FPGA-based adaptive dynamic sliding-mode neural control for a brushless DC motor (original) (raw)
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Intelligent fuzzy sliding mode controller based on FPGA for the speed control of a BLDC motor
International Journal of Power Electronics and Drive System (IJPEDS) , 2020
Brushless DC (BLDC) motors are one of the most widely used motors for various industrial applications due to their high efficiency, high torque to weight ratio and elimination of mechanical commutator. These motors operate in wide range of speeds and necessitate precise speed control techniques, for their nonlinear model, insenseitive to parameter variations and external disturbances, when used in various sensitive applications. Conventional PI and other existing controllers produce high overshoot and increased rise time and settling time. The performance of BLDC motor is enhanced using a Fuzzy Sliding Mode Controller (FSMC) whose gain is intelligently varied with the help of a Fuzzy Inference System (FIS). For this purpose, a suitable FSMC is designed, simulated and implemented using FPGA. The simulation results are validated using Hardware in the loop (HIL) simulation as well as actual hardware implementation. Great improvement in the transient performance is achieved when compared to chatter free SMC, Fuzzy PI and conventional PI controller.
Robust Adaptive Sliding Mode Control Design with Genetic Algorithm for Brushless DC Motor
Proceeding of the Electrical Engineering Computer Science and Informatics, 2018
This study aims to design a control scheme that is capable to improve performance and efficiency of brushless DC motor (BLDC) in operating condition. The control scheme is composed of sliding mode controller (SMC) with proportionalintegral-derivative (PID) sliding surface. The PID sliding surface is used to improve the system transient response. Then, the SMC-PID is optimized by genetic algorithm optimization for further improvement on the stability and robustness against nonlinearities and disturbances. Chattering problem that appear in the SMC is minimized by employing an adaptive switching gain for the SMC that is integrated with Luenberger Observer. Lyapunov function candidate is applied to guarantee the stability of the system. Simulation on the proposed work is done in Matlab Simulink. Results of the simulation works indicate that the proposed control scheme can improve the transient response, the stability and robustness of the BLDC motor compared to the conventional SMC in the existence of nonlinearities and disturbances.
International Conference on Aerospace Sciences and Aviation Technology
In this paper adaptive control of a brushless DC motor (BLDCM) using neural network identification and pole shifting (PS) controller is presented. Proper system identification is one of the important factors that gives a good controller performance. This means that when the model parameter estimates are good, the controller output is good, whereas if the model parameter estimates are bad then almost surely the computed control will be bad. Proper selection of the identified system model order is also investigated. A comparison study between fuzzy logic controller and the proposed controller is also investigated.
A hybrid neuro-fuzzy controller for brushless DC motors
Artificial Intelligence and Neural Networks, 2006
In this paper, a hybrid neuro-fuzzy controller (NFC) is presented for the speed control of brushless DC motors to improve the control performance of the drive under transient and steady state conditions. In the hybrid control system, proportional-derivative (PD) type neuro-fuzzy controller (NFC) is the main tracking controller, and an integral compensator is proposed to compensate the steady state errors. A simple and smooth activation mechanism described for integral compensator modifies the control law adaptively. The presented BLDC drive has fast tracking capability, less steady state error and robust to load disturbance, and do not need complicated control method. Experimental results showing the effectiveness of the proposed control system are presented.
11th Mexican International Conference on Artificial Intelligence (MICAI), 2012
DC motors have been leading the field of adjustable speed drives for a long time due to its excellent control characteristics. This paper addresses a novel speed control application for DC motors gathering the features of Sliding Mode Control (SMC), Fuzzy Inference System (FIS), Neural Networks (NNs) and Genetic Algorithms (GAs). The main goal about combining these techniques is to create a robust speed controller avoiding the main disadvantage of SMC, the chattering. The design of the controller is implemented on a FPGA (Field Programmable Gate Array) and the steps for carrying out the implementation are described in detail. Finally, the results show a comparison between three different schemes of the designed controller.
