Application of Artificial Neural Network to Robust Speed Control of Servodrive

1. Application of Artificial Neural Network to Robust Speed Control of Servodrive Tomasz Pajchrowski and Krzysztof Zawirski, Senior Member, IEEE IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 54, NO. 1, FEBRUARY 2007 PPT製作:100% 指導教授 : 龔應時 學　　生 : 顏志男

2. Outline • Abstract • INTRODUCTION • CONCEPT OF THE ROBUST SPEED CONTROLLER • SIMULATION RESULTS • EXPERIMENT RESULTS • CONCLUSION • REFERENCES

3. Abstract • This paper deals with the problem of robust speed control of electrical servodrives. • A robust speed controller is developed using an artificial neural network (ANN), which creates a nonlinear characteristic of controller. An original method of neural controller synthesis is presented. • The synthesis procedure is performed in two stages. The first stage consists in training the ANN and at the second stage controller settings are adjusted.

4. INTRODUCTION(1/5) • One of the well-known methods to achieve robustness is the sliding mode control (SMC) [1], [6], [12], [13]. • However, SMC creates some problems in practical applications due to chattering effects, such as high-frequency oscillation of motor torque, additional mechanical stresses, and noise during operation. • Many solutions have been proposed [1]–[3], [5], [6],[12], [13] to circumvent the problem mentioned above, among them one of the best is a continuous SMC [6], [12], [13].

5. INTRODUCTION(2/5) • The problem of servodrive control robustness seems to be the actual research task. • In this filed, interesting solutions can be obtained by the application of computational intelligence methods like fuzzy logic control (FLC) or artificial neural network (ANN) control [2],[8] • In this paper, an ANN controller is proposed which due to its specific feature and training ability enables an elaborate transparent synthesis procedure.

6. INTRODUCTION(3/5) • The robust speed controller proposed in this paper is based on the introduction of ANN into the structure of the PI speed controller[7]. • Robustness is achieved by creating proper nonlinear characteristics of the controller by ANN. • The original feature of this synthesis procedure consists of its decomposition into two stages and consideration of declared range of parameter variation in training procedure and controller settings adjustment.

7. INTRODUCTION(4/5) • The first stage consists in training the ANN to form the proper shape of the control surface, which represents the nonlinear characteristic of the controller. • At the second stage, the PI controller settings are adjusted by means of the random weight change (RWC) procedure [4] which optimizes the control quality index formulated in this paper. • Fig. 1 presents the structure of the servodrive control system.

8. INTRODUCTION(5/5)

9. CONCEPT OF THE ROBUST SPEED CONTROLLER(1/10) A.Controller Structure • The proposed robust speed controller consists of three blocks:a module of input signal conversion, an ANN module, and a module of output signal generation. • The controller structure is shown in Fig. 2.

10. CONCEPT OF THE ROBUST SPEED CONTROLLER(2/10) A.Controller Structure • The process of neural controller synthesis is decomposed into two stages:(1) synthesis of an ANN (selecting structure and weight coefficients in activation functions);(2) adjusting PI controller settings. • The structure shown in Fig. 3 was obtained by trial and error during numerous simulation tests.

11. CONCEPT OF THE ROBUST SPEED CONTROLLER(3/10) A.Controller Structure

12. CONCEPT OF THE ROBUST SPEED CONTROLLER(4/10) B.Training Procedure • Weight coefficients in the activation function are selected during the process of training the ANN. • For the training, a pattern speed response of the controlled system is assumed. • This pattern speed response [Fig. 4(a)] is created as a step response of a simplified model of the servodrive speed control system presented in Fig. 4(b).

