Conformable Fractional Order PI Controller Design and Optimization for Permanent Magnet Synchronous Motor Speed Tracking System

The use of permanent magnet synchronous motor (PMSM) is increasing rapidly to meet the need to increase efficiency in variable speed drive systems used in the industry, in recent years. This paper aims to improve the speed control performance of the PMSM based systems. To achieve this, a PMSM speed controller is designed based on the conformable fractional order proportional integral (CFOPI) method. CFOPI controller coefficients kp, ki and γ are optimized using response surface method (RSM). To validate the success of the proposed scheme, the CFOPI controller and the integer order PI (IOPI) controller are tested under the same simulation model and the results are compared. The proposed method grants robust performance with less computational load then the classical fractional order controllers for variable referenced PMSM speed tracking systems. The CFOPI controller can be applied easily for industrial variable speed drive systems which is using PMSM to improve the performance and stability of the systems

Evolutionary swarm algorithm for modelling and control of horizontal flexible plate structures

Numerous advantages offered by the horizontal flexible structure have attracted increasing industrial applications in many engineering fields particularly in the airport baggage conveyor system, micro hand surgery and semiconductor manufacturing industry. Nevertheless, the horizontal flexible structure is often subjected to disturbance forces as vibration is easily induced in the system. The vibration reduces the performance of the system, thus leading to the structure failure when excessive stress and noise prevail. Following this, it is crucial to minimize unwanted vibration so that the effectiveness and the lifetime of the structure can be preserved. In this thesis, an intelligent proportional-integral-derivative (PID) controller has been developed for vibration suppression of a horizontal flexible plate structure. Initially, a flexible plate experimental rig was designed and fabricated with all clamped edges boundary conditions at horizontal position. Then, the data acquisition and instrumentation systems were integrated into the experimental rig. Several experimental procedures were conducted to acquire the input-output vibration data of the system. Next, the dynamics of the system was modeled using linear auto regressive with exogenous, which is optimized with three types of evolutionary swarm algorithm, namely, the particle swarm optimization (PSO), artificial bee colony (ABC) and bat algorithm (BAT) model structure. Their effectiveness was then validated using mean squared error, correlation tests and pole zero diagram stability. Results showed that the PSO algorithm has superior performance compared to the other algorithms in modeling the system by achieving lowest mean squared error of 6103947.4 , correlation of up to 95 % confidence level and good stability. Next, five types of PID based controllers were chosen to suppress the unwanted vibration, namely, PID-Ziegler Nichols (ZN), PID-PSO, PID-ABC, Fuzzy-PID and PID-Iterative Learning Algorithm (ILA). The robustness of the controllers was validated by exerting different types of disturbances on the system. Amongst all controllers, the simulation results showed that PID tuned by ABC outperformed other controllers with 47.60 dB of attenuation level at the first mode (the dominant mode) of vibration, which is equivalent to 45.99 % of reduction in vibration amplitude. By implementing the controllers experimentally, the superiority of PID-ABC based controller was further verified by achieving an attenuation of 23.83 dB at the first mode of vibration and 21.62 % of reduction in vibration amplitude. This research proved that the PID controller tuned by ABC is superior compared to other tuning algorithms for vibration suppression of the horizontal flexible plate structure

Review of soft computing models in design and control of rotating electrical machines

Rotating electrical machines are electromechanical energy converters with a fundamental impact on the production and conversion of energy. Novelty and advancement in the control and high-performance design of these machines are of interest in energy management. Soft computing methods are known as the essential tools that significantly improve the performance of rotating electrical machines in both aspects of control and design. From this perspective, a wide range of energy conversion systems such as generators, high-performance electric engines, and electric vehicles, are highly reliant on the advancement of soft computing techniques used in rotating electrical machines. This article presents the-state-of-the-art of soft computing techniques and their applications, which have greatly influenced the progression of this significant realm of energy. Through a novel taxonomy of systems and applications, the most critical advancements in the field are reviewed for providing an insight into the future of control and design of rotating electrical machines

Implementation of Automatic DC Motor Braking PID Control System on (Disc Brakes)

