| Induction Motor(IM)is generally used in many fields such as industry,agriculture,and new energy because of its high operating efficiency and strong practicability.However,the dynamic mathematical model of IM is a nonlinear system with many variables,high order,strong coupling.In addition,IM may be disturbed by stochastic disturbances such as ambient temperature changes,voltage fluctuations during actual operation,and the existence of these stochastic factors may affect the control effectiveness of the system,and even cause oscillation and instability of the system.At the same time,the convergence speed of the system is also one of the significant indicators to measure the control performance,and achieving control objectives in a short time is more in line with practical application requirements.Therefore,realizing the finite-time position tracking control of IM system considering stochastic disturbances is a research topic of great significance.The research fruits of this thesis are as follows:1.A finite-time command filtering backstepping control method is researched for stochastic nonlinear systems with dead-zone input.Firstly,the dead-zone input nonlinearity is expressed as a combination of a linear term and a bounded disturbance term,and the fourth Lyapunov functions are selected to design the backstepping controller.In addition,the approximation properties of the adaptive fuzzy systems are used to process nonlinear functions and unknown parameters in the system model,the command filter is introduced to avoid the explosive growth of computation caused by the repeated derivation of the virtual control law in traditional backstepping design process,and the finite-time control technology is combined to guarantee fast convergence of the system.Finally,based on the stochastic finite-time stability theory,the steadiness of the closed-loop system is examined.2.For the IM system with stochastic disturbances,a fuzzy approximation-based finitetime command filtering adaptive backstepping control method is studied for its position tracking.Firstly,the dynamic mathematical model of the IM stochastic system is constructed,and the Ito differential formula is used to calculate the fourth Lyapunov functions.The fuzzy adaptive technology is used to process the nonlinear functions in the stochastic system.The second-order finite-time command filter is introduced to obtain the approximate value of the virtual control functions and its derivative,which is combined with error compensation signals to compensate filtering errors to ensure the control accuracy of the system and achieve rapid response and convergence.The studied control scheme by simulation and experimental verification show that it can overcome the interference of stochastic disturbances and achieve position tracking control faster.3.Considering IM stochastic system with speed sensorless,a command filter and observer-based adaptive finite-time backstepping control strategy is studied.The traditional controller design is carried out on the premise that state variables of the motor can be directly measured by sensors.However,in the case of mechanical sensors that cannot be installed or their sensitivity is reduced,a fuzzy reduced-order observer is designed to obtain the estimated value of the motor speed,which decreases the hardware investment and subsequent maintenance costs of the system.A combination of stochastic control theory,command filtering backstepping,fuzzy adaptive technology,finite-time control method is used to achieve fast and effective tracking of a given desired signal,and the rotational speed signal of IM can effectively estimate by the constructed fuzzy reduced-order observer. |
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