Adaptive MF tuned fuzzy logic speed controller for BLDC motor drive using ANN and PSO technique

The Journal of EngineeringVolume 2019, Issue 17 p. 3947-3950 The 9th International Conference on Power Electronics, Machines and Drives (PEMD 2018)Open Access Adaptive MF tuned fuzzy logic speed controller for BLDC motor drive using ANN and PSO technique Amirthalingam Ramya, Corresponding Author Amirthalingam Ramya ramyaa@ssn.edu.in SSN College of Engineering, Tamilnadu, IndiaSearch for more papers by this authorMahadevan Balaji, Mahadevan Balaji SSN College of Engineering, Tamilnadu, IndiaSearch for more papers by this authorVijayarajan Kamaraj, Vijayarajan Kamaraj SSN College of Engineering, Tamilnadu, IndiaSearch for more papers by this author Amirthalingam Ramya, Corresponding Author Amirthalingam Ramya ramyaa@ssn.edu.in SSN College of Engineering, Tamilnadu, IndiaSearch for more papers by this authorMahadevan Balaji, Mahadevan Balaji SSN College of Engineering, Tamilnadu, IndiaSearch for more papers by this authorVijayarajan Kamaraj, Vijayarajan Kamaraj SSN College of Engineering, Tamilnadu, IndiaSearch for more papers by this author First published: 26 April 2019 https://doi.org/10.1049/joe.2018.8179Citations: 13AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract This work focuses on adaptive controller design using fuzzy Logic (FL) controller to get better dynamic performance under non-linearities and parameter uncertainties. In the proposed controller, the membership function (MF) parameters are tuned in line with the parameter variations of the motor. The steady state, regulatory and servo response of the proposed controller are analysed to highlight the merits of the controller. The simulation results indicate that the proposed controller possesses good tracking capability and faster response time in comparison with conventional schemes. 1 Introduction The Brushless DC motors are primarily used in servo, actuation, positioning and variable speed applications where precise motion control and stable operation are critical. This necessitates the design of an efficient controller to achieve continual control performance under varying operating conditions [1]. The performance of traditional motor controllers such as PI and proportional integral derivative (PID) controllers are affected by parameter variations, load disturbances and speed variations due to their fixed gain parameters [2-4]. Researchers have explored the application of sliding mode control (SMC)-based algorithms for electric drive applications. A major drawback in SMC is that it suffers from chattering problems which leads to the degradation of system performance. These drawbacks of the conventional control algorithms have opened up new avenue for researchers to experiment the design and analysis of controllers for electric drive applications based on artificial intelligence (AI) techniques. AI techniques such as fuzzy logic and neural network approaches have been found to be attractive for speed controller as they are model-free design methods. A comparison of robustness of different controllers for BLDC motor drive has been analyzed by Premkumar et al. (2015) [5]. In addition to conventional fuzzy and neuro-fuzzy systems, self-tuning fuzzy approaches have been proposed to enhance the self-adaptability of fuzzy controllers in the presence of external disturbances [6]. To make the system adaptive, the scaling factors, rule base and membership function parameters are tuned in line with the parameter variations of the motor. Such an approach is termed self-tuning fuzzy logic controller (FLC). Among the various self-tuning approaches, MF parameters tuning method has significant impact on parameter variations and hence is explored in this paper with the objective of designing a robust and adaptive controller. The merits of the proposed controller are analysed in terms of steady state, regulatory and servo responses. The proposed paper is organised as follows: The conventional FLC for BLDC motor drive system is presented in Section 2. The design of MF parameter tuned FLC is discussed in Section 3. Section 4 discusses the PSO and artificial neural network (ANN) approach for the proposed controller. Section 5 validates the simulation results and the results of comparison with various controllers are presented and Section 6 presents the conclusion. 2 Design of conventional FLC The block diagram of FLC-based speed regulation in BLDC motor is shown in Fig. 1. Fig. 1Open in figure viewerPowerPoint Block diagram of FLC The drive consists of fuzzy logic (FL) speed controller, hysteresis current controller, hall sensor, voltage source inverter and BLDC motor. The FLC has two inputs, error (e) and change in error (ce) represented as (1) (2) The membership functions of the controller inputs e and ce defined on the normalised domain as [−1, 1]. The input speed error (e) and change in speed error (ce) to the FLC are divided into three linguistic variables: negative (N), zero (Z), positive (P). The linguistic variables for the output are L, M, and H represent low, medium, and high, respectively, as shown in Fig. 2. Here the rule base for FLC is presented in Table 1. The actual control output is obtained from the defuzzification method to get the crisp output. Fig. 2Open in figure viewerPowerPoint Membership functions for (a) error and change in error, (b) output variable Table 1. Rule base for output variable e /Δe N Z P N L L M Z L M H P M H H The literatures reveal that the performance of FLC is influenced by the choice of MF parameters. The performance of FLC can be improved by proper tuning of these parameters. The optimal tuning mechanism for MF parameters is presented in following sections. 