Adaptive Fuzzy Sliding Mode Control of Linear Induction Motors with Unknown End Effect Consideration

[[abstract]]In this paper, an adaptive fuzzy sliding mode controller (AFSMC) is proposed for a linear induction motor (LIM) taking into account the longitudinal end effect and uncertainties including the friction force. The dynamic mathematical model of an indirect field-oriented LIM drive is firstly derived for controlling the LIM. On the basis of a backstepping control law, a sliding mode controller (SMC) embedded with fuzzy boundary layer is designed to compensate lumped uncertainties during the tracking control of periodic reference trajectories. Since the bound of lumped uncertainties is difficult to obtain advance in practical applications, an adaptive tuner based on the sense of Lyapunov stability theorem is derived to adjust the controller parameter in real-time, and also for further confronting the increasing disturbance and uncertainties. The indirect field-oriented LIM with the proposed AFSMC assures the system stability, asymptotic output tracking, and the robust control performance. The effectiveness of the proposed control scheme is verified through experimental results, and its advantages of control performance and robustness are exhibited in comparison with SMC and FSMC approaches.[[conferencetype]]國際[[conferencedate]]20120918~20120921[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tokyo, Japa

Terminal sliding mode control strategy design for second-order nonlinear system

This study mainly focuses on the terminal sliding mode control (TSMC) strategy design, including an adaptive terminal sliding mode control (ATSMC) and an exact-estimator-based terminal sliding mode control (ETSMC) for second-order nonlinear dynamical systems. In the ATSMC system, an adaptive bound estimation for the lump uncertainty is proposed to ensure the system stability. On the other hand, an exact estimator is designed for exact estimating system uncertainties to solve the trouble of chattering phenomena caused by a sign function in ATSMC law in despite of the utilization of a fixed value or an adaptive tuning algorithm for the lumped uncertainty bound. The effectiveness of the proposed control schemes can be verified in numerical simulations.<br /

PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles

There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feed-forward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.Comment: This paper has been accepted for publication in Information Science Journal 201

Development of Fuzzy Applications for High Performance Induction Motor Drive

This chapter develops a sliding mode and fuzzy logic-based speed controller, which is named adaptive fuzzy sliding-mode controller (AFSMC) for an indirect field-oriented control (IFOC) of an induction motor (IM) drive. Essentially, the boundary layer approach is the most popular method to reduce the chattering phenomena, which leads to trade-off between control performances, and chattering elimination for uncertain nonlinear systems. For the proposed AFSMC, a fuzzy system is assigned as the reaching control part of the fuzzy sliding-mode controller so that it improves the control performances and eliminates the chattering completely despite large and small uncertainties in the system. A nonlinear adaptive law is also implemented to adjust the control gain with uncertainties of the system. The adaptive law is developed in the sense of Lyapunov stability theorem to minimize the control effort. The applied adaptive fuzzy controller acts like a saturation function in the thin boundary layer near the sliding surface to guarantee the stability of the system. The proposed AFSMC-based IM drive is implemented in real-time using digital signal processor (DSP) board TI TMS320F28335. The experimental and simulation results show the effectiveness of the proposed AFSMC-based IM drive at different operating conditions such as load disturbance, parameter variations, etc

Optimized Adaptive Sliding-mode Position Control System for Linear Induction Motor Drive

[[abstract]]This paper proposes an optimized adaptive position control system applied for a linear induction motor (LIM) drive taking into account the longitudinal end effects and uncertainties including the friction force. The dynamic mathematical model of an indirect field-oriented LIM drive is firstly derived for controlling the LIM. On the basis of a backstepping control law, a sliding mode controller (SMC) with embedded fuzzy boundary layer is designed to compensate the lumped uncertainties during the tracking control of periodic reference trajectories. Since it is difficult to obtain the bound of lumped uncertainties in advance in practical applications, an adaptive tuner based on the sense of Lyapunov stability theorem is derived to adjust the fuzzy boundary parameters in real-time. It is a quite complicated process of parameter tuning, especially for the proposed controller, due to the difficulty arisen from lacking of the accurate mathematical model of a system accompanied with unknown disturbance. Therefore, the soft-computing technique is adopted for off-line optimizing the controller parameters. The effectiveness of the proposed control scheme is validated through simulations and experiments for several scenarios. Finally, the advantages of performance improvement and robustness are illustrated at the end of the optimization procedure.[[conferencetype]]國際[[conferencedate]]20130410~20130412[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Paris, Franc

Hybrid Speed Controller Design Based on Sliding Mode Controller Performance Study for Vector Controlled Induction Motor Drives

The discontinuous control of the sliding mode control (SMC) law causes chattering phenomenon in system trajectories (the oscillation around the desired value), which results in various unwanted effects such as current harmonics and torque ripples. Therefore, this study aims to investigate the performance of a sliding mode speed controller for a three-phase induction motor (IM) controlled by a rotor flux orientation technique to obtain optimum performance. The study results show that the experimental control gains found in the control law have a clear effect on limiting chattering and the system response speed. According to the study results, a hybrid controller is designed based on the fuzzy logic control (FLC) approach to optimally tune these gains. The designed hybrid controller is verified by experimental approximation of simulations using Matlab/Simulink. The simulation results show that the hybrid controller reduces the chattering phenomenon and improves the system’s dynamic performance

Position control of induction motor using indirect adaptive fuzzy sliding mode control

Author name used in this publication: K. W. E. ChengAuthor name used in this publication: H. F. HoVersion of RecordPublishe