Experiment-Based Teaching in Advanced Control Engineering

Braking system plays a major safety feature in any vehicle; it is considered as one of the key points in safe motoring. The main objective of a good braking system is to provide safe stopping, in addition to the fact that it provides repeatable stopping. Anti-lock Braking System (ABS) is one of the recently developed safety feature used in a vehicle in order to prevent wheels from locking. This article proposes an intelligent adaptive fuzzy logic (FL) genetic algorithm (GA) controller for the Anti-Lock Braking System (ABS) to control the braking of a vehicle by controlling the slip ratio based on quarter car model. The main objective of the proposed controller is to prevent wheels from locking, which is a major safety issue in any braking system for different road conditions. The proposed controller consists of a Fuzzy Logic (FL) Genetic Algorithm (GA) based controller. The Genetic Algorithm is utilized in tuning the membership functions of the Fuzzy logic controller, which is developed to calculate the amount of reduction of the applied torque due to applying brake based on the error of the measured slip ratio signal compared with the desired one. Then a Genetic Algorithm (GA) is utilized in tuning the Fuzzy Logic parameters, which are the center of the Gaussian membership function, and the spread (б). The simulation results of the proposed fuzzy logic genetic algorithm controller are compared with both the results of the Fuzzy logic controller and the PD controller developed in previous work of the authors. The simulation results present many advantages of using Fuzzy Logic Genetic Algorithm based controller over both the Fuzzy Logic and the PD controllers, and the simulation is carried out at different road conditions, namely dry, wet and icy road surface.

The antilock braking system (ABS) is designed to optimize braking effectiveness and maintain steerability; however, the ABS performance will be degraded in the case of severe road conditions. In this study, a self-learning fuzzy sliding-mode control (SLFSMC) design method is proposed for ABS. The SLFSMC ABS will modulate the brake torque for optimum braking. The SLFSMC system is comprised of a fuzzy controller and a robust controller. The fuzzy controller is designed to mimic an ideal controller and the robust controller is designed to compensate for the approximation error between the ideal controller and the fuzzy controller. The tuning algorithms of the controller are derived in the Lyapunov sense; thus, the stability of the system can be guaranteed. Also, the derivation of the proposed SLFSMC ABS does not need to use a vehicle-braking model. Simulations are performed to demonstrate the effectiveness of the proposed SLFSMC ABS in adapting to changes for various road conditions.

The 2021 4th International Conference on Advanced Algorithms and Control Engineering (ICAACE 2021) was successfully held online through Zoom on Jan. 29-31, 2021. Due to COVID-19 and the pandemic-related nationwide lockdowns and other coordinated restrictive measures, the organizers decided to hold a conference in a virtual format with the organization of access for all participants to the presented reports with comprehensive discussion for ensuring the event at a high scientific level.ICAACE 2021 is to bring together innovative academics and industrial experts in the field of advanced algorithms and control engineering to a common forum. The primary goal of the conference is to promote research and developmental activities in advanced algorithms and control engineering. And another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in advanced algorithms and control engineering and related areas.The safety and well-being of all conference participants is our priority. The COVID-19 is unpredictable, so conference postponement met uncertainty, while many scholars and researchers want to attend this long-waited conference and have academic exchanges with their peers. But I want to note that there are no barriers to science, and we continue to work on our research areas remotely, using modern technical means. Under this situation, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches.More than 110 participants attended the meeting. We were greatly honored to have invited Prof. Ghassan Beydoun, from University of Technology Sydney and A. Prof. Antti Rasila, from Technion – Israel Institute of Technology, Guangdong Technion to serve as our General Conference Chair. There were over 20 experts and scholars in the area of Advanced Algorithms and Control Engineering representing different famous universities and institutes around the globe to form Conference Committees.List of Committee members are available in the pdf.

This paper deals with the control of an Anti-lock Brake System (ABS) assisted with an active suspension. The main objective is to track the slip rate of a car and ensure a shorter distance in the braking process. For the ABS subsystem an integral nested sliding mode controller based on the block control principle is designed. On the other hand, for the active suspension subsystem a sliding mode controller based on regular form and linear geometric techniques is proposed. Both closed-loop subsystems are robust in presence of matched and unmatched perturbations. To show the performance of the proposed control strategy, a simulation study is carried on, where results show good behavior of the ABS with active suspension under variations in the road.

