Role of optimization algorithms based fuzzy controller in achieving induction motor performance enhancement

Three-phase induction motors (TIMs) are widely used for machines in industrial operations. As an accurate and robust controller, fuzzy logic controller (FLC) is crucial in designing TIMs control systems. The performance of FLC highly depends on the membership function (MF) variables, which are evaluated by heuristic approaches, leading to a high processing time. To address these issues, optimisation algorithms for TIMs have received increasing interest among researchers and industrialists. Here, we present an advanced and efficient quantum-inspired lightning search algorithm (QLSA) to avoid exhaustive conventional heuristic procedures when obtaining MFs. The accuracy of the QLSA based FLC (QLSAF) speed control is superior to other controllers in terms of transient response, damping capability and minimisation of statistical errors under diverse speeds and loads. The performance of the proposed QLSAF speed controller is validated through experiments. Test results under different conditions show consistent speed responses and stator currents with the simulation results.


Reviewer #1
I read this paper with a lot of interest. Intelligent control is a fascinating subject and this paper presents a new optimization technique that may be used to improve the fuzzy logic controller of three-phase induction motors. I like the description of the fuzzy controller and the steps of the fuzzy system. The paper has merit and is technically sound. One of the strong points of the paper is its plethora of results. However, there are some limitations that need to be addressed: Comment 1: The title is a little bit off the content. It talks about machine performance but it is only tested in TIM. Furthermore, the authors discuss the "role of optimization algorithms" but in fact this paper highlights the QLSAF algorithm. In addition, the paper content is mainly for optimizing a fuzzy logic controller, something that is not reflected in the title. Therefore, the title does not fully express the paper content.
Authors Response: Thank you for your comments. Based on suggestion, we have revised the title into 2 (two) new title as follows;

Role of Quantum Lighting Search Algorithm Based Fuzzy Controller toward Induction Motor Performance Enhancement
We have included title no. 1 in the manuscript to highlight the impact of optimized algorithm in induction motor performance and readability. However, if you find that the title no. 2 is more suitable, we will fix the title 2.

Test 1: Constant Torque with Speed Variation
The first test involves increasing or decreasing the reference speed while maintaining a fixed torque. This case study aims to evaluate the performance of the proposed FLC and to estimate the reference speed variation with the constant torque of the TIM controlled by the V/F ratio. V/F control generally exhibits weak performance in low-speed applications. However, V/F ratio controls 25%-100% of the nominal speed of the TIM. The performance of the developed FLC in terms of reference speed involves step responses. The performance of the TIM drive during step response change is determined under the condition of a constant torque load applied on the TIM rotor shaft. By contrast, the no-load condition is applied to the TIM with variable speed in short periods, as illustrated in Fig. 6. A controller is used to sustain TIM performance. This study proposes a unique robust controller structure to indicate the speed responses of QLSAF, LSAF, BSAF, GSAF, PSOF, and PID with a nearly perfect speed change. The induction motor applies the reference speed change several times, as presented in Table 8. Table 8 shows the speed response, which varies based on specific durations, and the overshoot (%) values. The maximum overshoot is calculated as maximum overshoot (%) = ( ) × 100%. QLSAF successfully achieves the best result compared to the other optimization algorithms in terms of maximum overshoot values and settling time. QLSAF achieves better responses than that of LSAF, BSAF, GSAF, PSOF, and PID in terms of minimizing overshoot values, settling time, steady-state error, and damping ratio. After each change, QLSAF establishes excellent rapid stability during each speed change. None of these results can be obtained without a perfect controller, such as the one proposed in this study. Fig. 7 shows that the stator current signal during the start-up of the TIM involves a high current pull and, subsequently, stable signals. The changes in the frequency of the peak value are also fixed during the duration of sudden changes in speed based on system requirements. Controlling speed change corresponds to a change in supply frequency.

Test 2: Constant Speed with Torque Variation
This test aims to determine system performance and robustness of the proposed controller at full rotor speed with changes in mechanical load is evaluated. The results obtained are shown in Fig. 8, which also displays the speed response and its zoomed locations for each step when the load changes. The estimated speed is considered consistent with the actual speed with good accuracy. In terms of the steady-state error between the reference and actual speeds and damping minimization, QLSAF obtains a better response than LSAF, BSAF, GSAF, PSOF and PID. Fig. 9 presents the stator currents. Constant frequency and variable peak values are modified by step changes in mechanical load for a specific duration. Table 9 lists the mechanical load variations according to specific time durations, settling time and overshoot (%) values. QLSAF achieves the lowest overshoot values among the controllers, and these values allow for inducing the best response.

Time (sec) Stator currents (A)
Comment 3: I strongly suggest that the authors add a block diagram of the QSLAF algorithm or its pseudocode. This way it would facilitate the reader to understand it and reproduce it.
Authors Response: Thank you for your comments. The block diagram of QLSA is presented in the supplementary file ( Fig. 1).

