This study focuses on the design of a robust PD gain-scheduling controller (PD-GS-C) for an unstable SISO (single-input, single-output) magnetic levitation system with two electromagnets (MLS2EM). Magnetic levitation systems offer various advantages, including friction-free, reliable, fast, and cost-effective operations. However, due to their unstable and highly nonlinear nature, these systems require sophisticated feedback control techniques to ensure optimal performance and functionality. To address these challenges, in this study, we derive the nonlinear state-space mathematical model of the MLS2EM and linearize it around five different operating points. The PD-GS-C controller aims to stabilize the system and improve steady-state control error. The strategy for obtaining the PD controller gains involves a parameter space technique, which specifies performance requirements. This technique results in ranges of proportional ($K_P$) and derivative ($K_D$) gains that are used by the PD-GS-C structure. To optimize the controller’s performance further, we utilize the big bang–big crunch optimization technique (BB-BC) to determine the optimal PD gains within the specified ranges. The optimization process focuses on achieving optimal performance in terms of a specific performance index function. This function quantifies the system’s time-domain step response criteria, which include minimizing overshoot percentage, settling time, and rising time. The index function is inversely proportional to the desired performance criteria, meaning that the goal is to maximize the index function to optimize the system’s performance. To validate the effectiveness and viability of the proposed strategy, we conducts MATLAB simulations and real-time experiments. The simulations and experimental findings serve to demonstrate the controller’s performance and verify its capabilities in stabilizing the MLS2EM magnetic levitation system.

Modern control systems’ dependability, safety and efficiency have all been improved by studying fault-tolerant control systems (FTCS). FTCS techniques can typically be active or passive controls. The fault detection and diagnosis (FDD) method is used in this study’s active control branch to identify probable faults that could develop in the speed Hall sensors of brushless DC motors (BLDC). FDD methodologies can be categorised into two types, depending on the available data and the process involved: model-based methods and data-based methods. The proposed approach in this study explores the implementation of the Luenberger observer methodology as part of the model-based approach. The chosen methodology was practically implemented and subjected to experimental evaluation. The proposed observer relies on the residual signal, which displays the difference between the plant’s observed and estimated speed signals and serves as a failure alert for the entire system. Given the increasing demand for BLDC motors in various industrial control applications, including medical fields, automation and robotics, this particular motor was selected as a benchmark to thoroughly evaluate and validate the proposed method. The primary contribution of this paper lies in the real-time application of model-based sensor fault detection methods to BLDC motors. The efficiency of the suggested method is showcased through extensive MATLAB simulations, where the obtained results confirm the successful detection of faults with a high level of responsiveness. As a result, the project was successfully implemented in real-time, and the experimental results exhibited a close correlation with the simulated outcomes. This consistency between simulation and practical implementation validates the accuracy and reliability of the proposed methodology for detecting faults in the BLDC motor speed sensor. The results underscore the heightened reliability and safety attained by promptly and accurately detecting sensor faults during the operation of the motor.

Fault Tolerant Control Systems (FTCS) have become an important area of research for improving safety, reliability and efficiency of modern control systems. Generally, FTCS technique can be active or passive control. In this paper, the Fault Detection and Diagnosis (FDD) which belongs to the active control branch is used in order to detect faults that may occur in speed hall sensors of Brushless DC Motors (BLDC). The FDD methodologies, which depend on the process and the type of data available can be divided into two approaches; Model-based methods and data-based methods. Here, the proposed method focuses on the investigation of the use of Luenberger observer technique which belongs to the model-based approach. This observer depends on the residual signal which is used as a fault indicator in the overall system and represents the difference between the measured speed signal from the plant and the estimated speed signal. BLDC motor is selected as a benchmark for testing the proposed method due to the increasing demand for this type of motors especially in the renewable energy sector as in electrical vehicles, solar system, and new water pumping systems. The effectiveness of the proposed method is proved using MATLAB simulations and the results illustrate clearly the detection of faults as expected with high performance response.

In this paper, a comparative study between nonlinear sliding mode controller (SMC) and standard PI controller for stabilizing bridgeless AC/DC converter under uncertain parameters is presented. This type of converters is widely used in harvesting low energy systems as in wind turbine, piezoelectric transducers and heat exchange transducers. Designing robust controllers to enhance the efficiency and accuracy of these converters has become a promising track in control engineering field. The traditional bridge rectifier is widely used in the majority types of AC/DC conventional converters to gain the rectified DC voltage from the low AC input voltage source. However, these traditional converters are not effective for the low output voltage of renewable sources due to the voltage drops across rectifier’s diodes. The proposed SMC-PI controller is used to enhance the stability and the response of these converters under uncertain parameters comparing with the standard PI controller. The proposed approach consists of both Boost and Buck-Boost converters with two controllers in order to maximize the useful output energy from the source. The graphical method has been used to obtain the limitation of the coefficients of the standard PI controller. A parameter space approach is used to find all robust stabilization PI coefficients and the stability regions. A comparative study using simulations in MATLAB is presented to ensure the effectiveness and robustness of the proposed SMC-PI controller under some external disturbances.

One of the most popular methods for giving feedback to the control loop in industrial control systems is the proportional-integral-derivative (PID) controller. The tuning of the PID controller, however, is currently being researched by engineers. In this research, a robust PID controller is proposed for the CE152 magnetic levitation system. Magnetic levitation, commonly referred to as maglev, is a technology that uses magnetic fields to levitate an object, such as a vehicle or train, above a track. By using magnetic forces to counteract gravitational and inertial forces, maglev systems can achieve frictionless movement and potentially higher speeds compared to conventional wheeled transportation. In this research, the robust PID controller is involved by computing all stabilized PID controller gains for the affine linear characteristic polynomial in the presence of uncertain parameters using the parameter space approach and the edge theorem. The results of the parameter space approach are ranges of PID gains . Here, the optimal PID gains were chosen by the Artificial Bee Colony optimization algorithm to get optimal performance for CE152 magnetic levitation. The research defines a specific performance index function that quantifies the system's time-domain step response criteria (small overshoot percentage with significant minimization of both settling and rising times). This index function is inversely proportional to the desired performance criteria, aiming to optimize the system's performance. MATLAB simulations are used to validate and demonstrate the efficiency of the proposed graphical method for enhancing stability in the maglev system.