The vital role of an automated braking system in ensuring the safety of motorized vehicles and their passengers cannot be overstated. It simplifies the braking process during driving, enhancing control and reducing the chances of accidents. This study is centered on the design of an automatic braking device for DC motors utilizing disc brakes. The instrument employed in this study was designed to accelerate the vehicle in two primary scenarios - before the collision with an obstacle and upon crossing the safety threshold. It achieves this by implementing the Proportional Integral Derivative (PID) control method. A significant part of this system comprises ultrasonic sensors, used for detecting the distance to obstructions, and rotary encoder sensors, which are utilized to measure the motor's rotational speed. These distance and speed readings serve as essential reference points for the braking process. The system is engineered to initiate braking when the distance value equals or falls below 60cm or when the speed surpasses 8000rpm. During such events, the disc brake is activated to reduce the motor's rotary motion. The suppression of the disc brake lever is executed pneumatically, informed by the sensor readings. Applying the PID method to the automatic braking system improved braking outcomes compared to a system without the PID method. This was proven by more effective braking results when the sensors detected specific distance and speed values. Numerous PID tuning tests achieved optimal results with K_p = 5, K_i = 1, and K_d = 3. These values can be integrated into automatic braking systems for improved performance. The PID method yielded more responsive braking outcomes when applied in distance testing. On the contrary, the braking results were largely unchanged in the absence of PID. Regarding speed testing, the PID method significantly improved the slowing down of the motor speed when it exceeded the maximum speed limit of 8000 rpm. This eliminates the possibility of sudden braking, thus maintaining the system within a safe threshold. The average time taken by the system to apply braking was 01.09 seconds, an indication of its quick responsiveness. This research is a valuable addition to control science, applying the PID control method to automatic DC motor braking. It provides valuable insights and concrete applications of PID control to complex mechatronic systems. It is also noteworthy for its development and optimization of suitable PID parameters to achieve responsive and stable braking. The study, therefore, offers a profound understanding of how PID control can be employed to manage braking systems on automatic DC motors, thereby advancing knowledge and application of control in control science and mechatronics

Large Grid-Connected Wind Turbines

This book covers the technological progress and developments of a large-scale wind energy conversion system along with its future trends, with each chapter constituting a contribution by a different leader in the wind energy arena. Recent developments in wind energy conversion systems, system optimization, stability augmentation, power smoothing, and many other fascinating topics are included in this book. Chapters are supported through modeling, control, and simulation analysis. This book contains both technical and review articles

Towards a generic optimal co-design of hardware architecture and control configuration for interacting subsystems

In plants consisting of multiple interacting subsystems, the decision on how to optimally select and place actuators and sensors and the accompanying question on how to control the overall plant is a challenging task. Since there is no theoretical framework describing the impact of sensor and actuator placement on performance, an optimization method exploring the possible configurations is introduced in this paper to find a trade-off between implementation cost and achievable performance. Moreover, a novel model-based procedure is presented to simultaneously co-design the optimal number, type and location of actuators and sensors and to determine the corresponding optimal control architecture and accompanying control parameters. This paper adds the optimization of the control architecture to the current state-of-the-art. As an optimization output, a Pareto front is presented, providing insights on the optimal total plant performance related to the hardware and control design implementation cost. The proposed algorithm is not focused on one particular application or a specific optimization problem, but is instead a generally applicable method and can be applied to a wide range of applications (e.g., mechatronic, electrical, thermal). In this paper, the co-design approach is validated on a mechanical setup

Load frequency control for multi-area interconnected power system using artificial intelligent controllers

Power system control and stability have been an area with different and continuous challenges in order to reach the desired operation that satisfies consumers and suppliers. To accomplish the purpose of stable operation in power systems, different loops have been equipped to control different parameters. For example, Load Frequency Control (LFC) is introduced to maintain the frequency at or near its nominal values, this loop is also responsible for maintaining the interchanged power between control areas interconnected via tie-lines at scheduled values. Other loops are also employed within power systems such as the Automatic Voltage Regulator (AVR). This thesis focuses on the problem of frequency deviation in power systems and proposes different solutions based on different theories. The proposed methods are implemented in two different power systems namely: unequal two-area interconnected thermal power system and the simplified Great Britain (GB) power system. Artificial intelligence-based controllers have recently dominated the field of control engineering as they are practicable with relatively low solution costs, this is in addition to providing a stable, reliable and robust dynamic performance of the controlled plant. They professionally can handle different technical issues resulting from nonlinearities and uncertainties. In order to achieve the best possible control and dynamic system behaviour, a soft computing technique based on the Bees Algorithm (BA) is suggested for tuning the parameters of the proposed controllers for LFC purposes. Fuzzy PID controller with filtered derivative action (Fuzzy PIDF) optimized by the BA is designed and implemented to improve the frequency performance in the two different systems under study during and after load disturbance. Further, three different fuzzy control configurations that offer higher reliability, namely Fuzzy Cascade PI − PD, Fuzzy PI plus Fuzzy PD, and Fuzzy (PI + PD), optimized by the BA have also been implemented in the two-area interconnected power system. The robustness of these fuzzy configurations has been evidenced against parametric uncertainties of the controlled power systems Sliding Mode Control (SMC) design, modelling and implementation have also been conducted for LFC in the investigated systems where the parameters are tuned by the BA. The mathematical model design of the SMC is derived based on the parameters of the testbed systems. The robustness analysis of the proposed SMC against the controlled systems’ parametric uncertainties has been carried out considering different scenarios. Furthermore, to authenticate the excellence of the proposed controllers, a comparative study is carried out based on the obtained results and those from previously introduced works based on classical PID tuned by the Losi Map-Based Chaotic Optimization Algorithm (LCOA), Fuzzy PID Optimized by Teaching Learning-Based Optimization (TLBO