Real-time implementation of a novel hybrid fuzzy sliding mode control of a BLDC motor
International Journal of Power Electronics and Drive System (IJPEDS), 2018
This paper presents a novel hybrid control of a BLDC motor using a mixed sliding mode and fuzzy logic controller. The objective is to build a fast and robust controller which overcome classical controllers' inconveniences and exploit the fast response of brushless dc motors characterized by an intense torque and fast response time. First the paper study pros and cons of both sliding mode and fuzzy logic controllers. Then the novel controller and its stability demonstration are presented. Finally the proposed controller method is used for the speed control of a BLDC motor 3KW. The obtained results are compared with those of a fuzzy logic and a conventional sliding mode controller. It allows to show performance of the proposed controller in terms of speed response and reaction against disturbances, which is improved more than 5 times without losing stability or altering tracking accuracy.
King Mongkut’s University of Technology North Bangkok International Journal of Applied Science and Technology, 2016
This paper presents an Artificial Intelligence (AI) based approach uniquely applied to permanent magnet DC motor actuator for position control. The AI method employed in this work is fuzzy logic. A first order lag sliding mode controller is tuned and combined with an adaptive Fuzzy-PI controller architecture which operates in parallel. The controller architecture proposed in this study is aimed at improving the disturbance rejection capability, steady state as well as transient performance of the conventional adaptive Fuzzy-PI controller and sliding mode controller. Hence, the robust control law of the proposed controller (SM+FZ-PI) consists of a discontinuous sliding mode output added to a continuous adaptive Fuzzy-PI controller output. The sliding mode controller switches on only when disturbance in the system is detected. The performance of the proposed controller architecture has been compared with a conventional PID and adaptive Fuzzy-PI controllers for performance evaluation with respect to several operating conditions such as load torque disturbance injection, noise injection in feedback loop, motor non-linearity exhibited by parameters variation, and a step change in reference input demand. The proposed controller (SM+FZ-PI), had the best disturbance rejection and steady state error elimination.
A tracking control design for a DC motor using robust sliding mode learning control
International Journal of Power Electronics and Drive Systems (IJPEDS)
The proposed robust sliding mode learning control (RSMLC) is a new controlapproach that uses immediate feedback from the closed-loop system to improve tracking performance. A recursive learning technique is integrated with the sliding mode controller to ensure that the tracking error and sliding variables asymptotically converge to zero, which can be guaranteed within the framework of the proposed control approach. Moreover, the proposed controller design does not require system uncertainty and its upper limits. Thus, these benefits can be significantly simplified and mitigated by the design and implementation of RSMLC for DC motor applications. In comparison with conventional sliding mode control (CSMC), the RSMLC structure does not contain an explicit switching element, so the chattering phenomenon will be eliminated. Meanwhile, it will preserve the CSMC’s durability feature. Based on Lyapunov criteria, the stability and convergence analysis of the proposed controller were rigorou...
COMPARISON OF SLIDING MODE AND PROPORTIONAL INTEGRAL CONTROL FOR BRUSHLESS DC MOTOR
This paper will compare properties of Sliding Mode Controlled (SMC) and classical Proportional Integral (PI) controlled brushless DC motor (BLDC) in applications. It is the simple strategy required to achieve good performance in speed or position control applications. This paper addresses controlling of speed of a BLDC motor which remains among the vital issues. A BLDC motor is generally controlled by Proportional plus Integral (PI) controller. PI controller is simple but sensitive to parameter variations and external disturbance. Due to this reasons, Sliding Mode Control (SMC) is proposed in this paper. This control technique works against parameters variations and external disturbances, and also its ability in controlling linear and nonlinear systems. Performance of these controllers has been verified through simulation using MATLAB/SIMULINK software. The simulation results showed that SMC was a superior controller than PI controller for speed control of a BLDC motor