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Performance analysis of PID controller-based metaheuristic optimisation algorithms for BLDC motor

ABSTRACT Today, the use of permanent magnet brushless DC (PMBLDC) motors in vehicles is increasing due to the characteristics of sensorless operation. PMBLDC motor controllers can control the speed and position in the closed-loop feedback system without the need for a position sensor mounted on the shaft. Proportional – Integral – Derivative (PID) controller is one of the most common feedback control algorithms used in PMBLDC motor controllers. Optimizing problems using deterministic methods such as Lagrange and simplex methods requires basic information and complex calculations. Meta-heuristic algorithms are a type of stochastic algorithm that is used to find the optimal solution. Meta-heuristic algorithms are divided into three general categories: evolutionary algorithms, swarm intelligence algorithms, and stochastic algorithms. In this paper, using 14 metaheuristic optimisation algorithms, PID control parameters including settling time, time rise, overshoot, and stability of step response of the mentioned system are optimised. In this paper, 14 meta-heuristic algorithms are simulated and evaluated to optimise the PID coefficients of the controller, including settling time, rising time, excessive increase, and step response stability. The simulation result shows that the genetic algorithm (GA) has the best performance in terms of cost function and biogeography-based optimisation (BBO) in terms of settling time and rising time parameters. Finally, the simulation results are confirmed using experimental results.

Maximum torque per ampere controlled induction motor drive with reduced DC link capacitor

ABSTRACT This paper presents a three-phase induction motor (IM) drive comprising a single-phase Pulse Width Modulated (PWM) converter with an active power decoupling (APD) as an active front-end converter (AFEC) to reduce the size of a DC link capacitor. Consequently, film capacitors can be used in place of bulky electrolytic capacitors that improve the power density, reliability, and cost of AFECs. A control scheme for an APD circuit is developed to mitigate the ripple power, and to control the three-phase IM, a maximum torque per ampere (MTPA) control scheme is implemented. A Simulink model of the proposed system rated for 3.73 kW, 200 V, and 50 Hz three-phase IM drive and its equivalent prototype model are developed in a laboratory and the APD and the MTPA control schemes are implemented with the help of STM32F407VG, a 32-bit microcontroller and dSPACE 1104, respectively. This paper demonstrates the experimental verification of the concepts for APD and MTPA control. Further, a significant reduction in motor current at starting/light load conditions is observed that leads to lower energy requirements. The prospective application of these schemes includes railway traction drive, electric vehicles, and so on, where compactness, reliability, and lower energy requirements are the prime concerns.

High gain modified SEPIC converter with asymmetrical switched inductor super lift cell for renewable energy applications

ABSTRACT This paper provides a modified single-ended primary inductor converter (SEPIC) with an asymmetrical switched inductor super lift cell (ASISLC) for renewable energy applications. Solar, fuel cells, and so on, provide low output voltage, which is not sufficient for high voltage dc grid or dc loads. Therefore, the existence of a high gain DC-DC converter is mandatory. The proposed converter provides high gain, high efficiency, and less voltage stress at a low duty cycle. The proposed converter incorporates switched inductor together with a super lift cell. The super lift cell has a charge pump capacitor is connected in an asymmetrical fashion. The proposed converter offers continuous current at the input together with the above-mentioned advantages. This paper explains the operation of the ASISLC SEPIC converter in continuous conduction mode (CCM) and discontinuous conduction mode (DCM). The operating principle, theoretical analysis, design process, and comparison of the suggested converters with converters from the literature that use similar operating principles are all discussed in this paper. A MATLAB simulation is done and their results are provided. Finally, a hardware prototype for 200W, 50 kHz switching frequency is built and their results are validated to authenticate the effectiveness of the proposed converter.

Sensorless Adaptive control of VSI-Fed Induction Motor Drive with Optimized MLP-Neural Network

ABSTRACT Multilayer-Perceptron Neural Network (MLP-NN)-based sensorless speed control of adaptive Indirect Field-Oriented Control (IFOC) strategy is implemented for online parameter estimation of Induction Motor Drive (IMD) fed from Common mode voltage injection Space vector PWM (CVSVPWM) based Voltage Source Inverter. Harris Hawks Optimization (HHO) is implemented in this work, to train the MLP-NN model by choosing the optimal weight and biases for the estimation of accurate parameters and speed of IMD. The objective of optimal MLP-NN is to improve the IMD reliability and response fast during dynamic operation. The model performances are evaluated by employing statistical metrics of MSE, RMSE, MAE, MAPE, and R for training and testing. These are reported for testing to be 0.000602064, 0.0245, 0.4015, 0.25474, and 0.9997 which indicates the best-fitted prediction model and proves the minimized error. The results reveal that an optimized MLP-NN accomplishes promising performance in estimating the parameters and speed with the least errors such as rs is 3.82%, rr is 4.19%, ls is 0.41%, lr is 0.72%, lm is 0.21%, and strongly tracking of reference speed. In addition, HHO is also employed to evolve the gains of the PI-controller in adaptive-IFOC for generation of reference signals by reducing the computational effort.