Electric vehicles (EVs) have the advantage of using both regenerative braking and mechanical braking to produce the necessary braking torque. Currently, vehicles use Anti-lock Braking Systems (ABS) which provide intermittent braking torque to the vehicle and thereby prevents skidding or slipping of the vehicle by ensuring pure rolling of the wheels. Regenerative braking on the other hand generates the necessary braking torque by converting the kinetic energy of the vehicle to electrical energy via the motor(s). To coordinate both braking systems, the conventional method involves the use of crisp logic controllers that depend on lookup tables containing the data about various aspects of the vehicle such as battery state of charge (SoC), brake pedal input etc., to enable co-operative control of both systems. This paper presents a combined implementation of both braking systems in electric vehicles using separate fuzzy controllers for both ABS and regenerative braking. The control scheme is implemented such that when regenerative braking is not capable of providing enough braking torque, braking will be one by ABS. Two fuzzy logic controllers have been used, one to implement regenerative braking and the other to implement mechanical braking via ABS. If such a control algorithm is used, the driver will not have to manually adjust the level of regenerative braking in the vehicle and such an approach ensures that the braking process of an EV is smooth, safe and efficient. It also reduces the wear and tear of the brake pads thereby extending their life and reducing the cost of maintenance and by extension the total cost of ownership of the vehicle. The results demonstrate that the controllers for both ABS and regenerative braking can coordinate efficiently and reliably to produce proper and safe braking of the vehicle.

This paper focuses on the design of a fuzzy fractional Proportional-Integral-Derivative (PID) controller. The controller which is based on the conventional PID controller is modified to be P1–α I1–β D1–γ with the objective to make the controller more flexible and robust to the changes in input and the parameters. In this control structure, the fuzzy logic control acts as the tuner for the parameters α, β and γ, so that the overall controller parameter can be varied and converge better to the changes in a system. The designed fuzzy fractional PID controller is applied to a validated antilock braking system model using an electronic wedge brake system as the actuator. Numerous numerical simulations and comparisons with other fractional PID/conventional PID controllers show that the fuzzy fractional PID controller can not only ensure good control performance with respect to reference input but also improve the system robustness with respect to model uncertainties.

The yaw angle control of a wind turbine allows maximization of the power absorbed from the wind and, thus, the increment of the system efficiency. Conventionally, classical control algorithms have been used for the yaw angle control of wind turbines. Nevertheless, in recent years, advanced control strategies have been designed and implemented for this purpose. These advanced control strategies are considered to offer improved features in comparison to classical algorithms. In this paper, an advanced yaw control strategy based on reinforcement learning (RL) is designed and verified in simulation environment. The proposed RL algorithm considers multivariable states and actions, as well as the mechanical loads due to the yaw rotation of the wind turbine nacelle and rotor. Furthermore, a particle swarm optimization (PSO) and Pareto optimal front (PoF)‐based algorithm have been developed in order to find the optimal actions that satisfy the compromise between the power gain and the mechanical loads due to the yaw rotation. Maximizing the power generation and minimizing the mechanical loads in the yaw bearings in an automatic way are the objectives of the proposed RL algorithm. The data of the matrices Q (s,a) of the RL algorithm are stored as continuous functions in an artificial neural network (ANN) avoiding any quantification problem. The NREL 5‐MW reference wind turbine has been considered for the analysis, and real wind data from Salt Lake, Utah, have been used for the validation of the designed yaw control strategy via simulations with the aeroelastic code FAST.

Hierarchical [formula omitted] fuzzy-neural control of anti-lock braking system and active suspension in a vehicle

This paper includes the design and implementation of a fuzzy logic controller for level control system to overcome the limitations of the conventional controllers. Mostly preferred controller is a Proportional Integral Derivative (PID) controller which has fixed control parameters. Selection of wrong parameters in a PID design results into uncontrollable process. But fuzzy logic controller is a rule based. These rules are designed on the basis of heuristic information provided by an operator. Process model is identified and used for the design of Proportional Integral (PI) and fuzzy controllers by using MATLAB. To evaluate the performance of both the controllers, it is tested on a real time system with the help of Real Time Workspace (RTW) module of a MATLAB. From real time results it is understood that, fuzzy controller provides more stable and improved output for level control. Even fuzzy controller has ability to remove overshoot which is present in PI controller. Also it requires less settling time than PI controller. Hence fuzzy control system is more relevant control strategy to control the level.

Anti-lock and also Anti-slip braking system controller designed for stability enhancement of vehicle during braking and turning circumstances is presented. Using available signals, a novel structure is proposed for vehicle stability improvement encountering critical braking conditions such as braking on slippery or μ-split road surfaces. In conventional vehicles, undesired lane changes may occur due to equal dispatch of braking torques to all wheels simultaneously. Also, intensive pressure on brake pedal can bring about wheels lockup which results in vehicle instability and undesired lane changes. The Anti-Lock Braking System (ALBS) and also Anti-Slip Braking System (ASBS) can serve as a driver assist system in vehicle path correction facing critical driving conditions during braking and turning round on different road surfaces. For these purposes, at first, a reference yaw angle is used for predicting of stable vehicle path. Then, slip of each wheel will be controlled by an Anti-Slip fuzzy controller which has been designed for each wheel. Also, a sliding mode controller has been designed so as to control the yaw angle concerning the yaw error where the yaw error is resulted from the difference between the reference value and actual yaw angle. Then, the difference between the left and the right wheels braking torques is used by the sliding mode controller in order to decrease the yaw error. Considering a model of Three-Degree-of-Freedom for chassis modeling and One-Degree-of-Freedom Dugoff's tire model for each wheel, a series of Matlab/Simulink simulation results will be presented in order to validate the effectiveness of the proposed controller.