Quantum Lightning Search Algorithm
The implementation of QLSA is demonstrated in the flow diagram and there are some sections with the gray shadow which shows the main contributions over the LSA algorithm as described in Fig 1. The gray shadow which is illustrated in the flow diagram is added to improve the LSA computational intelligence by calculating the initial population, factor and MeanBest for each projectile.

Comment 4:
The test dataset is not described and no details are given. Therefore, the reader cannot understand the difficulty of the testing problem. In addition, a block diagram of the TIM is useful to the reader.
Authors Response: Thank you for your comments. The fourteen benchmark functions were tested using the value of dimension problems, search space and function minimum, as shown in Table 1. We have added the block diagram of the TIM in the supplementary file (Fig. 10).

Authors action in the manuscript:
The third stage describes the control rules and linguistic terms of fuzzy logic to make the appropriate decisions for TIM. Generally, the inference systems are structured either using Mamdani method or Takagi-Sugeno method. In this study, Mamdani is applied because of its simple design and structure. The fuzzy rules are established using the if-then linguistic term, and output MFs are determined between the inputs (e, de) and the output ( ). A total of 49 rules are developed for controlling TIM as listed in Table 2 and illustrated in the following equations:  (F11, F12, F13 and F14). The results illustrate that the best global minimum for QLSA is found in F11 and F12, and the near-global minimum is achieved in other functions.
Comments 13: Lastly, I would like to ask and this is something that the authors have to clarify whether the QLSAF algorithm can be efficient for other type of controllers or only for fuzzy? If yes, the paper contribution is very narrow.
Authors Response: Thank you for your comments. QLSA is a novel algorithm introduced in this study which is developed from a novel lightning search algorithm (LSA) using quantum mechanics theory to generate a quantuminspired LSA (QLSA). The QLSA improves the search of each step leader to obtain the best position for a projectile using an exponential distribution through the global convergence and by calculating the mean best position. Yes, QLSA can be efficiently use in other type of controllers. QLSA is the advanced form of LSA. We found that LSA works effectively not only in the fuzzy controller but also operates satisfactorily in the PID controller and machine learning algorithms. We have added two prominent articles for your clarifications.

Reviewer #2
The authors of the paper describe their proposed approach for Role of Optimization Algorithms in Achieving Efficient Machine Performance. The topic is interesting and with possible applicability. However, the paper needs several improvements:

Comment 1:
The main contribution and originality should be explained in more detail, optimization of fuzzy controllers?
Authors Response: Thank you for your comments. We have extended the contribution and originality of the paper.

Authors action in the manuscript:
The significant contributions of this research are summarised, as follows: i. A novel QLSA is introduced and compared with other optimization techniques by using different benchmark functions. Firstly, this research develops a novel LSA using quantum mechanics theory to generate a quantuminspired LSA (QLSA). The QLSA improves the search of each step leader to obtain the best position for a projectile using an exponential distribution through the global convergence and by calculating the mean best position. Secondly, the proposed QLSA is applied to a group of fourteen benchmark functions and validated with different tests. The obtained results are QLSA compared with LSA, backtracking search algorithm (BSA), gravitational search algorithm (GSA) and particle swarm optimization (PSO). ii. An optimal QLSA-based FLC (QLSAF) speed controller is employed to tune and minimize the OF, thereby iii. The prototype of the QLSA-based fuzzy (QLSAF) speed controller is implemented in a low-cost single-chip DSP-TMS320F28335 control board. A suitable prototype is designed using DSP-TMS320F28335 control board along with three-phase inverter, related gate driving circuit, rotary encoder connect circuit, motor selector circuit, analogue-digital conversion (ADC), enhanced quadrature encoder pulse (eQEP) and enhanced pulse width modulation (ePWM) blocks. The implementation of the QLSAF speed controller is carried out in V/f control with pulse width modulation (PWM) switching technique and DSP-TMS320F28335 control board. iv. The proposed method is validated by experiments, and the results of the simulation and experimental system are consistent with that of the TIM drive system. The results validate and confirm the implementation of the proposed algorithm in a multi-induction motor drive.

Comment 2:
The motivation of the approach with needs further clarification.
Authors Response: Thank you for your comments. We have clarified the motivation of our research.

Authors action in the manuscript:
The conventional controller, namely, proportional-integral-derivative (PID) has been widely applied to adjust the main parameters of TIM, including rotor flux, torque, speed, current and voltage 21,22 . However, PID has negatives in terms of appropriate parameter selection due to the trial-and-error (TE) considerations. The artificial intelligence (AI) based controllers including artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) have been performing satisfactorily in motor applications such as fault identification 23 , speed assessment 24 and harmonics and torque ripple minimization 25 . However, the AI-based controllers have drawbacks concerning huge data requirement, long learning and training duration. Fuzzy logic controller (FLC) is extensively utilised in real-time TIM control using adaptive modelling under sudden changes 26-28 . Furthermore, FLC can operate in highly linear and nonlinear systems without considering any mathematical model 28,29 . Nevertheless, the accuracy of FLC depends on the suitable design and the optimal number of membership functions (MFs), as well as appropriate fuzzy rule generation 30 . Generally, a TE procedure is used to determine these variables; however, this procedure causes a substantial delay in control operation 31 .
The execution of TIM drive through the experimental platform is carried out using dSPACE, field-programmable gate array (FPGA), or digital signal processor (DSP). The dSPACE and FPGA have illustrated effectiveness in the implementation of grid-integrated voltage source inverter 51 and five-phase voltage source inverter 52 , respectively. Nevertheless, dSPACE and FPGA have shortcomings in terms of cost and working method that cannot operate on a standalone basis. In contract, DSP offers benefits with regard to cost-effectiveness, low power consumption, fast computational capability, and embedding processor 53,54 and has been excellent in operating TIM drive 55 and permanent magnet synchronous motor (PMSM) 56 .
Comment 3: Discussion of related work in optimization of fuzzy control should be expanded with more recent work.
Authors Response: Thank you for your comments. We have added a few established references related to optimization of fuzzy control in induction motor drive.

