Bat Algorithm Based an Adaptive PID Controller Design for Buck Converter Model

In the recent years, Buck converters have been widely involved in a variety of the everyday applications such as smartphones and PCs. Buck converters can provide better and steadier performance when integrating a control system in the design. Therefore, it is interesting to work on this integration and gain the required efficiency in term of the gained voltage. In this paper, PID controller is adopted to control the output voltage of the Buck converter. An optimization is achieved on the performance of the Buck converter using two bio-inspired algorithms namely, Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). The voltage controlled Buck converter system is simulated using Matlab environment to validate the proposed PID controller system. In this study, the voltage regulation process of Buck converter is investigated based on many working disturbances such as the change in the supply voltage, reference voltage, and load resistance in order to verify the robustness of the proposed PID controller. Finally, the feedabck voltage control system of the Buck converter is implemented experimentaly in real-time to validatde the simulated PID controller.

The DC-DC converters are extensively used in modern telecommunication and consumer electronics. Stability is one of the most aspect in designing of automatic control of DC-DC buck converter. In this paper a discrete auto-tuning PID scheme is developed for DC-DC converters for fast response time. When the required output does not follow the set point, a controller for synchronous buck converter is to be used with objective of keeping the error as minimum. The algorithm is used for the tuning discrete PID controller to obtain its parameters with a minimum error and is applied to Synchronous buck converter to improve its performance. For fast transient response, settling time and rise time of the Converter, the controller gains are continuously modified. For its implementation a buck converter is designed and its MATLAB/Simulink model is developed. The simulation results describes the effectiveness of the developed algorithms.

The aim of this paper is to propose a neural network based reference modified PID control which has a simple duration design method for transient characteristics improvement of digitally controlled dc-dc converters. In renewable energy network systems, various types of dc-dc converters are widely used for power conversion and such converters require a superior control method for a stable operation. Especially, transient characteristics should be improved since they heavily affect the stability of the system. For such purposes, designing of conventional control methods becomes a difficult task since the optimization of control parameters needs complicated analysis and it is affected from variations of circuit components of converters. Therefore, simple and easy design of control is widely required for a stable operation of power converters. The neural network can provide a suitable control methodology for such situation since it treats the plant as a black box and it can realize a non-linear control based on training of the input-output relation without complicated modelling and analysis. On the other hand, the neural network based method has a disadvantage caused from the fact that the neural network is trained with data obtained in advance and an overcompensation phenomenon occurs in the transient response. In this paper, the neural network control is adopted to control the dc-dc converter in coordination with a conventional PID control. The neural network predicts the output voltage of the converter and the reference value in the PID control is modified with the predictions to reduce the error of the output voltage. To avoid overcompensation, a simple duration design for the neural network control is also provided to improve the transient response effectively. From prototype testing in simulation and experiment, it is revealed that the proposed method contributes to obtain a superior transient performance compared with the conventional PID control.

This study uses intelligent techniques to regulate brushless direct current speed (BLDC) motors. After these motors solved the problem of using brushes and commutators in traditional DC motors, they succeeded in replacing brushes and commutators with electronic commutators. Due to the use of electronic switching, brushless motor algorithms are more complex than those of conventional motors. In this study, to adjust the PID controller's settings (Kp, Ki, and Kd), a trial-and-error approach was taken, and a completely new method known as the settings of known PID controllers have been modified using the new Gray Wolf algorithm. A BLDC motor's main benefit is that it has easy speed adjustment across a broad range, whereas AC motors often cannot be controlled in this way. Through the use of Matlab/Simulink, the BLDC motor's mathematical model was developed and implemented. The simulation results show that in the first case, a PID controller effectively induces the turbulent dynamic behavior of BLDC under load and no-load conditions, and in the second case, the speed shows the lowest rise time, stability, overshoot, and stability conditions, and performs at its best. The characteristics of the traditional PID controller that regulates the engine speed must be regulated online to achieve the use of intelligent technologies, and the adjustment is done online using the neural network. The results showed that this technology, or feature - online tuning - is the most effective and reliable of all.