The anti-lock braking system (ABS) can improve the safety of the vehicle. As the ABS assisted device, electronic brake force distribution (EBD) can automatically regulate the braking force distribution among the wheels of the vehicle, so that the wheels can reach the start condition of ABS at the same time, which can make better performance on braking distance and braking stability. In this paper, a mathematical model of the EBD system is built and the control system based on fuzzy control is designed. The results of the simulation on straight road show that the EBD control system is effective in the braking force distribution control.

Focusing on the passive network, this paper analyzes the discrete mathematical model and its control strategy of the passive terminal of the VSC-HVDC system on the basis of the α-β frame of axes. The method of the functioning-fuzzy-subset inference is introduced into automatic regulation of the proportional integral (PI) parameters. Based on the digital signal processor (DSP), the fuzzy PI control method is adopted and the VSC-HVDC experiment device is constructed. The experiment of constant AC voltage control based on fuzzy adaptive PI controller in transmitting power to passive network via VSC-HVDC system is developed. The experimental results show that the fuzzy controller with adaptive PI parameters for AC voltage control of VSC-HVDC system is effective.

<div>As the basic function of the active safety configuration of a vehicle, the anti-lock braking system will compromise the driving safety if it fails. Based on the self-designed electro-hydraulic braking system, this article proposes an anti-lock brake redundant control architecture. The electro-hydraulic braking system is mainly composed of four parts: a brake pedal unit, a hydraulic drive unit, a brake execution unit, and a control unit. The mechanical structure is compact and exquisite, and the system has the function of precise and adjustable hydraulic pressure. The control architecture adopts a hierarchical control design, which is mainly composed of an upper wheel slip rate controller and a lower hydraulic pressure controller. Both the upper and lower controllers use a sliding mode variable structure control to improve the robustness and accuracy of the control. The upper slip rate controller outputs the desired master cylinder hydraulic pressure with the optimum slip rate of the rear wheels of the vehicle as the control target. The lower hydraulic pressure controller outputs the desired torque of the motor with the desired master cylinder hydraulic pressure as the target and achieves hydraulic pressure regulation of the master cylinder by controlling the motor motion. To verify the effectiveness of the algorithm, co-simulation and a hardware-in-the-loop test platform are built. Anti-lock braking tests are carried out under different typical working conditions: low-adhesion road, high-adhesion road, butt road, and split road. The results show that when the anti-lock braking system fails, the redundant control algorithm can achieve effective slip rate control under different driving road conditions and meets the anti-lock brake redundant control requirements. This study provides a reference for the development of low-cost anti-lock braking redundancy functions for vehicles equipped with electro-hydraulic braking systems.</div>

This paper presents the development of a PID controller for an Antilock Braking System (ABS) using vehicle longitudinal model. A Five Degree of Freedom (5-DOF) vehicle longitudinal dynamic model was derived and integrated with an analytical tyre dynamics, the Magic Tyre model. Several transient handling tests are performed such as sudden acceleration and sudden braking test to validate the vehicle model. The model is used as a plant to develop an antilock braking system to control longitudinal slip and reduce the stopping distance. A hydraulic brake model was developed as the brake actuator to produce brake torque. A conventional PID controller has been implemented to deal with the strong nonlinearity in the design of ABS controller. The proposed ABS control structure is shown able to significantly reduce stopping distance and control the longitudinal slip during heavy braking.

In modern power systems, photovoltaic (PV) generation plays a vital role in sustainable energy supply. PV systems generate DC power, which is converted to AC using built-in converters for grid integration. The quality of power injected into the grid is crucial, especially in the presence of plug-in electric vehicles (PEVs) and non-linear loads, which introduce harmonics and dynamic disturbances. To enhance power quality, advanced control strategies are employed. This paper presents a comparative study of direct-quadrature (DQ) control techniques using traditional proportional-integral (PI) controllers and fuzzy logic controllers (FLCs) in a grid-connected PV system. The DQ control method simplifies the regulation of active and reactive power by transforming three-phase signals into a rotating reference frame. While PI controllers are widely used, they often struggle with non-linearities and load variations. FLCs, on the other hand, offer adaptive control without requiring precise mathematical models, making them more effective under dynamic conditions. The system under study includes PV generation, PEVs, and non linear loads. Performance metrics such as total harmonic distortion (THD), voltage stability, and power factor are analyzed. Results show that fuzzy controllers significantly improve power quality and system response.