Improvement of PMSM Sensorless Control Based on Synergetic and Sliding Mode Controllers Using a Reinforcement Learning Deep Deterministic Policy Gradient Agent +

ABSTRACT:

The field‐oriented control (FOC) strategy of a permanent magnet synchronous motor (PMSM) in a simplified form is based on PI‐type controllers. In addition to their low complexity (an advantage for real‐time implementation), these controllers also provide limited performance due to the nonlinear character of the description equations of the PMSM model under the usual conditions of a relatively wide variation in the load torque and the high dynamics of the PMSM speed reference. Moreover, a number of significant improvements in the performance of PMSM control systems, also based on the FOC control strategy, are obtained if the controller of the speed control loop uses sliding mode control (SMC), and if the controllers for the inner control loops of id and iq currents are of the synergetic type. Furthermore, using such a control structure, very good performance of the PMSM control system is also obtained under conditions of parametric uncertainties and significant variations in the combined rotor‐load moment of inertia and the load resistance. To improve the performance of the PMSM control system without using controllers having a more complicated mathematical description, the advantages provided by reinforcement learning (RL) for process control can also be used. This technique does not require the exact knowledge of the mathematical model of the controlled system or the type of uncertainties. The improvement in the performance of the PMSM control system based on the FOC‐type strategy, both when using simple PI‐type controllers or in the case of complex SMC or synergetic‐type controllers, is achieved using the RL based on the Deep Deterministic Policy Gradient (DDPG). This improvement is obtained by using the correction signals provided by a trained reinforcement learning agent, which is added to the control signals ud, uq, and iqref. A speed observer is also implemented for estimating the PMSM rotor speed. The PMSM control structures are presented using the FOC‐type strategy, both in the case of simple PI‐type controllers and complex SMC or synergetic‐type controllers, and numerical simulations performed in the MATLAB/Simulink environment show the improvements in the performance of the PMSM control system, even under conditions of parametric uncertainties, by using the RL‐DDPG.

KEYWORDS:

1.      Permanent magnet synchronous motor

2.      Sliding mode control

3.      Synergetic control

4.      Reinforcement learning

5.      Deep neural networks

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

 

Figure 1. Block diagram for FOC‐type control of the PMSM based on PI‐type controllers using RL.

EXPECTED SIMULATION RESULTS:

 

 

Figure 2. Time evolution for the numerical simulation of the PMSM control system based on the FOC‐type strategy.

Figure 3. Time evolution for the numerical simulation of the PMSM control system based on the RL‐TD3 agent for the correction of iqref.

Figure 4. Time evolution for the numerical simulation of the PMSM control system based on the

RL‐TD3 agent for the correction of udref and uqref.

Figure 5. Time evolution for the numerical simulation of the PMSM control system based on the

RL‐TD3 agent for the correction of udref, uqref, and iqref.

Figure 6. Time evolution for the numerical simulation of the PMSM control system based on control

using SMC and synergetic controllers.

Figure 7. Time evolution for the numerical simulation of the PMSM control system based on control

using SMC and synergetic controllers using an RL‐TD3 agent for the correction of iqref.

 

CONCLUSION:

This paper presents the FOC‐type control structure of a PMSM, which is improved in terms of performance by using a RL technique. Thus, the comparative results are presented for the case where the RL‐TD3 agent is properly trained and provides correction signals that are added to the control signals ud, uq, and iqref. The FOC‐type control structure for the PMSM control based on an SMC speed controller and synergetic current controller is also presented. To improve the performance of the PMSM control system without using controllers having a more complicated mathematical description, the advantages provided by the RL on process control can also be used. This improvement is obtained using the correction signals provided by a trained RL‐TD3 agent, which is added to the control signals ud, uq, and iqref. A speed observer is also implemented for estimating the PMSM rotor speed. The parametric robustness of the proposed PMSM control system is proved by very good control performances achieved even when the uniformly distributed noise is added to the load torque TL, and under high variations in the load torque TL and the moment of inertia J. Numerical simulations are used to prove the superiority of the control system that uses the RL‐TD3 agent.

REFERENCES:

1. Eriksson, S. Design of Permanent‐Magnet Linear Generators with Constant‐Torque‐Angle Control for Wave Power. Energies 2019, 12, 1312.

2. Ouyang, P.R.; Tang, J.; Pano, V. Position domain nonlinear PD control for contour tracking of robotic manipulator. Robot. Comput. Integr. Manuf. 2018, 51, 14–24.

3. Baek, S.W.; Lee, S.W. Design Optimization and Experimental Verification of Permanent Magnet Synchronous Motor Used in Electric Compressors in Electric Vehicles. Appl. Sci. 2020, 10, 3235.

4. Amin, F.; Sulaiman, E.B.; Utomo, W.M.; Soomro, H.A.; Jenal, M.; Kumar, R. Modelling and Simulation of Field Oriented Control based Permanent Magnet Synchronous Motor Drive System. Indones. J. Electr. Eng. Comput. Sci. 2017, 6, 387.

5. Mohd Zaihidee, F.; Mekhilef, S.; Mubin, M. Robust Speed Control of PMSM Using Sliding Mode Control (SMC)—A Review. Energies 2019, 12, 1669.

Improvement of PMSM Control Using Reinforcement Learning Deep Deterministic Policy Gradient Agent

ABSTRACT:

Based on the advantage of using the reinforcement learning on process control, provided by the fact that it is not necessary to know the exact mathematical model and the structure of its uncertainties, this article approaches the possibility of improving the performances of the Permanent Magnet Synchronous Motor (PMSM) control system based on the Field Oriented Control (FOC) type control strategy, by using the correction signals provided by a trained reinforcement learning agent, which will be added to the control signals ud, uq, and iqref . The type of reinforcement learning used is the Deep Deterministic Policy Gradient (DDPG). The combination possibilities of these control structures are presented, and their superiority over the FOC type control strategy is validated by numerical simulations.

KEYWORDS:

1.      Permanent magnet motors

2.      Field oriented control

3.      Reinforcement learning

4.      Intelligent agent

5.      Deep neural networks

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

 

Fig. 1. Block diagram for FOC-type control of the PMSM based on reinforcement learning.

EXPECTED SIMULATION RESULTS:

Fig. 2. Time evolution for the numerical simulation of the PMSM control system based on the FOC-type strategy.

 

Fig. 3. Time evolution for the numerical simulation of the PMSM control system based on TD3 agent for the correction of udref and uqref .

 

Fig. 4. Time evolution for the numerical simulation of the PMSM control system based on TD3 agent for the correction of iqref.

 

Fig. 5. Time evolution for the numerical simulation of the PMSM control system based on TD3 agent for the correction of udref, uqref and iqref.

 

CONCLUSION:

This article presents the FOC-type control structure of a PMSM, which is improved in terms of performance by using a reinforcement learning technique. Thus, the comparative results are presented for the case where the reinforcement learning agent is properly trained and provides correction signals that will be added to the control signals ud, uq, and iqref. Numerical simulations are used to demonstrate the superiority of the control system that uses the reinforcement learning, and the following papers will study the possibilities for optimization in terms of the implementation of the reinforcement learning in the PMSM control.           

REFERENCES:

[1] B. Wu and M. Narimani, Control of Synchronous Motor Drives, in High-Power Converters and AC Drives , Wiley-IEEE Press, 2017, pp.353-391.

[2] B. K. Bose, Modern power electronics and AC drives, Prentice Hall, Knoxville, Tennessee, USA, 2002.

[3] H. Wang and J. Leng, “Summary on development of permanent magnet synchronous motor,” Chinese Control And Decision Conference (CCDC), Shenyang, China, 2018, pp. 689-693.

[4] Z. Liu, Y. Li, and Z. Zheng, “A review of drive techniques for multiphase machines,” in CES Transactions on Electrical Machines and Systems, vol. 2, pp. 243-251, June 2018.

[5] S. Sakunthala, R. Kiranmayi, and P. N. Mandadi, “A Review on Speed Control of Permanent Magnet Synchronous Motor Drive Using Different Control Techniques,”International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, China , 2018, pp. 97-102.

