Vehicle suspension system results in a trade-off between the crucial requirements of road holding and ride comfort. To increase the suspension performance and overcome this difficulty, vehicle dynamic systems must be equipped with new technologies and intelligent materials. This paper proposes a fuzzy logic control strategy to enhance ride comfort and road holding dynamics for a half-vehicle active suspension system. The fuzzy logic controller offers a control approach that enables systems with uncertainty and complexity to be managed effectively. Two fuzzy logic controllers for each tire are executed and arranged for the characteristics of the unsprung and sprung masses. The controllers’ inputs are suspension deflection and sprung mass acceleration for each tire. Moreover, actuator force is generated as the controllers’ outputs. Bump road disturbances are applied to each tire for performance evaluation of the controllers. The performance criteria for the suspension system are selected as acceleration and displacement of sprung mass, suspension deflection, and dynamic tire load. These parameters are compared to the passive suspension system and evaluated on ride comfort and road holding. Ride comfort is enhanced by 34% with the active suspension system, including a fuzzy logic controller. Furthermore, road holding is improved by about 13% regarding suspension deflection and dynamic tire load. In conclusion of the simulation, the proposed control approach enhances ride comfort and road-holding dynamics concurrently.

This study presents an implementation of a proportional–integral–derivative (PID) controller utilizing particle swarm optimization (PSO) to enhance the compromise on road holding and ride comfort of a quarter car semi-active suspension system (SASS) through simulation and experimental study. The proposed controller is verified with a processor-in-the-loop (PIL) approach before real-time suspension tests. Using experimental data, the magnetorheological damper (MR) is modeled by an artificial neural network (ANN). A series of experiments are applied to the system for three distinct bump disturbances. The algorithm performance is evaluated by various key metrics, such as suspension deflection, sprung mass displacement, and sprung mass acceleration for simulation. The phase plane method is used to prove the stability of the system. The experimental results reveal that the proposed controller for the SASS significantly improves road holding and ride comfort simultaneously.

This paper presents an implementation of type-2 fuzzy controller for DC – DC boost converter fed by photovoltaic panels. High power boost converter is the imperative part of the renewable energy power system that transfers energy to the utility or standalone study. Power circuit applications require fast dynamic response, lower overshoot and lower steady state error. Intelligent control algorithms are used to obtain these features. Type-1 fuzzy logic control is one of the intelligent control algorithms. Power circuits are non-linear structure and contain uncertainties. Conventional controllers and type-1 fuzzy controllers cannot be enough to overcome uncertainties. In this study, type-2 fuzzy controller which has high performance for modeling uncertainties has been developed as control algorithm for DC – DC boost converter. The converter has been also controlled with proportional–integral (PI) and type-1 fuzzy controller separately to show the superiority of the developed type-2 fuzzy controller. The converter has been controlled with TMS320F28335 DSP for real time application. Each control algorithm has been used to control of the converter individually. Settling time, overshoot and load variation responses have been investigated for the converter. Results showed that type-2 fuzzy controller has pointed out the best performance.

Accurate fault detection for Permanent Magnet Synchronous Motors (PMSMs) prevents costly failures and improves overall reliability. This paper presents a hybrid one-dimensional convolutional neural network (1DCNN)–bidirectional gated recurrent unit (BiGRU) deep learning model for PMSM fault detection. Inverter-driven short-circuit, open-circuit, and thermal faults, as well as stator faults, can cause electrical and thermal disturbances that affect PMSMs. Significant harmonic distortions, current and voltage peaks, and transient fluctuations are introduced by these faults. The proposed architecture utilizes handcrafted features, including statistical analysis, fast Fourier transform (FFT), and Discrete Wavelet Transform (DWT), extracted from the raw PMSM signals to efficiently capture these faults. 1DCNN effectively extracts local and high-frequency fault-related patterns that encode the effects of peaks and harmonic distortions, while the BiGRU of this enriched representation models complex temporal dependencies, including global asymmetries across phase currents and long-term fault evolution trends seen in stator faults and thermal faults. The proposed model reveals the highest metrics for inverter-driven and stator winding fault datasets compared to the other approaches, achieving an accuracy of 99.44% and 99.98%, respectively. As a result, the study with realistic and comprehensive datasets guarantees high accuracy and generalizability not only in the laboratory but also in industry.

This paper presents fuzzy logic control (FLC) of a grid connected two stage three phase photovoltaic system. The first stage consist of a DC-DC boost converter with maximum power point tracking (MPPT), and the second stage consists of a three phase grid connected PWM switched inverter. In the first stage, excess currents are transferred to the grid by using perturb and observation (P&O) MPPT algorithm. Fuzzy logic controller regulates the d-q axis currents for the inverter control. Proposed system was simulated using Matlab/Simulink under variable irradiation and temperature. The simulation results show that the proposed system achieved better performance in terms of dynamic responses, the axes currents follow the reference currents without overshoot and the settling time is very short.

This paper presents a practical implementation of an interval type-2 fuzzy logic controller for two stages photovoltaic system consisting of DC- DC boost converter and three-phase inverter. The power converter circuits are nonlinear and have uncertainties. These uncertainties cause noise and delay in the response of power systems. A photovoltaic system has also uncertainties caused by itself. The change in temperature and irradiation cause the change in parameters of photovoltaic systems. Conventional controllers and type-1 fuzzy controllers can’t overcome uncertainties. The main objective of this study is to show the superiority of interval type-2 fuzzy logic controller to overcome the uncertainties. Interval type-2 fuzzy logic controller algorithm was developed for DC- DC boost converter and three-phase space vector modulated inverter. To make comparison, the system was controlled by a type-1 and an interval type-2 fuzzy controller respectively. Simulation studies and real time application were performed. Settling time, overshoot and ripple voltage were investigated for the boost converter. Current and voltage harmonic analysis, frequency and phase angle data were analyzed for three-phase inverter. According to the simulation results and the practical results, interval type-2 fuzzy logic controller offered better solution for the whole system.