The use of Fuzzy Logic Controller (FLC) as speed controller for Induction Motor (IM) drives is garnering strong researchers’ interest since it has proven to achieve superior performance compared to conventional controllers. The aim of this study is to review and investigate the design, operations, and effects of rules reduction for FLC in IM drives. Based on the literature, the most commonly used technique to design FLC Membership Functions (MFs) rule-base and control model is based on engineering skills and experienced behavioral aspects of the controlled system. Simplified fuzzy rules approaches have been introduced to reduce the number of fuzzy rules in order to realize hardware implementation. This study discusses different simplified rules methods applied to IM drives. Most of the proposed methods shared a common drawback in that they lacked systematic procedures for designing FLC rule base. Therefore, this research proposed a methodological approach to designing and simplifying the FLC rule-base for IM drives based on dynamic step response and phase plane trajectory of the second order representation of IM drives systems. The proposed method presents guidance for designing FLC rule-base based on general dynamic step response of the controlled system. Following the proposed method procedures, a (9, 25, 49) rules size has been designed and simplified to a (5, 7, 9) rules size. The effectiveness and accuracy of the designed rules as well as the simplified rules were verified by conducting simulation analysis of IM drives using MATLAB/Simulink environment. Step speed command performance comparisons were achieved with both standard designed and simplified rules at various speed demands. The simulation results showed that the simplified rules maintain the drive performance and produced similar behavior as the standard designed rules.

High-performance Induction Motor (IM) drives require fast dynamic responses, robust to parameter variations, withstand load disturbance, stable control systems and support easy hardware/software implementation. Fuzzy Logic Control (FLC) for speed controllers is garnering attention from researchers since it is proven to produce better results compared to conventional PI speed controllers. However, fixed parameter FLC experience performance degradation when the system operates away from the design point, or is affected by parameter variations or load disturbances. The purpose of this paper is to design and implement a simple Self-Tuning Fuzzy Logic Controller (ST-FLC) for IM drives application. The proposed self-tuning mechanism is able to adjust the output scaling factor of the main FLC speed controller by improving the accuracy of the crisp output. The induction motor drive employed an Indirect Field Oriented Control (IFOC) method fed by a Hysteresis Current Controller (HCC). The fixed parameter FLC for the main speed controller comprises 9 rules that are tuned to achieve the best performance. Then a simple self-tuning mechanism is applied to the main fuzzy logic speed controller. All simulation work was done using Simulink and fuzzy tools in the MATLAB software. The effectiveness of the proposed controller was investigated by conducting a comparative analysis between fixed parameter FLC and ST-FLC in forward and reverse speed operations, with and without load disturbances. Finally, an experimental testing was carried out to validate the simulation results with the aid of a digital signal controller board, dSPACE DS1104, with an induction motor drive system. Based on the results, ST-FLC showed superior performance in transient and steady state conditions in terms of various performance measures such as overshoot, rise time, settling time and recovery time.

Data-driven control approaches of permanent magnet synchronous machines (PMSMs) have gained significant attention due to their ability to eliminate reliance on analytical machine models and parameters. Reinforcement learning (RL) has emerged as a viable method for achieving a data-driven current control of the PMSM drive without the necessity of machine parameter information. In this approach, RL is trained offline to learn an optimal control policy, resulting in a computationally efficient controller compared to other data-driven control methods. However, standard RL methods struggle to adapt to new operating conditions and different parameter sets, reducing system performance and robustness. This research proposes a multi-set robust reinforcement learning (MSR-RL) based current control method for PMSM drives. MSR-RL leverages multi-task RL to optimize a single policy that can generalize and provide robust performance across multiple parameter sets. The parameter sets, referred to as contexts, are represented as Contextual Markov decision processes (CMDPs), capturing the dynamics associated with each parameter set. During the training phase, CMDPs with shared information are clustered into models. These models are then utilized to generate a unified policy that remains robust to all clustered and unseen models. The effectiveness of MSR-RL is validated through comparison with standard RL based on numerical simulations, experimental tests, and robustness evaluation. The findings highlight the advantages of MSR-RL in terms of adaptability, robustness, and performance of PMSM current control.

Fuzzy logic controls (FLCs) have emerged as a promising solution for speed regulation in induction motor (IM) drives, offering adaptability to non-linearities, parameter variations, and external disturbances. However, conventional FLCs with fixed parameters and a huge number of rules can limit adaptiveness and increase system complexity, leading to deteriorated performance and high computational requirements. Moreover, reliance on costly encoders in traditional sensor-based IM drives introduces measurement errors and contributes toward the overall cost. To tackle these challenges, this paper proposes an integrated sensorless IM drive with a simplified self-tuning FLC (ST-FLC) and data-driven reinforcement learning (RL) for speed estimation. By employing a simplified 9-rule FLC instead of an intensive 49-rule counterpart and integrating a simple self-tuning mechanism based on mathematical equations, adaptiveness is maintained while computational overhead is reduced. Furthermore, the adoption of RL-based sensorless speed estimation eliminates reliance on encoder data, offering a cost-effective and computationally efficient alternative. Unlike conventional sensorless methods, the proposed sensorless-RL approach is data-driven and does not rely on motor parameters, leveraging a pre-trained policy for efficient speed estimation. Validation through simulation and experimentation on the dSPACE DS1104 platform demonstrates the efficacy of the proposed ST-FLC Sim 9-rule with sensorless RL. The method showcases accurate speed estimation, with simulation results comparable to standard 49-rule FLC and superior experimental performance. Significant computational time reduction is achieved with the proposed approach, resulting in a notable improvement in experimental performance metrics. Specifically, reductions of 50.5%, 20.4%, 15%, and 14.9% in settling time, current ripples, torque ripples, and current harmonics, respectively, underscore the practical benefits of the proposed integrated ST-FLC Sim 9-rule with sensorless-RL IM drive system.

Fuzzy Logic Controller (FLC) as speed controller is preferred in many AC machine drives, due to its ability to handle model non-linearity, speed variations and parameters change. Additionally, Self-Tuning FLC (ST-FLC) is a modified FLC controller to overcome the issues associated with a fixed parameter FLC and to avoid performance degradation of the machine drive. It can update the FLC parameters in accordance to any variation, changes or disturbances that may occur to the drive system. However, FLC system requires huge computation capacity which increases the computational burden of the overall machine drive system and may result in poor performance. This research proposed a simple ST-FLC mechanism to tune the main FLC speed controller. Three different rule-size of FLC (9, 25, and 49) rules are implemented with ST-FLC mechanism based Induction Motor (IM) drive. Performance comparison of the three different rule-size based ST-FLC is conducted based on simulation and experimental analysis. In addition, a computational effort is technically analyzed and compared for the three different rule-size. In the experiment, ST-FLC with less number of rules (9-rules) shows superior performance, lower sampling and lower computational efforts compared to ST-FLC with higher rule-size (25, 49) rules.

Analysis and investigation of different advanced control strategies for high-performance induction motor drives