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https //iaeme com/Home/journal/IJPTM 9 editor@iaeme com International Journal of Production Technology and Management (IJPTM) Volume 13, Issue 1, January December, 2022, pp 9–16, Article ID IJPTM 13 0[.]
International Journal of Production Technology and Management (IJPTM) Volume 13, Issue 1, January-December, 2022, pp 9–16, Article ID: IJPTM_13_01_002 Available online at https://iaeme.com/Home/issue/IJPTM?Volume=13&Issue=1 ISSN Print: 0976- 6383 and ISSN Online: 0976 – 6391 DOI: https://doi.org/10.17605/OSF.IO/32RG6 © IAEME Publication STUDY OF IDENTIFIED AND CONTROL PERMANENT MAGNET SYNCHRONOUS MOTOR USING THE ALGORITHM NEURAL NETWORK Nam Nguyen Duy Dong Nai Technology University, Vietnam ABSTRACT This paper proposes a method to control speed of permanent magnet synchronous motor (PMSM), using the PI control combines intelligent neural network control, radial basis function neural network (RBF NN) Firstly, the model of RBF NN, consisting of an input layer, a hidden layer of nonlinear processing neurons with Gaussian functions and an output layer is presented in the paper In particular, a monitoring mechanism based on the Stochastic gradient descent (SGD) method is used to update the parameters of the RBF NN to minimize the error to minimum the error value Then, the high-speed integrated circuit hardware description language (VHDL) is used to describe the behavior of the whole RBF NN and related learning algorithms Simulation model is implemented in Matlab Simulink and ModelSim Finally, the RBF neural network is used for identification and control in linear/ nonlinear systems and in permanent magnet synchronous motor systems (PMSM), to validate the efficiency and effectiveness Key words: Radial Basis Function Neural Network (RBF NN); PI Controller; VHDL; Simulink simulation; ModelSim simulation; Permanent magnet synchronous motor (PMSM) drive Cite this Article: Nam Nguyen Duy, Study of Identified and Control Permanent Magnet Synchronous Motor Using the Algorithm Neural Network, International Journal of Production Technology and Management (IJPTM), 13(1), 2022, pp 1–8 https://iaeme.com/Home/issue/IJPTM?Volume=13&Issue=1 INTRODUCTION In recognition and control problems, the application of neural networks is increasingly popular, but due to the high nonlinearity of the activation function in neural networks, the implementation of neural networks in digital systems faces many difficulties and the accuracy is not high high Besides, the design of the forward propagation process and the back propagation process of the network need to have a good coordination for the effective learning of the neural network In this case, the neural network needs to be implemented on the hardware https://iaeme.com/Home/journal/IJPTM editor@iaeme.com Study of Identified and Control Permanent Magnet Synchronous Motor Using the Algorithm Neural Network platform [1-3] In fact, neural -networks can be implemented using both software and hardware Hardware-based implementations are especially useful in applications that require high speed and accuracy, such as in recognition technology and some other applications [4-5] SURVEY OF KINEMATIC MODELS PERMANENT MAGNET SYNCHRONOUS MOTOR 2.1 Overview of Permanent Magnet Synchronous Motor (PMSM) The permanent magnet synchronous motor is composed of symmetrically distributed 3-phase windings and a permanent magnet-mounted rotor to create a gap magnetic field The elimination of the excitation circuit on the rotor side offers several advantages for PMSM such as reduced copper loss, higher power density, reduced motor moment of inertia, and mechanically stable rotor construction.[2-5] 2.2 Kinetic model of PMSM 2.2.1 Equivalent Source The power inputs for three-phase machines that are equal to the power inputs for two-phase machines have explanatory significance in measurement, analysis, and simulation p = vt i =v i +v i +v i i abc abc as as bs bs cs cs (1) pi : instantaneous input power vt abc : instantaneous phase voltage vector abc v , v v : phase input voltage a, b, c as bs, cs iabc : vector phase voltage abc i , i i : phase instantaneous stator current as bs, cs For balanced 3-phase machines, sequence currents are non-existent so the input power can be reduced: 3 p = v r i r + v r i r i qs qs ds ds (2) 2.2.2 Electromagnetic Torque Electromagnetic torque is the most important output variable for determining machine mechanical dynamics such as rotor position and speed It is derived from the machine matrix equation by looking at the input power and its other components such as resistive losses, mechanical power, and how much magnetic energy accumulates in the stator wire coil [6-7] The kinematic equations of PMSM can be written as: V = R i + L pi + G i r (3) Substituting [G] in the above equation we have the electromagnetic torque obtained as: 3P T = + L − L i r i r ( N m) e 2 af d q ds qs https://iaeme.com/Home/journal/IJPTM (4) 10 editor@iaeme.com Nam Nguyen Duy 2.2.3 Electromagnetic Torque The dynamic equations of the PMSM in the rotor coordinate system can be expressed using flux linkages as variables Even if the voltage and current are discontinuous, the loop fluxes are continuous The stator and rotor loop fluxes in the rotor coordinate system are defined as: r = L i r qs q qs (5) r = L i r + ds d ds af (6) The electromagnetic torque as a function of the loop fluxes is obtained by substituting the stator currents for the loop fluxes and is calculated as follows: 3P 3P T = + (1 − )r r = r i r − r i r e 2 L af ds qs 2 ds qs qs ds q (7) 2.2.4 Electromagnetic Torque The equivalent circuits of the PMSM can be derived from the stator equations and shown as Figure Figure Block diagram of a permanent magnet synchronous motor CONTROL STRATEGY OF PERMANENT MAGNET SYNCHRONIC MOTOR 3.1 Structure of Drive System to Control Permanent Magnet Synchronous Motor Figure Schematic diagram of vector control structure of permanent magnet synchronous motor torque control transmission system https://iaeme.com/Home/journal/IJPTM 11 editor@iaeme.com Study of Identified and Control Permanent Magnet Synchronous Motor Using the Algorithm Neural Network Figure The drive system regulates the speed of the permanent magnet synchronous motor 3.2 Control Strategies The control strategies are considered in detail in synchronous motor drive systems: Torque angle control is constant or shaft current d is Power factor control Controlling the total flux is constant Angle control of total flux and complex current vector Optimal control of torque Loss reduction control on the basis of control in the maximum torque-speed range Figure Implement the control to keep the power loss constant Figure The drivetrain structure controls the minimum loss https://iaeme.com/Home/journal/IJPTM 12 editor@iaeme.com Nam Nguyen Duy INTRODUCTION TO ARTIFICIAL NEUTRAL NETWORK Artificial Neural Networks are a powerful computing technology in the field of computer science With high-speed processing ability in learning solutions to a complex problem from a set of patterns, it has been widely studied and applied in recent years Figure Neural network model RBF NEUTRAL NETWORK APPLICATION IN IDENTIFICATION AND CONTROL Figure Response of RBF-PI controller based on RBF neural network Figure Linear system recognition using RBF NN u(k): control signal Yp(k+1): linear system Input of RBF NN includes: output of previous linear system + control signal https://iaeme.com/Home/journal/IJPTM 13 editor@iaeme.com Study of Identified and Control Permanent Magnet Synchronous Motor Using the Algorithm Neural Network Figure Tracking results with different learning rates (a) 0.1 (b) 0.25 (c) 0.5 From the results of the waveform, we can see that, with a small learning rate, the tracking response is slow The large learning rate leads to unstable response of the Neural network and strong fluctuations in the transition period Figure 10 Simulation model for identification of PMSM drive system Figure 11 The results of identification (a) and error (b) between the rotor speed and the speed according to the Neural network https://iaeme.com/Home/journal/IJPTM 14 editor@iaeme.com Nam Nguyen Duy Figure 12 Output response result when following reference model with input command step for (a) normal PI control and (b) RBF-PI control Table Comparison between conventional PI controller and RBF based PI controller Figure 13 Simulation model for adaptive RBF-PI identification and control with PMSM drive systems Table Input system initial values and PMSM parameters https://iaeme.com/Home/journal/IJPTM 15 editor@iaeme.com Study of Identified and Control Permanent Magnet Synchronous Motor Using the Algorithm Neural Network Figure 14 Speed response by conventional PI controller and RBF-PI We see that the rotor speed recovers at steady state when the system changes faster, the rotor speed is also closer to the reference speed CONCLUSION In the control algorithm, to adapt to the system uncertainty, RBF - PI is used to identify the nonlinear system and provide more accurate information for the parameters of RBF - PI The simulation results show that, according to the reference model, the speed of the PMSM can quickly and accurately respond after the proposed controller implements it simulation results achieved Through the research process, we can see that the adjusted RBF - PI controller exhibits good control efficiency, improving performance compared to conventional PI controllers That proves that the RBF - PI controller has good monitoring ability even though the system is unstable and complex REFERENCES [1] A Esmaeili, N Mozayani, “Adjusting the Parameters of Radial Basis Function Networks Using Particle Swarm Optimization,” IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, pp 179–181, 2009 [2] S W Ellacott, J C Mason, I J Anderson, Mathematics of Neural Networks: Modes, Algorithms, and Applications, New York, Springer, 1997 [3] H Robbins, D Siegmund, A convergence theorem for nonnegative almost super martingales and some applications, New York, Springer, 1985 [4] F Sargeni, V Bonaiuto, “Digitally programmable nonlinear function generator for neural networks,” Electronics Letters, vol 41, pp.143-145, 2005 [5] K Basterretxea, J M Tarela, I D Campo, G Bosque, “An experimental study on nonlinear function computation for neural/fuzzy hardware design,” IEEE Transactions on Neural Networks, vol 18, pp.266–283, 2007 [6] S Himavathi, D Anitha, A Muthuramalingam, “Feed-forward neural network implementation in FPGA using layer multiplexing for effective resource utilization,” IEEE Transactions on Neural Networks, vol 18, pp.880–888, 2007 [7] A Dinu, M N Cirstea, S E Cirstea, “Direct neural network hardware implementation algorithm,” IEEE Transactions on Industrial Electronics, vol 57, pp.1845–1848, 2010 https://iaeme.com/Home/journal/IJPTM 16 editor@iaeme.com ... editor@iaeme.com Study of Identified and Control Permanent Magnet Synchronous Motor Using the Algorithm Neural Network Figure The drive system regulates the speed of the permanent magnet synchronous motor. . .Study of Identified and Control Permanent Magnet Synchronous Motor Using the Algorithm Neural Network platform [1-3] In fact, neural -networks can be implemented using both software and hardware... SYNCHRONIC MOTOR 3.1 Structure of Drive System to Control Permanent Magnet Synchronous Motor Figure Schematic diagram of vector control structure of permanent magnet synchronous motor torque control