Research on Anti DC Bias and High Order Harmonics of Fifth Order Flux Observer for IPMSM Sensorless Drive

ABSTRACT:

Due to various nonideal factors, including the motor parameter mismatches, detection errors, converter nonlinearities, noise, etc. DC bias and high order harmonics exist in flux model, which make the traditional flux observer estimation inaccurate. In order to suppress DC bias and high order harmonics, an interior permanent magnet synchronous motors (IPMSM) sensorless drive method based on a fifth order flux observer (FOFO) is proposed in this paper. The proposed FOFO can completely remove DC bias and has strong filtering ability for high order harmonics. Additionally, the parameters of the FOFO are set through s-domain analysis. Then, the discrete FOFO is obtained to better implementation in digital systems. The proposed FOFO is verified by experiments on a 2.0-kW IPMSM drive platform.

KEYWORDS:

1.      Interior permanent magnet synchronous motors

2.       Fifth order flux observer

3.      Sensorless drive

4.      DC bias

5.      High order harmonics

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

 

Fig. 1. The block diagram of IPMSM sensorless drive based on the FOFO.

 EXPECTED SIMULATION RESULTS:

 

Fig. 2. Estimated rotor flux and position estimation error at 1000 r/min. (a) SOFO. (b) FOFO.

 

Fig. 3. Eestimated rotor flux and position estimation error at 1800 r/min. (a) SOFO. (b) FOFO.

 

Fig. 4. Estimated rotor flux and position estimation error at 50 r/min. (a) SOFO. (b) FOFO.

 

CONCLUSION:

In this paper, to further improve the performance of the flux observer to suppress DC bias and high order harmonics, a FOFO is proposed. Theoretical analysis shows that proposed FOFO has strong attenuation ability against the DC bias and harmonics, and motor parameters mismatch and additional interference can be avoided. Additionally, the parameters of FOFO are set through s-domain analysis. Moreover, for better implementation in digital systems, the structure of discrete FOFO is obtained. The effectiveness of the proposed FOFO has been verified at a 2.0-kW IPMSM sensorless drive. Compared with the SOFO sensorless drive, the proposed FOFO method has strong performance of suppressing stator voltage DC bias and stator current DC bias, and the proposed method has better suppression of stator resistance mismatch and q-axis inductance mismatch under the condition of stator current DC bias. The main advantages of the proposed FOFO are: 1) it is insensitive to DC bias; 2) it has strong suppression ability to high order harmonics; 3) it has high rotor position estimation accuracy. Our future research work will further study the application of FOFO in synchronous reluctance motors.

REFERENCES:

[1] S. Kim, J. Im, E. Song, and R. Kim, “A new rotor position estimation method of IPMSM using all-pass filter on high-frequency rotating voltage signal injection,” IEEE Trans. Ind. Electron., vol. 63, no. 10, pp. 6499-6509, Oct. 2016.

[2] H, Zhang, W. Liu, Z. Chen, S. Mao, T. Meng, J. Peng, and N. Jiao, “A time-delay compensation method for IPMSM hybrid sensorless drives in rail transit applications,” IEEE Trans. Ind. Electron., vol.66, no. 9, pp. 6715-6726, Sept. 2019.

[3] R. Antonello, L. Ortombina, F. Tinazzi, and M. Zigliotto, “Enhanced low-speed operations for sensorless anisotropic PM synchronous motor drives by a modified back-EKF observer,” IEEE Trans. Ind. Electron., vol. 65, no. 4, pp. 3069-3076, Apr. 2018.

[4] C. Li, G.Wang, G. Zhang, N. Zhao, and D. Xu, “Adaptive pseudorandom high-frequency square-wave voltage injection based sensorless control for SynRM drives,” IEEE Trans. Power Electron., vol. 36, no. 3, pp. 3200-3210, Mar. 2021.

[5] G. Zhang, G. Wang, H. Zhang, H. Wang, G. Bi, X. Zhang, and D. Xu, “Pseudo-random-frequency sinusoidal injection for position sensorless IPMSM drives considering sample and hold effect,” IEEE Trans. Power Electron., vol. 34, no. 10, pp. 9929-9941, Oct. 2019.

