In this paper, we propose a data-driven supplementary control approach with adaptive learning capability for air-breathing hypersonic vehicle tracking control based on action-dependent heuristic dynamic programming (ADHDP). The control action is generated by the combination of sliding mode control (SMC) and the ADHDP controller to track the desired velocity and the desired altitude. In particular, the ADHDP controller observes the differences between the actual velocity/altitude and the desired velocity/altitude, and then provides a supplementary control action accordingly. The ADHDP controller does not rely on the accurate mathematical model function and is data driven. Meanwhile, it is capable to adjust its parameters online over time under various working conditions, which is very suitable for hypersonic vehicle system with parameter uncertainties and disturbances. We verify the adaptive supplementary control approach versus the traditional SMC in the cruising flight, and provide three simulation studies to illustrate the improved performance with the proposed approach.
In this paper, based on the adaptive critic learning technique, the control for a class of unknown nonlinear dynamic systems is investigated by adopting a mixed data and event driven design approach. The nonlinear control problem is formulated as a two-player zero-sum differential game and the adaptive critic method is employed to cope with the data-based optimization. The novelty lies in that the data driven learning identifier is combined with the event driven design formulation, in order to develop the adaptive critic controller, thereby accomplishing the nonlinear control. The event driven optimal control law and the time driven worst case disturbance law are approximated by constructing and tuning a critic neural network. Applying the event driven feedback control, the closed-loop system is built with stability analysis. Simulation studies are conducted to verify the theoretical results and illustrate the control performance. It is significant to observe that the present research provides a new avenue of integrating data-based control and event-triggering mechanism into establishing advanced adaptive critic systems.
The conventional rotor flux estimation method has issues of dc offset and harmonics, which are caused by initial rotor flux, detection errors, etc. To eliminate these defects, one improved nonlinear flux observer is proposed for sensorless control of permanent magnet synchronous machine (PMSM). Firstly, the rotor position estimation method based on PMSM rotor flux observation is studied. Meanwhile, the limitations of the traditional rotor flux estimators, i.e., the saturation of pure integrator, phase shift and amplitude attenuation of low-pass filter are analyzed. Then, two novel flux observers, second-order generalized integral flux observer (SOIFO) and second-order SOIFO are designed for the rotor flux estimation of PMSM. Based on second-order generalized integrator (SOGI) structure, the SOIFO can limit the dc component to a certain value. Furthermore, the second-order SOIFO is developed from the SOGI, which is characterized with effective dc and harmonics attenuation capability. With the second-order SOIFO, even without magnitude and phase compensation, the dc offset and harmonics of estimated rotor flux could be well eliminated. Therefore, the speed and rotor position can be estimated accurately. All the performances of the four methods are analyzed by transfer functions and Bode diagrams. Lastly, the new sensorless control strategy is validated by comprehensive experimental results.
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