A Deep Reinforcement Learning-Based Controller for Magnetorheological-Damped Vehicle Suspension

This paper proposes a novel approach to controller design for MR-damped vehicle suspension system. This approach is predicated on the premise that the optimal control strategy can be learned through real-world or simulated experiments utilizing a reinforcement learning algorithm with continuous states/actions. The sensor data is fed into a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which generates the actuation voltage required for the MR damper. The resulting suspension space (displacement), sprung mass acceleration, and dynamic tire load are calculated using a quarter vehicle model incorporating the modified Bouc-Wen MR damper model. Deep RL's reward function is based on sprung mass acceleration. The proposed approach outperforms traditional suspension control strategies regarding ride comfort and stability, as demonstrated by multiple simulated experimentsComment: 19 pages , 9 figures , 5 table

Designing an Improved Deep Learning-based Model for COVID-19 Recognition in Chest X-ray Images: A Knowledge Distillation Approach

COVID-19 has adversely affected humans and societies in different aspects. Numerous people have perished due to inaccurate COVID-19 identification and, consequently, a lack of appropriate medical treatment. Numerous solutions based on manual and automatic feature extraction techniques have been investigated to address this issue by researchers worldwide. Typically, automatic feature extraction methods, particularly deep learning models, necessitate a powerful hardware system to perform the necessary computations. Unfortunately, many institutions and societies cannot benefit from these advancements due to the prohibitively high cost of high-quality hardware equipment. As a result, this study focused on two primary goals: first, lowering the computational costs associated with running the proposed model on embedded devices, mobile devices, and conventional computers; and second, improving the model's performance in comparison to previously published methods (at least performs on par with state-of-the-art models) in order to ensure its performance and accuracy for the medical recognition task. This study used two neural networks to improve feature extraction from our dataset: VGG19 and ResNet50V2. Both of these networks are capable of providing semantic features from the nominated dataset. To this end, An alternative network was considered, namely MobileNetV2, which excels at extracting semantic features while requiring minimal computation on mobile and embedded devices. Knowledge distillation (KD) was used to transfer knowledge from the teacher network (concatenated ResNet50V2 and VGG19) to the student network (MobileNetV2) to improve MobileNetV2 performance and to achieve a robust and accurate model for the COVID-19 identification task from chest X-ray images.Comment: 25 pages, 3 figures , 5 table

Design and Implementation of a Fuzzy Adaptive Controller for Time-Varying Formation Leader-Follower Configuration of Nonholonomic Mobile Robots

In this paper, a time-varying leader-follower formation control of nonholonomic mobile robots based on a trajectory tracking control strategy is considered. In the time-varying formation, the relative bearing and distance of each follower are variable parameters, and therefore, the followers can carry out various and complex behaviour even without changing the linear and angular velocities of the leader robot. After proposing the kinematic model of the time-varying leader-follower formation, the backstepping control method is exploited to keep the structure of the defined formation. The global stability of the formation is investigated using the Lyapunov theorem. Moreover, the designed nonlinear controller suffers from the ineffectual large input commands at the beginning of the formation. To rectify this problem, a fuzzy adaptive algorithm is proposed to improve the backstepping controller and the global stability of the resulting fuzzy adaptive backstepping controller is guaranteed. Considering the rate change of relative distance and bearing in the kinematic model of the leader-follower formation and controller design procedure, makes the formation more practical in dynamic and clutter environments, as well as capable of defining complicated behaviour for followers, and provides crash and obstacle avoidance without switching between different control strategies. Finally, the performance of the proposed kinematics model and designed controllers are investigated through simulations and experimental studies

Modeling and Fault Detection of Quadrotor with Rotor Thrust Deviation Fault

In this study, modeling and fault detection of a novel faulty quadrotor is presented. It is assumed that a quadrotor vehicle has encountered a fault during a flight accident, and as a result, one of the rotors does not operate vertically. Although the rotor's rotational axis has deviated from the vertical direction, the amount of produced thrust remains constant. Detecting this fault along with utilizing a proper controlling approach can reduce the risk of failure in the vehicle. Based on this statement, the procedure of this study has been developed in three main stages. First, the kinematic and dynamic equations governing the faulty system are driven using Newton's second law and Euler's principle. Then, equations governing the faulty system and the Thau observer are employed to calculate the residual value. This parameter is calculated based on the differences between states’ measurement and estimation. Eventually, by comparing the computed residual value with the assumed threshold, thrust deviation in the shortest possible time has been detected

Modeling and prediction of driver-vehicle-unit velocity using adaptive neuro-fuzzy inference system in real traffic flow / Iman Tahbaz-zadeh Moghaddam...[et al.]

Prediction of the driver-vehicle-unit (DVU) future state is a challenging problem due to many dynamic factors influencing driver capability, performance and behavior. In this study, a soft computing method is proposed to predict the accelerating behavior of driver-vehicle-unit in the genuine traffic stream that is collected on the California urban roads by US Federal Highway Administration’s NGSIM. This method is used to predict DVU velocity for different time-steps ahead using adaptive neuro-fuzzy inference system (ANFIS) predicator. To evaluate the performance of proposed method, standard time series forecasting approach called autoregressive (AR) model is considered as a rival method. The predictions accuracy of two methods are compared using root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination or R-squared (R2) as three error criteria. The results demonstrate the adequacy of proposed algorithm on real traffic information and the predicted speed profile shows that ANFIS is able to predict the dynamic traffic changes. The proposed model can be employed in intelligent transportation systems (ITS), collision prevention systems (CPS) and etc

Finite-Time Disturbance-Observer-Based Integral Terminal Sliding Mode Controller for three-phase Synchronous Rectifier

Lisätään kokoteksti kun saatavilla IEEE verkkopalvelussa / mmThis article is concerned with the design of a finite-time disturbance-observer-based integral terminal sliding mode controller for the effective performance of three-phase synchronous rectifiers. The proposed control technique is developed based on the conventional synchronous reference frame model of the three-phase grid-connected converter, and the system dynamics is described in terms of a time-varying non-linear state equation. The variation of DC-load is considered as a disturbance. Therefore, a combination of a fast disturbance observer and an integral terminal sliding mode controller is utilized to produce the reference value of the direct axis for the current control loop. In this research, by employing Lyapunov stability theorem in the theoretical analysis and by numerical simulations, it is confirmed that the proposed closed-loop system is stable and the states converge to desired values in finite time even in the presence of load disturbance and control input saturation. The integral terminal sliding mode controller is utilized to maintain a robust performance along with a faster response of the converter. In order to demonstrate the performance ability of the proposed control scheme under real condition, an AC power source, impregnated with low order harmonics, is assumed. A real-time laboratory setup of the synchronous rectifier has been developed successfully, and the effective performance of the proposed control technique is fully proven.Peer reviewe