Authors action in the manuscript:
Ali et al. 37

Authors action in the manuscript:
Comment 4: Minor grammar and syntax issues need correction.
Authors Response: Thank you for your comments. We have carefully checked the grammar and syntax issues accordingly.
Comment 5: More simulation results and formal comparison of results are needed.
Authors Response: Thank you for your comments. The comparison between QLSA based fuzzy controller and PID controller is shown in the supplementary file. We carried out two experiments (1) constant torque with speed variation and (2) constant speed with torque variation.

Comparison between QLSA and PID controllers
The effectiveness and robustness of the proposed QLSA-FLC in comparison to PID controllers is assessed under two experiments (1) constant torque with speed variation and (2) constant speed with torque variation.

Test 1: Constant Torque with Speed Variation
The first test involves increasing or decreasing the reference speed while maintaining a fixed torque. This case study aims to evaluate the performance of the proposed FLC and to estimate the reference speed variation with the constant torque of the TIM controlled by the V/F ratio. V/F control generally exhibits weak performance in low-speed applications. However, V/F ratio controls 25%-100% of the nominal speed of the TIM. The performance of the developed FLC in terms of reference speed involves step responses. The performance of the TIM drive during step response change is determined under the condition of a constant torque load applied on the TIM rotor shaft. By contrast, the no-load condition is applied to the TIM with variable speed in short periods, as illustrated in Fig. 6. A controller is used to sustain TIM performance. This study proposes a unique robust controller structure to indicate the speed responses of QLSAF, LSAF, BSAF, GSAF, PSOF, and PID with a nearly perfect speed change. The induction motor applies the reference speed change several times, as presented in Table 8. Table 8 shows the speed response, which varies based on specific durations, and the overshoot (%) values. The maximum overshoot is calculated as maximum overshoot (%) = ( ) × 100%. QLSAF successfully achieves the best result compared to the other optimization algorithms in terms of maximum overshoot values and settling time. QLSAF achieves better responses than that of LSAF, BSAF, GSAF, PSOF and PID in terms of minimizing overshoot values, settling time, steady-state error and damping ratio. After each change, QLSAF establishes excellent rapid stability during each speed change. None of these results can be obtained without a perfect controller, such as the one proposed in this study. Fig. 7 shows that the stator current signal during the start-up of the TIM involves a high current pull and, subsequently, stable signals. The changes in the frequency of the peak value are also fixed during the duration of sudden changes in speed based on system requirements. Controlling speed change corresponds to a change in supply frequency.

Test 2: Constant Speed with Torque Variation
This test aims to determine system performance and robustness of the proposed controller at full rotor speed with changes in mechanical load is evaluated. The results obtained are shown in Fig. 8, which also displays the speed response and its zoomed locations for each step when the load changes. The estimated speed is considered consistent with the actual speed with good accuracy. In terms of the steady-state error between the reference and actual speeds and damping minimization, QLSAF obtains a better response than LSAF, BSAF, GSAF, PSOF, and PID. Fig. 9 presents the stator currents. Constant frequency and variable peak values are modified by step changes in mechanical load for a specific duration. Table 9 lists the mechanical load variations according to specific time durations, settling time, and overshoot (%) values. QLSAF achieves the lowest overshoot values among the controllers, and these values allow for inducing the best response.    Authors Response: Thank you for your comments. We have elaborated the conclusions with more future works.

Authors action in the manuscript:
The proposed controller for the TIM drive is implemented in a single chip DSP-TMS320F28335 control board which is considered novel and effective. However, following proposals can be considered for future works and developments: i. A method can be developed to convert FLC into a formula or transfer function. This method can be implemented in an extensive range of controllers to reduce the long computational process in FLC. ii.
New direct torque control can be designed and implemented to control a multi induction motor drive. iii.
The developed optimization techniques can be applied to other controllers such as fuzzy type-2 control, model-free control or hybrid FLC-PI control, to enhance the control of the multi-induction motor drive system. iv.
The developed controller can be implemented on a multi DC motor or multi permanent magnet synchronous motor drive to minimize the manufacturing cost of the control system. Authors Response: Thank you for your comments. We have included the aforementioned references accordingly.
Authors action in the manuscript: