtering Adaptive Neural Network Control for Uncertain Switched Interconnected Nonlinear Systems

This article presents a novel decentralized filtering adaptive neural network control framework for uncertain switched interconnected nonlinear systems. Each subsystem has its own decentralized controller based on the established decentralized state predictor. For each subsystem, the nonlinear uncertainties are approximated by a Gaussian radial basis function (GRBF) neural network incorporated with a piecewise constant adaptive law, where the adaptive law will update adaptive parameters from the error dynamics between the host system and the decentralized state predictor by discarding the unknowns, whereas a decentralized filtering control law is derived to cancel both local and mismatched uncertainties from other subsystems, as well as achieve the local objective tracking of the host system. The achievement of global objective depends on the achievement of local objective for each subsystem. The matched uncertainties are canceled directly by adopting their opposite in the control signal, whereas a dynamic inversion of the system is required to eliminate the effect of the mismatched uncertainties on the output. By exploiting the average dwell time principle, the error bounds between the real system and the virtual reference system, which defines the best performance that can be achieved by the closed-loop system, are derived. A numerical example is given to illustrate the effectiveness of the decentralized filtering adaptive neural network control architecture by comparing against the model reference adaptive control (MRAC).

Sensorless speed control of SPIM using BS_PCH novel control structure and NNSM_SC MRAS speed observer

This paper propose a novel Port Controlled Hamiltonian_Backstepping (PCH_BS) control structure with online tuned parameters, in combination with the modified Stator Current Model Reference Adaptive Syatem (SC_MRAS) based on speed and flux estimator using Neural Networks(NN) and sliding mode (SM) for sensorless vector control of the six phase induction motor (SPIM). The control design is based on combination PCH and BS techniques to improve its performance and robustness. The combination of BS_PCH controller with speed estimator can compensate for the uncertainties caused by the machine parameter variations, measurement errors, and external load disturbances, enables very good static and dynamic performance of the sensorless drive system (perfect tuning of the speed reference values, fast response of the motor current and torque, high accuracy of speed regulation) in a wide speed range, and robust for the disturbances of the load, the speed variation and low speed. The proposed sensorless speed control scheme is validated through Matlab-Simulink. The simulation results verify the effectiveness of the proposed control and observer.