3 Membership function parameter tuned FLC for BLDC motor drive The block diagram of MF tuned FLC for BLDC motor drive system is depicted in Fig. 3. The FLC is categorised into two inputs and one output which are related by MFs. The MF parameters corresponding to e and ce are a 1, a 2, b 1, b 2, c 1 and c 2. The starting and ending parameters a 1 and c 2 are fixed normalised values [ − 1,1] and the remaining intermediate parameters b 1, a 2, c 1, b 2 are tuned for minimal error. Fig. 3Open in figure viewerPowerPoint Block diagram for MF tuned fuzzy logic controller Tuning is done based on particle swarm optimisation (PSO) for better computational efficiency and robustness [7] (Fig. 4). Fig. 4Open in figure viewerPowerPoint Membership functions tuning factors for e and ce The objective function to determine the optimal MF parameters is based on Integral Square Error (ISE) and is represented in (3) (3) 3.1 Determination of optimal MF parameters using PSO Particle swarm optimisation is a population based stochastic optimisation technique inspired by flocking and schooling patterns of birds and fish. The searching process can be artificially simulated for solving non-linear optimisation problem. In PSO, the particles constitute a swarm moving around in an N -dimensional search space looking for the best solution. To determine the optimal solution, a flock of particles are swept over a wide range specified by the bounds [8]. During every generation, each particle follows the coordinates in the problem which are related to the best solution of the particle termed as 'P best' and the global best value for any particle termed as 'G best'. A new velocity value for each particle is calculated based on its current velocity, P best and G best. The new velocity value is then used to update the position of the particle in the search space. This process is iterated until the desired stopping criterion is achieved [9]. The velocity of each particle is updated by the following equation: (4) Here the velocity which is close to P best and G best can be calculated. The searching point can be modified by the following equation: (5) The inertia weight is given as (6) where xk, particle's position; xk +1, particle's individual best solution; vk is the current velocity; vk +1, modified velocity; P best, the best solution observed by current particle; G best, the best solution of all particles; C 1, C 2 -positive constants; W, inertia weight; rand, random generated number; K, current number of iterations; k max, maximum number of iterations; w max, maximum weight; w min, minimum weight. The process flow of PSO algorithm is represented in flowchart shown in Fig. 5. Fig. 5Open in figure viewerPowerPoint Flowchart for PSO tuned fuzzy logic controller The PSO algorithm is coded in MATLAB environment with the parameters shown in Table 2. The MF parameters are obtained using PSO algorithm for different speed and load conditions. The variation of MF parameters with respect to speed and load change is depicted through Fig. 6. From the figures, it is evident that the optimum MF parametrs vary with respect to speed and load variations. Table 2. Parameters of PSO algorithm number of particles 30 maximum iteration 50 no. of variables 4 acceleration constant (C 1, C 2) 1.4 Fig. 6Open in figure viewerPowerPoint Data sets of MF parameters The data sets of optimal MF parameters b 1, a 2, c 1, b 2 which are obtained from the PSO algorithm for various speed and load torque is trained using ANN. The trained ANN when incorporated in the controller will interpolate the values for the whole range of speed and load torque. 3.2 ANN method for training fuzzy membership function The neural network model for estimating the controller parameters is shown in Fig. 7 which has three hidden layers with six neurons in each layer. The input parameters to the network are speed and load torque whereas the output parameters are the optimised MF parameters. Fig. 7Open in figure viewerPowerPoint ANN model for training fuzzy membership function The essential equations of ANN model training are summarised as follows. The net input of the j th neuron of the hidden layer is given by (7) The output of the j th neuron from the hidden layer is given as (8) The net input of the k th neuron of the hidden layer is given by (9) The output of the k th neuron from the output layer is given as (10) In this proposed work, back-propagation algorithm is applied to train the network. The main objective of the training