13. CONCEPT OF THE ROBUST SPEED CONTROLLER(5/10) B.Training Procedure

14. CONCEPT OF THE ROBUST SPEED CONTROLLER(6/10) B.Training Procedure • For the block diagram presented in Fig. 4(b), one can introduce an equivalent gain coefficient kω, which is described as • where kωmax and kωmix are the assumed maximum and minimum values of the variable parameter

15. CONCEPT OF THE ROBUST SPEED CONTROLLER(7/10) B.Training Procedure • The pattern waveform of the transient process is divided into 200 time steps. At each step(k) the speed value and its derivative is calculated, which results in a pair of input signals e(k),de/dt(k) for the ANN. • The required output controller signal, the value diq/dt(k), is calculated from (3) to (5) where JΣ/Ko is equal to 1/kω according to (1).

16. CONCEPT OF THE ROBUST SPEED CONTROLLER(8/10) B.Training Procedure • The learning error QL,determined from (6), reaches a smaller value than the arbitrary selected acceptable value

17. CONCEPT OF THE ROBUST SPEED CONTROLLER(9/10) C. Controller Settings Adjustment • At the second stage of the controller synthesis, an adjustment of two controller settings: the gain coefficient kp and the integral time constant Ti is performed. • The criterion Qc (quality index) assumed for controller optimization, described by formula (10), contains a sum of three components

18. CONCEPT OF THE ROBUST SPEED CONTROLLER(10/10) C. Controller Settings Adjustment • All three components characterize the step response of speed control, which is shown in Fig. 5.

19. SIMULATION RESULTS(1/10) A. Model Description • The simulation technique was used for training the ANN and testing the control system operation. As mentioned above, the PMSM was assumed as a good representative of servodirve. • The model is written in the d-q rotating frame

20. SIMULATION RESULTS(2/10) B. Influence of Quality Index Components on Controller Operation • The model of the PMSM and the control system was performed in MATLAB. Data of the investigated drive are given in Table III. • These tests were performed for different values of kω. For simplicity of PVR determination, only variation of the total moment of inertia was assumed. • The simulation results are presented in Figs. 6–8.

21. SIMULATION RESULTS(3/10) B. Influence of Quality Index Components on Controller Operation

22. SIMULATION RESULTS(4/10) B. Influence of Quality Index Components on Controller Operation

23. SIMULATION RESULTS(5/10) B. Influence of Quality Index Components on Controller Operation

24. SIMULATION RESULTS(6/10) B. Influence of Quality Index Components on Controller Operation • Such analysis can be made on the basis of results collected in Table I. For transient process, the same is presented in Figs. 6–8, the ISE(Qo) value was measured for different inertia. • The value of ISE calculated in reference to its assumed maximum value using formula (12) is treated as an index(Qo) which helps to evaluate obtained robustness

25. SIMULATION RESULTS(7/10) B. Influence of Quality Index Components on Controller Operation

26. SIMULATION RESULTS(8/10) C. Influence of PVR Value • The proposed method gives a chance to make a synthesis of the robust speed controller with assumption of different range of drive parameter variation determined by the value of PVR. • To analyze the influence of assumed different values of PVR on control properties some additional test were done. • The robust controller settings were adjusted during synthesis process for following values of PVR: 2, 8, and 32. Figs. 9

27. SIMULATION RESULTS(9/10) C. Influence of PVR Value

28. SIMULATION RESULTS(10/10) C. Influence of PVR Value • This effect is better visible comparing values of index Qo and settling time collected in Table II.

29. EXPERIMENT RESULTS(1/4) • The laboratory setup consists of a PMSM supplied from a PWM transistor inverter and controlled by the floating-point signal processor ADSP–21061. • Some results of the laboratory tests are shown in Figs. 10 and 11. During the tests, the total value of the moment of inertia of the drive was changed from small to large.

30. EXPERIMENT RESULTS(2/4)

31. EXPERIMENT RESULTS(3/4)

32. EXPERIMENT RESULTS(4/4)

33. CONCLUSION • The presented investigation results confirm that the proposed speed controller of PI type with proper nonlinear characteristics allows to obtain the robustness of speed control against the variations of the moment of inertia and torque constant. • These nonlinear controller characteristics were effectively achieved using an ANN application. • Finally, it must be remarked that the cost of obtained robustness is visible in the reduction of dynamic properties of the drive.