This paper introduces a practical modification of the buck converter and a complementary controller that are well suited for point of load applications with highly dynamic loads. The presented flyback-transformer-based buck (FTBB) converter has faster transient response than the conventional buck, allowing for a reduction in the output capacitance without penalty in the size of the magnetic core. During heavy-to-light load transients, the FTBB converter also recycles excess energy stored in the magnetic component, providing a useful energy savings mechanism. In this modification, the conventional buck inductor is replaced with the primary winding of the flyback transformer, an extra switch, and a set of small auxiliary switches on the secondary side. During heavy-to-light load transients, the inductor current is steered away from the output capacitor to the input port, achieving both energy recycling and savings due to reduced voltage overshoots. The light-to-heavy transient response is improved by reducing the equivalent inductance of the primary transformer winding to its leakage value. A mixed-signal current-programmed mode controller regulates operation of the converter. The controller provides minimum output voltage deviation and ensures seamless transitions between different operating modes. The performance of the FTBB converter is verified with a 6-to-1-V, 3-W, 390-kHz experimental prototype. In comparison with an equivalent buck converter, the experimental system has about three times smaller maximum output voltage deviation, allowing for the same output capacitor reduction and, for frequently changing loads, about 7% decrease in power losses.

DC motor is the most common choice if the wide range of adjustable speed drive operation is desired. In many studies, the DC motor controllers have been implemented without considering the final control element (FCE), leading to a non-realistic performance. In this paper, the authors have suggested a new approach to obtain the adaptive and robust control of DC motor. Buck converter, driven by high frequency PWM signals obtained from PID controller has been chosen as FCE in this study. Ziegler-Nichols method is applied to the combined system of dc motor and buck converter, to get the initial settings of PID controller. Using fuzzy logic, these settings are online updated corresponding to the changes that may occur during system operating conditions. Also, this approach has a robust performance against disturbance/parameter variations. The buck converter has been simulated using sim-power system library of MATLAB. Comparative results have been shown for PID controller and adaptive fuzzy-PID controller for both tracking and regulatory type of control problems to obtain excellent performance with no steady state error.

A new design approach is presented in this study for tuning of proportional-integral-derivative (PID) controller parameters in a DC–DC buck converter utilizing a hybrid whale optimization algorithm (WOA) with simulated annealing (SA), namely the WOASAT algorithm, which uses a tournament selection mechanism. The proposed algorithm’s efficacy ensures that the optimum PID controller parameters are tuned quickly and that the quality of tuning is high. A time domain performance index is utilized to validate the proposed WOASAT-based PID controller’s performance. In addition, from the comparative results of statistical analysis, frequency response analysis, transient response analysis, disturbance rejection analysis, and performance indices analysis, the proposed WOASAT-PID controller was found to be more efficient than the SA-PID controller and WOA-PID controller in enhancing the buck converter’s transient response. Cite this article as: Hekimoğlu B, Ekinci S. Optimally Designed PID Controller for a DC-DC Buck Converter via a Hybrid Whale Optimization Algorithm with Simulated Annealing. Electrica, 2020; 20(1): 19-27.

Experimental investigation for the dynamic mechanical properties of a vertical anisotropically supported rotor

This paper presents an improved emotional controller for brushless DC motor (BLDC) drive. The proposed controller is called brain emotional learning based intelligent controller (BELBIC). The utilization of the new controller is based on the emotion processing mechanism in brain. This intelligent control is inspired by the limbic system of mammalian brain, especially amygdala. The controller is successfully implemented in simulation using MATLAB software, brushless dc drive with trapezoidal back-emf. In this work, a novel and simple implementation of BLDC motor drive system is achieved by using the intelligent controller, which controls the motor speed accurately. This emotional intelligent controller has simple structure with high auto learning feature. Simulation results show that both accurate steady state and fast transient speed responses can be achieved in wide range of speed from 20 to 300 [rpm]. Moreover, for evaluate this emotional controller and hence to assess the effectiveness and control capability of the proposed BELBIC scheme, the performances of proposed control scheme are compared with a conventional PID controller for the BLDC drive control, in simulation different conditions. This is shown proper operating than the PID controller. And also shows excellent promise for industrial scale utilization.

This paper presents the modeling and controlling of a fuzzy logic controller (FLC) for achieving improved dynamic performance of the Brushless DC (BLDC) motor drive. The performance of fuzzy and the PI controller based BLDC motor drives is investigated under the different operating conditions such as change in reference speed, parameter variations and the load disturbance. BLDC motors are used in industrial control, automation and instrumentation system applications. Based on other applications the conventional controllers like P, PI and PID controllers are used in the BLDC motor drive control systems to achieve the desired level of transient and steady state responses. However, the major problem in the conventional PI controller there is some variations in the parameter and some load disturbances are occur and do not yield the better transient and the steady state responses under the operating conditions. In this paper, the modeling and controlling of fuzzy logic controller is presented its performance and it compared with the PI controller shows the error capability and usefulness of the fuzzy controller in the control applications.