algorithm is to adjust the weights to reduce the error [10]. Back Propagation algorithm performs gradient descent method for minimising error. To minimise error, the sensitivity to each of the weights must be calculated. The initial weights and the biases for the backpropagation network are randomly chosen. The weights of the output layer and hidden layer are updated by the following equations: (11) (12) where Wij, weight between the i th neuron at input layer and j th neuron at hidden layer; Ii, i th input; N, number of inputs; Bj, bias of the j th neuron at hidden layer; F, non-linear transfer function; M, number of neurons in the hidden layer; Wjk, weight between the j th neuron in hidden layer and k th neuron in output layer, Bk, bias of the kth neuron at output layer, η, learning rate; δk (t), local gradient at the output layer; δj (t), local gradient at the hidden layer. With the above equations, the ANN training method is carried out in MATLAB neural network toolbox to compute the desired value of MF parameters. The performance of BLDC motor drive with MF tuned FLC and their comparisons with other controllers are discussed in the forthcoming section. 4 Results and discussion The aforementioned adaptive FLC technique discussed are implemented in MATLAB/Simulink platform and the results are compared with the conventional fuzzy controller. The response of the proposed adaptive FLC is investigated under steady state, regulatory and servo states through its voltage, current, speed and torque waveforms. The simulated response of BLDC motor drive with MF tuned FLC is portrayed in Fig. 8 when it undergoes a step change in load from 0.5 to 1 N m at t = 0.1 s and a voltage disturbance created at 0.3 s with a load torque of 1 N m. Fig. 8Open in figure viewerPowerPoint Simulated response of BLDC motor drive with MF tuned FLC at 3200 rpm The time response analysis of the proposed as well as the conventional controllers following a load disturbance is portrayed in Fig. 9. A thorough observation of the figure shows that the time domain constants are satisfactory for MF tuned FLC amidst all other controllers. The rise time, peak time and settling time for MF tuned FLC are recorded as 0.005, 0.01 and 0.016 s, respectively. The comparison of steady-state error of proposed controller with conventional FLC for various loading conditions at 3200 rpm is presented in Table 3. Fig. 9Open in figure viewerPowerPoint Time response analysis of speed controllers in BLDC motor Table 3. Steady-state error for proposed controller at 3200 rpm S. No. Load, N m Steady-state error, rpm MF tuned FLC FLC 1 0.25 1.2148 3.4124 2 0.5 1.3248 5.1881 3 0.75 1.4567 6.1823 4 1 1.5123 7.5133 5 Conclusion In this work, self-tuning fuzzy logic based speed control algorithms have been proposed to regulate the speed of BLDC motor drive. The self-tuning approaches tune the MF parameters of FLC to make the controller adaptive. Simulation of the two proposed control methodologies have been investigated under steady state, regulatory and servo states through its voltage, current, speed and torque waveforms besides their time response specifications. The performance analysis of controllers indicates that MF parameter tuned FLC has better time response and less steady-state error with fast tracking and it is robust to load perturbation when compared to conventional FLC. 6 References 1Krishnan R.: ' Permanent magnet synchronous and brushless dc motor drives' ( CRC Press, Boca Raton, FL, USA, 2010) 2Pillay P., Krishnan R.: 'Modeling, simulation and analysis of permanent magnet motor drives', IEEE Trans. Ind. Appl., 1989, 25, (2), pp. 274 – 279 3Wu H.X., Cheng S.K., Cui S.M.: 'A controller of brushless DC motor for electric vehicle', IEEE Trans. Magn., 2005, 41, (1), pp. 509 – 513 4Shanmugasundaram R., Zakariah K.M., Yadaiah N.: 'Implementation and performance analysis of digital controllers for brushless DC motor drives', IEEE Trans. Mechatronics, 2014, 19, (1), pp. 213 – 224 5Premkumar K., Manikandan B.V.: 'Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor', Eng.Sci. Technol., 2015, 5, (19), pp. 818 – 840 6Ping H., Liu Y.T.: 'Integrated design of speed sensorless and adaptive speed controller for a brushless DC motor', IEEE Trans. Power Electron., 2006, 21, (2), pp. 518 – 523 7Huang M., Lin H., Yunkai H. et al.: 'Fuzzy control for flux weakening of hybrid exciting synchronous motor based on particle swarm optimization algorithm', IEEE Trans. Magn., 2012, 48, (11), pp. 2989 – 2992 8Valdez F., Melin P., Castillo O.: 'A survey on nature-inspired optimization algorithms with fuzzy logic for dynamic parameter adaptation', Expert Syst. Appl., 2014, 41, (14), pp. 6459 – 6466 9Li X., Yao X.: 'Cooperatively coevolving particle swarms for large scale optimization', IEEE Trans. Evol. Comput., 2012, 16, (2), pp. 210 – 224 10Hasanien H.M.: 'FPGA implementation of adaptive ANN controller for speed regulation of permanent magnet stepper motor drives', Energy Conversation Manage., 2011, 52, (2), pp. 1252 – 1257 Citing Literature Volume2019, Issue17June 2019Pages 3947-3950 FiguresReferencesRelatedInformation