Simulation of a four-car elevator operation using MATLAB

The design and simulation of a four-cars-elevator controller in a nine storey building is described in this paper. The design and simulation were accomplished using MATLABTM fuzzy logic toolbox. The logic of the controller of a multi-car elevator has to be designed in such a way that the average waiting time is minimized while keeping the energy consumption of the system optimum. This is a multi-criteria optimization problem in stochastic environment and is best approached through Artificial Intelligent techniques. The work here focuses mainly on extracting the rules to minimize factors (i.e. waiting time, travelled distance and riding time) in order to minimize the energy consumed by the system. In this paper a detailed algorithm is presented to achieve the multiple objectives of minimizing the waiting time and the distance travelled simultaneously. This was accomplished by distributing different weightage to different quantities and then minimizing a combined cost. A simulator has been built with interactive GUI in Matlab to evaluate the efficacy of the algorithm

Simulation of a four-car elevator operation using MATLAB

The design and simulation of a four-cars-elevator controller in a nine storey building is described in this paper. The design and simulation were accomplished using MATLABTM fuzzy logic toolbox. The logic of the controller of a multi-car elevator has to be designed in such a way that the average waiting time is minimized while keeping the energy consumption of the system optimum. This is a multi-criteria optimization problem in stochastic environment and is best approached through Artificial Intelligent techniques. The work here focuses mainly on extracting the rules to minimize factors (i.e. waiting time, travelled distance and riding time) in order to minimize the energy consumed by the system. In this paper a detailed algorithm is presented to achieve the multiple objectives of minimizing the waiting time and the distance travelled simultaneously. This was accomplished by distributing different weightage to different quantities and then minimizing a combined cost. A simulator has been built with interactive GUI in Matlab to evaluate the efficacy of the algorithm

Adaptive cruise control of a passenger car using hybrid of sliding mode control and fuzzy logic control

ABSTRACT This paper focuses on the design of adaptive cruise control (ACC) which was implemented on a passenger car based on sliding mode control (SMC) of throttle valve combining with fuzzy logic control of brake pedal. An important feature of the new adaptive cruise control system is the ability to maintain a proper inter-vehicle gap based on the speed of host vehicle and headway way. There are three important inputs of the ACC system, speed of host vehicle read from electronic control unit (ECU), headway time set by driver, and actual gap measured from a laser scanner. The ACC processes these three inputs in order to calculate distance error and relative velocity which are used as the two inputs for both SMC and fuzzy controller. The SMC determines throttle valve angle while fuzzy controller determines the brake command to maintain a proper gap based on current speed of the leading vehicle and the desired time headway. The experimental results show that the proposed controller can perform efficiently in ACC of a passenger car

Autonomous Vehicle Coordination with Wireless Sensor and Actuator Networks

A coordinated team of mobile wireless sensor and actuator nodes can bring numerous benefits for various applications in the field of cooperative surveillance, mapping unknown areas, disaster management, automated highway and space exploration. This article explores the idea of mobile nodes using vehicles on wheels, augmented with wireless, sensing, and control capabilities. One of the vehicles acts as a leader, being remotely driven by the user, the others represent the followers. Each vehicle has a low-power wireless sensor node attached, featuring a 3D accelerometer and a magnetic compass. Speed and orientation are computed in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leader's movement pattern. We report in detail on all development phases, covering design, simulation, controller tuning, inertial sensor evaluation, calibration, scheduling, fixed-point computation, debugging, benchmarking, field experiments, and lessons learned

Making Transport Safer: V2V-Based Automated Emergency Braking System

An important goal in the field of intelligent transportation systems (ITS) is to provide driving aids aimed at preventing accidents and reducing the number of traffic victims. The commonest traffic accidents in urban areas are due to sudden braking that demands a very fast response on the part of drivers. Attempts to solve this problem have motivated many ITS advances including the detection of the intention of surrounding cars using lasers, radars or cameras. However, this might not be enough to increase safety when there is a danger of collision. Vehicle to vehicle communications are needed to ensure that the other intentions of cars are also available. The article describes the development of a controller to perform an emergency stop via an electro-hydraulic braking system employed on dry asphalt. An original V2V communication scheme based on WiFi cards has been used for broadcasting positioning information to other vehicles. The reliability of the scheme has been theoretically analyzed to estimate its performance when the number of vehicles involved is much higher. This controller has been incorporated into the AUTOPIA program control for automatic cars. The system has been implemented in Citroën C3 Pluriel, and various tests were performed to evaluate its operation

A layered fuzzy logic controller for nonholonomic car-like robot

A system for real time navigation of a nonholonomic car-like robot in a dynamic environment consists of two layers is described: a Sugeno-type fuzzy motion planner; and a modified proportional navigation based fuzzy controller. The system philosophy is inspired by human routing when moving between obstacles based on visual information including right and left views to identify the next step to the goal. A Sugeno-type fuzzy motion planner of four inputs one output is introduced to give a clear direction to the robot controller. The second stage is a modified proportional navigation based fuzzy controller based on the proportional navigation guidance law and able to optimize the robot's behavior in real time, i.e. to avoid stationary and moving obstacles in its local environment obeying kinematics constraints. The system has an intelligent combination of two behaviors to cope with obstacle avoidance as well as approaching a target using a proportional navigation path. The system was simulated and tested on different environments with various obstacle distributions. The simulation reveals that the system gives good results for various simple environments