Reduced-Order Feedback Linearization for Independent Torque Control of a Dual Parallel-PMSM System

 ABSTRACT:

Connecting two PMSMs in parallel to a 2-level 3-leg inverter gives a way to build up a high power-density driving system using existing electronic devices. But this type of system has a nature of nonlinearity that creates an obstacle in high performance control and the original system cannot be feedback-linearized directly. This article presents a reduced-order feedback-linearization method. In the first place, an extra order-reducing step that separates the system as a main system and an auxiliary system is applied. Then a feedback-linearization method is applied to the reduced-order system. With these effort, the original system can be converted into a linear time-invariant system bringing the controller design problem into the linear domain. In the last step, a linear robust state-feedback controller is used to achieve the speed control as well as compensate the unmeasurable external load torque. An extensive experiment is given to verify the feasibility and good performance in a highly unbalanced load torque situation of the designed controller.

KEYWORDS:

1.      Parallel PMSM

2.       Robust control

3.       Feedback-linearization

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Figure 1. Proposed Controller Scheme.

EXPECTED SIMULATION RESULTS:

 

Figure 2. Speed And _D Response Of Speed Command Experiment.

 

Figure 3. Current Response Of Speed Command Experiment.

 

Figure 4. Speed And _D Response Of _D Command Experiment.

Figure 5. Current Response Of _D Command Experiment.

 CONCLUSION:

In this article, we have presented a two-step state-feedback controller for the MIDPMSM system such that the two machines are carried out in closed-loop systems for handling the highly unbalanced load torque situation. This article proposes a new way to linearize a nonlinear system if feedback-linearization cannot be applied directly. The major contribution can be summarized in three aspects. First of all, a state-space description for the MIDPMSM system is set up, and it is an affine nonlinear system with unknown inputs. And then, the original affine nonlinear system is linearized through two steps: order reducing and state-feedback linearization. With these two steps, the controller design problem is brought into the LTI system domain. Secondly, in the state-feedback linearization stage, the stability of the constrained two-dimensional subsystem (7) is fully considered and dealt with. Indeed, in order to keep its stability, the calculation of the disturbance compensation gain k is given by analyzing eigenvalue constraints through solving its characteristic polynomial. Thirdly, based on the reduced-order linearized system, a state feedback controller together with an integrator is designed. In this way, both goals, closed-loop stability and reference tracking, are reached. The experiment also proves that an open-loop machine can have the risk of becoming unstable when the "master-slave" method is used. The proposed controller can avoid this situation by putting both machines under closed-loop control. Although the proposed controller design method has shown its great advantages, at least one drawback of the controller is also left. This controller can hardly handle the singularity point of the system, which creates an obstacle. How to overcome the drawback becomes one of our next considerations.

 REFERENCES:

 [1] Z. Deng and X. Nian, ``Robust control of two parallel-connected permanent magnet synchronous motors fed by a single inverter,'' IET Power Electron., vol. 9, no. 15, pp. 2833_2845, Dec. 2016.

[2] J. M. Lazi, Z. Ibrahim, M. H. N. Talib, and R. Mustafa, ``Dual motor drives for PMSM using average phase current technique,'' in Proc. IEEE Int. Conf. Power Energy, Kuala Lumpur, Malaysia, Nov. 2010, pp. 786_790.

[3] A. A. A. Samat, D. Ishak, P. Saedin, and S. Iqbal, ``Speed-sensorless control of parallel-connected PMSM fed by a single inverter using MRAS,'' in Proc. IEEE Int. Power Eng. Optim. Conf., Melaka, Malaysia, Jun. 2012, pp. 35_39.

[4] A. Del Pizzo, D. Iannuzzi, and I. Spina, ``High performance control technique for unbalanced operations of single-vsi dual-PM brushles motor drives,'' in Proc. IEEE Int. Symp. Ind. Electron., Bari, Italy, Jul. 2010, pp. 1302_1307.

[5] J. M. Lazi, Z. Ibrahim, M. Sulaiman, I. W. Jamaludin, and M. Y. Lada, ``Performance comparison of SVPWM and hysteresis current control for dual motor drives,'' in Proc. IEEE Appl. Power Electron. Colloq. (IAPEC), Johor Bahru, Malaysia, Apr. 2011, pp. 75_80.

Peak Current Detection Starting based Position Sensorless Control of BLDC Motor Drive for PV Array Fed Irrigation Pump

ABSTRACT:

a single stage position sensorless control based solar power fed PMBLDC (Permanent Magnet Brushless DC) motor drive scheme for irrigation pump is proposed in this paper. The proposed system is designed without using any mechanical sensor to reduce the cost along with the complexity of the system with optimum utilization of the solar Photovoltaic (PV) power. The proposed system integrated with a PMBLDC motor drive coupled to a water pump is controlled by an inverter input voltage sensing based position sensorless control with high current detection and commutation point estimation based starting to wide speed range control .Elimination of position sensor, makes the system control compact and cheaper. The peak current estimation based starting in sensorless mode, enables soft starting restricting high starting current with reliability like sensor based operation. The proposed drive is tested and validated on a developed laboratory prototype and its suitability is justified with different test results under steady state and dynamic operating conditions.

KEYWORDS:

1.      Peak Current detection based starting

2.       Position sensorless control

3.      Incremental Conductance MPPT Algorithm

4.      PMBLDC motor drive

5.      Water pumping

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Fig.1 System configuration of position sensorless brushless DC motor drive operated aqua pumping

EXPECTED SIMULATION RESULT:

 

Fig.2 Solar PV Array Performance (a) Steady-state and Starting performance at 1000 W/m2 insolation (b) Dynamic performance varying from 500 W/m2 to 1000 W/m2

 

Fig. 3 BLDC motor performance at sensorless scheme(a)Zero starting and steady state performance at 1000W/m2 irradiance(b)Dynamic performance varying from 500 W/m2 to 1000 W/m2 irradiance

 

CONCLUSION:

Position sensorless control scheme of the BLDC motor has been presented for irrigation pump application. Sensorless control scheme has been justified for adverse environment application especially for rural areas. The performance of the proposed configuration has been evaluated satisfactory for water pumping application at different weather conditions.

REFERENCES:

[1] S. Jain, A. K. Thopukara, R. Karampuri and V. T. Somasekhar, “A Single-Stage Photovoltaic System for a Dual-Inverter-Fed Open-End Winding Induction Motor Drive for Pumping Applications, ” in IEEE Transactions on Power Electronics, vol. 30, no. 9, pp. 4809-4818, Sept. 2015

[2] L. An and D. D. Lu, “ Design of a Single-Switch DC/DC Converter for a PV-Battery-Powered Pump System With PFM+PWM Control, ” in IEEE Transactions on Industrial Electronics, vol. 62, no. 2, pp. 910- 921, Feb. 2015.

[3] J. V. M. Caracas, G. d. C. Farias, L. F. M. Teixeira and L. A. d. S. Ribeiro, “ Implementation of a High-Efficiency, High-Lifetime, and Low-Cost Converter for an Autonomous Photovoltaic Water Pumping System, ” in IEEE Transactions on Industry Applications, vol. 50, no. 1, pp. 631-641, Jan.-Feb. 2014.

[4] Tae-Hyung Kim and M. Ehsani, “Sensorless control of the BLDC motors from near-zero to high speeds, ” in IEEE Transactions on Power Electronics, vol. 19, no. 6, pp. 1635-1645, Nov. 2004.

[5] S. Dusmez, A. Khaligh, M. Krishnamurthy, E. Ugur and M. Uzunoglu, “Sensorless control of BLDCs for all speed ranges with minimal components, ”International Aegean Conference on Electrical Machines and Power Electronics and Electromotion, Joint Conference, Istanbul, 2011, pp. 626-631

Online Estimation Method of DC-Link Capacitors for Reduced DC-Link Capacitance IPMSM Drives

ABSTRACT:

 In order to extend the lifetime and save the system cost, the film capacitor is applied in the DC-link of IPMSM drives. Many active damping control methods have been carried out to improve the drive system stability, which need the accurate value of the DC-link film capacitor. In this letter, an online DC-link capacitance estimation method is investigated for reduced capacitance IPMSM drives, which does not need any additional signal injection or sensor. The power coupling characteristics are analyzed to obtain the instantaneous power of the DC-link capacitor from the inverter and the grid sides. The band-pass filter is applied to extract the DC-link voltage and capacitor power with twice the frequency of the grid voltage. The DC-link capacitance could be estimated by the fundamental component of the DC-link voltage. The proposed method can be used for different kinds of load types and motor types of the drive system. Experimental results are performed to verify the estimation method, and the estimation error is within 1%.