This paper concerns design and implementation of PID and fractional-order PID (FOPID) controllers to control position of an electromagnetically suspended ferromagnetic ball in a magnetic levitation (Maglev) system in real time. The Maglev system, manufactured by Feedback Instruments (Model No 33-210) is used as a platform to test the performance of proposed controllers. Parameters of PID and FOPID controllers are tuned by firefly algorithm (FA). FA is a metaheuristic algorithm based on movement of fireflies toward more attractive and brighter ones. PID and FOPID controllers are implemented in MATLAB and SIMULINK environment inside PC using fractional-order modeling and control toolbox. Controller in the SIMULINK environment inside PC is connected to the Maglev system through Advantech card. Effectiveness of proposed controllers is tested by checking the ability of the suspended ball to track a reference signal. Step change, sine wave, and square wave are used as reference signals. Real-time results have revealed satisfactory transient and steady-state responses over the contemporary existing controllers. FOPID controller showed better results compared to PID.

Windup effect in a PID controller is the condition of cumulative error in the integral action. It saturates the system actuators, letting the system out of control with a higher controller action. Putting a saturator for the controller action, it is limited into a linear range of operating values. However, it does not eliminate the cumulative error of the integral term. This paper presents the speed control of a motor generator system using a digital PID controller with antiwindup scheme to eliminate the cumulative error of the integral term of controller action. Initially, a linear model of the motor generator system was identified. From the linear model of the system, a PID controller was designed using pole placement technique. Controller discrete implementation was perform using the backward difference discretization methodology. PID controller consider the antiwindup scheme. PID controller practical implementation employed Matlab Stateflow and a data acquisition card. Obtained results showed that discrete implementation of the PID controller has a good performance against external disturbances and random noise.

Today’s arm manipulators are more and more demanding in terms of productivity. Conventional controllers are not always able to provide good and accurate results. To complete a position movement of the manipulator’s end-effector, a set of joint angles of manipulator first required to be converted to the position coordinates by using the forward kinematics method, and each joint rotation is executed using a servomotor feedback control. The kinematic model has been validated using MATLAB® robotics toolbox. An end-effector based 6 degree of freedom (6-DOF) platform is proposed in this work which uses DC servomotor for actuation of the three revolute joints. PID controller is used as a reference benchmark. And FLC controller with different defuzzification strategies was employed. Results were compared in terms of time response criteria. Simulation results using MATLAB are demonstrated that PID has superior performance in terms of transient parameters. In Steady state response, both PID and FLC manage to converge to the desired output but in terms of overshot FLC is outperformed.

This paper is proposed to show the interaction between the LFC and the AVR loops in the Power Plant. The combinational effects of these two LFC and AVR loops are studied by extending the linear zed AGC system. A complete system model for LFC with Speed Governor, Turbine, Integral controller and Power System and AVR with Excitation System and PID controllers are described. LFC is for regulation of system frequency. It is also called a power factor control loop and influence the active power balances in the power system network. The LFC is achieved by the speed-governor mechanism. The basic principle of the speed-governor mechanism is to adjust itself as per the load variations. The voltage of the generator is proportional to the speed and excitation of the generator. If the speed is kept at constant, then the excitation system is used to control the voltage. The voltage control is also called as an excitation control system. This combined model is tested with conventional PID Controller, PID with Power System Stabilizer and Fuzzy-PID with Power System Stabilizer. The results are shown by using simulation; this will be reachable in dynamic and steady state responses.

This paper deals with the output voltage regulation problem for dc-dc buck converters. Overcurrent protection and disturbances are both taken into consideration. The main challenge of control design is how to ensure that the current constraints are not violated at any time in the presence of disturbances. At first, a high-gain disturbance observer is utilized to estimate the disturbances. Then, a novel punishment mechanism for the inductor current is designed in the control scheme. Finally, based on the novel current-constrained technique and the high-gain disturbance observer, a composite control approach is proposed. The new control method not only guarantees the current within the required range but also has a nice ability on the disturbance rejection. In the meantime, rigorous stability proof is also presented. The theoretical analysis also explains how disturbances affect the satisfaction of the current constraints. The experimental results on a buck converter system are illustrated to verify the feasibility and effectiveness of the proposed control scheme.