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A nonlinear and robust adaptive backstepping based maximum torque per ampere speed sensorless control scheme with fully uncertain parameters is proposed for a permanent magnet-assisted synchronous reluctance motor. In the design of the controller, the relation to --axis currents constrained by maximum torque per ampere control is firstly derived. Then, a fully adaptive backstepping control method is employed to design control scenario and the stability of the proposed control scenario is proven through a proper Lyapunov function candidate. The derived controller guarantees tracking the reference signals of change asymptotically and has good robustness against the uncertainties of motor parameters and the perturbation of load torque. Moreover, in allusion to the strong nonlinearity of permanent magnet-assisted synchronous reluctance motor, an active flux based improved reduced-order Luenberger speed observer is presented to estimate the speed. Digital simulations testify the feasibility and applicability of the presented control scheme.

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Wind power curtailment is of great importance with the increase of large-scale wind power connected to the grid. A new concept of redundant wind power accommodated by dispatching electric water heaters (EWHs) is developed in the paper. Precise predictions of wind power and EWHs load power are the basis for this work. A hybrid multi-kernel prediction approach integrating an adaptive fruit fly optimization algorithm (AFOA) and multi-kernel relevance vector machine (MKRVM) is proposed to deal with the sample distribution of multisource heterogeneous features uncovered by an energy entropy method, where AFOA is used to determine the kernel parameters in MKRVM adaptively and avoid the arbitrariness. For the large computation of the prediction approach, parallel computation based on the Hadoop cluster is used to accelerate the calculation. Then, an economic dispatching model for accommodating wind power is built taking into account the penalty of curtailed wind power and the operating cost of EWHs. The proposed scheme is implemented in an intelligent residential district. The results show that the optimization performance of the hybrid prediction approach is superior to those of four usual optimization algorithms in this case. Regular or orderly scheduling of EWHs enables accommodation of superfluous wind power and reduces dispatch cost.

Combined with two approaches of sliding mode control and backstepping control, an adaptive sliding mode-based backstepping control scenario on the basis of nonlinear disturbance observer is proposed to complete maximum torque per ampere control for permanent magnet-assisted synchronous reluctance motor. The constraint relation of permanent magnet-assisted synchronous reluctance motor under maximum torque per ampere control is built, and the design of the controller is elaborated in detail. The uncertainties of modeling errors considering unmatched items are estimated through a presented nonlinear disturbance observer. The adaptive law reflecting the modeling error of the system is constructed. Globally asymptotic stability and convergence of the tracking error for the system are validated through Lyapunov stability criterion. Simulation and experimental results illustrate that external disturbances and uncertainties are observed correctly by nonlinear disturbance observer; the close-loop system controlled by the proposed controller can track the references rapidly and precisely, and the designed controller has a good robust ability.