34. REFERENCES(1/2) • [1] H. Z. Akpolat, G. M. Asher, and J. C. Clare, “A practical approach to the design of robust speed controllers for machine drives,” IEEE Trans. Ind. Electron., vol. 47, pp. 315–324, Apr. 2000. • [2] J. Arellano-Padilla, G. M. Asher, and M. Sumner, “Robust fuzzysliding mode control for motor drives operating with variable loads and predefined system noise limits system,” in Proc. 9th Eur. Conf. Power Electron. Appl., Graz, Austria, Aug. 27–29, 2001. • [3] S. Brock, J. Deskur, and K. Zawirski, “Modified sliding mode speed controller for servo drives,” in Proc. IEEE Int. Symp. Ind. Electron., Bled-Slovenia, 1999, pp. 635–640. • [4] B. Burton, F. Karman, R. G. Harley, T. G. Habetler,M. A. Brooke, and R. Poddar, “Identification and control of induction motor station currents using fast on line random training neural networks,” IEEE Trans. Ind. Appl., vol. 33, no. 3, pp. 697–704, Jun. 1997. • [5] S. K. Chung, J. H. Lee, J. S. Ko, and M. J. Youn, “Robust speed control of brushless direct-drive motor using integral variable structure control,”Proc. IEEE Electron. Power Appl., vol. 142, no. 6, Nov. 1995. • [6] K. Erbatur, O. M. Kaynak, and A. Sabanovic, “A study on robustness property of sliding-mode controllers: A novel design and experimental investigations,” IEEE Trans. Ind. Electron., vol. 46, no. 5, pp.1012–1017, Oct. 1999. • [7] M. Norgaard, O. Ravn, N. K. Poulsen, and L. K. Hansen, Neural Networks for Modelling and Control of Dynamic Systems. Berlin, Germany: Springer-Verlag, 2000. • [8] T. Orlowska-Kowalska and K. Szabat, “Optimization of fuzzy logic speed controller for DC drive system with elastic joints,” IEEE Trans. Ind. Appl., vol. 40, no. 4, pp. 1138–144, Jul./Aug. 2004.

35. REFERENCES(2/2) • [9] T. Pajchrowski, K. Urbanski, and K. Zawirski, “Robust speed control of PMSM servodrive based on ANN application,” in Proc. 10th Eur.Conf. Power Electron. Appl., Toulouse, France, Sep. 2–4, 2003. • [10] T. Pajchrowski and K. Zawirski, “Robust speed control of PMSM with neuro and fuzzy technique application,” in Proc. 12th Int. Power Electron.Motion Control Conf., Ryga, Sep. 2–4, 2004. • [11] ——, “Robust speed control of servodrive based on ANN,” in Proc. Int. Symp. Ind. Electron., Dubrovnik, Croatia, Jun. 20–23, 2005. • [12] M. H. Park and K. S. Kim, “Chattering reduction in the position control of induction motor using the sliding mode,” IEEE Trans. Power Electron.,vol. 6, Jul. 1991. • [13] M. Rodic, A. Sabanovic, and K. Jezernik, “Sliding mode observer scheme for induction motor control,” EPE Journal, vol. 11, no. 3, pp.29–34, Aug. 2001. • [14] B. Shamin and M. Hassul, Control System Design Using Matlab. Englewood Cliffs, NJ: Prentice-Hall, 1993. • [15] P. Vas, Sensorless Vector and Direct Torque Control. London, U.K.: Oxford Univ. Press, 1998. • [16] J. Vitek, T. Baculak, S. J. Dodds, and R. Perryman, “Near-time-optimal control of electrical drives with permanent magnet synchronous motor,” Elect. Power Electron., 2003, Toulouse, CD-Rom.