KEYWORDS:

 

1.      Online capacitance estimation

2.      Motor drive

3.      Online capacitance estimation

4.      Reduced DC-link capacitance

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

 

Fig. 1. Block diagram of DC-link capacitance estimation.

 EXPECTED SIMULATION RESULTS:

Fig. 2. Experimental results when the motor operates at 1800rpm. (a) Waveforms of grid power, inverter power, capacitor power and its fundamental component. (b) Waveforms of the DC-link voltage, the product of DC-link voltage and its derivative, and the fundamental component of the product M. (c) Detailed waveform of the fundamental component of capacitor power, M, and the estimated DC-link capacitance.

 

Fig. 3. Experimental results when the motor operates at 4800rpm. (a) Waveforms of grid power, inverter power, capacitor power, and its fundamental component. (b) Waveforms of the DC-link voltage, the product of DC-link voltage and its derivative, and the fundamental component of the product M. (c) Detailed waveform of the fundamental component of capacitor power, M, and the estimated DC-link capacitance.

 

Fig. 4. Experimental results when the motor operates at 3000rpm and the DC-link capacitance is 59.5μF. (a) Waveforms of grid power, inverter power, capacitor power and its fundamental component. (b) Waveforms of the DC-link voltage, the product of DC-link voltage and its derivative, and the fundamental component of the product M. (c) Detailed waveform of the fundamental component of capacitor power, M, and the estimated DC-link capacitance.

  

CONCLUSION:

As for the reduced DC-link capacitance IPMSM drive system, a real-online DC-link capacitance estimation method is investigated in this letter, which does not need an additional signal injection. The power coupling characteristics are analyzed, and the instantaneous DC-link capacitor power is obtained. The DC-link capacitance could be estimated by the ratio of the fundamental component of DC-link capacitor power and that of the product between DC-link voltage and its derivative term. Moreover, the proposed method only depends on the DC-link voltage and the instantaneous DC-link capacitor power, which benefits its application in other motor type and load type reduced DC-link capacitance motor drive system. Experimental results verify the effectiveness of the proposed DC-link capacitance estimation method, which could realize the estimation precision within an error of 1% for the several tens microfarad of DC-link capacitance.

REFERENCES:

 

[1] Y. Zhang, Z. Yin, J. Liu, R. Zhang and X. Sun, “IPMSM Sensorless Control Using High-Frequency Voltage Injection Method With Random Switching Frequency for Audible Noise Improvement,” IEEE Trans. Ind. Electron., vol. 67, no. 7, pp. 6019-6030, Jul. 2020.

[2] K. Liu and Z. Zhu, “Fast Determination of Moment of Inertia of Permanent Magnet Synchronous Machine Drives for Design of Speed Loop Regulator,” IEEE Trans. Control Syst. Technol., vol. 25, no. 5, pp. 1816-1824, Sept. 2017.

[3] J. Hang, H. Wu, S. Ding, Y. Huang and W. Hua, “Improved Loss Minimization Control for IPMSM Using Equivalent Conversion Method,” IEEE Trans. Power Electron., vol. 36, no. 2, pp. 1931-1940, Feb. 2021

[4] K. Abe, H. Haga, K. Ohishi and Y. Yokokura, “Current ripple suppression control based on prediction of resonance cancellation voltage for electrolytic-capacitor-less inverter,” IEEJ J. Ind. Appl., vol. 6, no. 1, pp. 1-11, 2017.

[5] Y. Zhou, W. Huang, and F. Hong, “Single-phase input variable-speed AC motor system based on electrolytic capacitor-less single-stage boost three-phase inverter,” IEEE Trans. Power Electron., vol. 31, no. 10, pp. 7043-7052, Oct. 2016.