Automatic Control of Clutch Engagement and Slip for Hybrid Vehicle

This paper develops a design of an automatic controller of clutch engagement and slip regulation for hybrid electrical vehicle (HEV) using fuzzy logic. The motivation for the use of fuzzy logic control in this study is its ability to handle the system based on uncertain and imprecise input information. Fuzzy logic can reduce the difficulty of mathematical modeling for complex system and can provide a smooth and fast clutch engagement. Fuzzy logic controller can be also used to reduce the vehicle vibration via regulating the slip between two clutch disks. Simulations for the new controller are conducted with Matlab Simulink. Results show that the system can achieve clutch engagement with low jerk and high comfort with considerable vibration reduction

Application of Artificial Intelligence in Smart Kitchen: Application of Artificial Intelligence in Smart Kitchen

This study discusses, how smart home automation system is designed and employed in kitchen appliances such as refrigerators, stoves and microwave which are used for daily life. Although smart systems support physically disabled but also is a great service for people with busy schedules, hectic life styles and even students. It has done with the aid of wireless networks, smart phones and internet. Android application would provide the user-friendly interface which will control kitchen equipment with various sensors and barcodes. Here Arduino Uno module is utilized to perform the same tasks. It collects and processes the signals and sends them wirelessly to raspberry PI module, using a zig bee unit. The raspberry PI runs an Open HAB server which the appliances are controlled with smartphones. Experimental results and computed solutions from MATLAB are sent back to autonomous mobile robots for their executions and interactions in real-time

Autonomous Driving Vehicles and Control System Design

Autonomous driving vehicles and the control system design have been undergoing rapid changes in the last decade and affecting the concept and behaviour of human traffic. However, the control system design for autonomous driving vehicles is still a great challenge since the real vehicles are subject to enormous dynamic constraints depending on the vehicle physical limitations, environmental constraints and surrounding obstacles. This paper presents a new scheme of nonlinear model predictive control subject to softened constraints for autonomous driving vehicles. When some vehicle dynamic limitations can be converted to softened constraints, the model predictive control optimizer can be easier to find out the optimal control action. This helps to improve the system stability and the application for further intelligent control in the future. Simulation results show that the new controller can drive the vehicle tracking well on different trajectories amid dynamic constraints on states, outputs and inputs

Fuzzy Logic Control for a Ball and Beam System: Fuzzy Logic Control for a Ball and Beam System

This paper presents the design of a PID controller and two different fuzzy logic controllers of Mamdani and Sugeno to control the non-linear model of a ball rolling on a beam using Matlab and Malab Simulink. Results from simulations are analyzed to provide comprehensive understandings on the ability use of different controllers. The paper also investigates the performance ability of these controllers for tracking on different references such as step, sinusoidal and square waves. Finally, advantages and disadvantages of each control strategies are concluded

Feasible Path Planning for Autonomous Vehicles

The objective of this paper is to find feasible path planning algorithms for nonholonomic vehicles including flatness, polynomial, and symmetric polynomial trajectories subject to the real vehicle dynamical constraints. Performances of these path planning methods are simulated and compared to evaluate the more realistic and smoother generated trajectories. Results show that the symmetric polynomial algorithm provides the smoothest trajectory. Therefore, this algorithm is recommended for the development of an automatic control for autonomous vehicles

Feasible Path Planning for Autonomous Vehicles

The objective of this paper is to find feasible path planning algorithms for nonholonomic vehicles including flatness, polynomial, and symmetric polynomial trajectories subject to the real vehicle dynamical constraints. Performances of these path planning methods are simulated and compared to evaluate the more realistic and smoother generated trajectories. Results show that the symmetric polynomial algorithm provides the smoothest trajectory. Therefore, this algorithm is recommended for the development of an automatic control for autonomous vehicles

Model Predictive Control for Autonomous Vehicle Tracking

This study develops model predictive control (MPC) schemes for controlling autonomous vehicles tracking on feasible trajectories generated from flatness or polynomial equations. All of the vehicle online moving parameters including coordinate positions, body orientation angle, and steering angle are included into the MPC optimizer for calculating the real-time optimal inputs for the vehicle linear velocity and its steering velocity to minimize the errors between the desired and the actual course of travel. The use of MPC can simplify and eliminate the complexity of controller design since MPC can work itself as a system modelling controller. MPC can also handle on-line the constraints of any variables exceeding their limits. However the high computational demands are the main challenge for this method applying for the real applications

Modeling and Control Strategy for Hybrid Electrical Vehicle

This chapter reviews the developments and configurations of hybrid electrical vehicles. A classic model for a parallel hybrid electrical vehicle is chosen and modeled. Model predictive controllers and simulations for this vehicle model are applied to control the vehicle speed and power to check the ability of the system to handle the transitional period for the automatic clutch engagement from the electrical driving to the internal combustion engine (ICE) driving. The chapter produces potential model predictive control considerations to achieve the optimal real-time control actions subject to the vehicle physical constraints. The new system can be applied for electronic control units in real hybrid vehicle powertrains