Intelligent control based on fuzzy logic and neural net theory

In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment

Vehicle Based Intersection Management with Intelligent Agents

Signal-based intersection management will change when vehicles with intelligent capability are available in the future. Intelligent agents embedded in vehicle software will be responsible for vehicle control and route guidance. Intersection management can be achieved through the collaboration of these agents, without a centralized control infrastructure. This research focuses on the use of distributed multi-agent systems to provide microscopic adaptive control which might reduce traffic delay and chances of collisions at intersections. A hypothesized Mobile Ad-hoc Network provides communication links to connect the agents.Intelligent Agents, Adaptive Intersection Control

WSN and RFID integration to support intelligent monitoring in smart buildings using hybrid intelligent decision support systems

The real time monitoring of environment context aware activities is becoming a standard in the service delivery in a wide range of domains (child and elderly care and supervision, logistics, circulation, and other). The safety of people, goods and premises depends on the prompt reaction to potential hazards identified at an early stage to engage appropriate control actions. This requires capturing real time data to process locally at the device level or communicate to backend systems for real time decision making. This research examines the wireless sensor network and radio frequency identification technology integration in smart homes to support advanced safety systems deployed upstream to safety and emergency response. These systems are based on the use of hybrid intelligent decision support systems configured in a multi-distributed architecture enabled by the wireless communication of detection and tracking data to support intelligent real-time monitoring in smart buildings. This paper introduces first the concept of wireless sensor network and radio frequency identification technology integration showing the various options for the task distribution between radio frequency identification and hybrid intelligent decision support systems. This integration is then illustrated in a multi-distributed system architecture to identify motion and control access in a smart building using a room capacity model for occupancy and evacuation, access rights and a navigation map automatically generated by the system. The solution shown in the case study is based on a virtual layout of the smart building which is implemented using the capabilities of the building information model and hybrid intelligent decision support system.The Saudi High Education Ministry and Brunel University (UK

Distributed Control Monitoring System Using Controller Area Network

Network-based systems are widely used in many application areas such as intelligent systems and distributed systems. To design distributed systems, distributed information processing and intelligent terminals are required. This system can be integrated efficiently into the present automation management network system. This paper presents the development of distributed network control system based on Controller Area Network (CAN). The system includes three micro controller based boards with PIC18F458 with Analog-to-Digital (ADC) interface for analog measurement. The first board use a ADC input sensor for revolution per minute (RPM) calculation of motor, while the second board uses to capture temperature and the third board which connects to PC for Graphical User Interface (GUI) display for monitoring system. The three boards communicate via the existing data communications network. These micro controller based hardware devices are driven by programs written in the C programming language, while LabVIEW provides the user-control interface. This paper will present the methodology, design approach and results of the online monitoring system

Nano-satellite attitude control system based on adaptive neuro-controller

The current research focuses on designing of an intelligent controller for attitude control system (ACS) of nano-satellite. The nanosatellite namely Innovative Satellite (InnoSAT) was organized by Agensi Angkasa Negara (ANGKASA) to attract the interest of Malaysian universities in satellite development.In this study, an intelligent controller based on Hybrid Multi Layered Perceptron (HMLP) network was developed. The network used model reference adaptive control (MRAC) system as a control scheme to control a time varying systems where the performance specifications are given in terms of a reference model.The Weighted Recursive Least Square (WRLS) algorithm will adjust the controller parameters to minimize error between the plant output and the model reference output.The objective of this paper is to analyze the tracking performance of ANC based on HMLP network and ANC based on standard MLP network for controlling a satellite attitude. The simulation results indicate that ANC based on HMLP network gave better performance than ANC based on standard MLP network

Reinforcement learning based adaptive control method for traffic lights in intelligent transportation

Addressing the requirements and challenges of traffic light control, a reinforcement learning based adaptive optimal control model for traffic lights in intelligent transportation systems is proposed. In the model design, we combined Markov decision process, Q-learning algorithm, and Deep Q-Learning Network (DQN) control theory to establish a comprehensive signal light Adaptive Optimal Control of Signal Lights in Intelligent Transportation Systems (AOCITL) control model. Through simulation experiments on the model and the application of actual road scene data, we have verified the superiority of the model in improving traffic system efficiency and reducing traffic pressure. The experimental results show that compared with traditional fixed cycle signal light control, the adaptive optimal control model based on reinforcement learning can significantly improve the traffic efficiency of roads, reduce the incidence of traffic accidents, and enhance the overall operational effectiveness of urban transportation systems. The proposed method is possible to further optimize the model algorithm, expand its application scope, and promote the development and practical application of intelligent transportation systems

Advanced satellite workstation: An integrated workstation environment for operational support of satellite system planning and analysis

A prototype integrated environment, the Advanced Satellite Workstation (ASW), is described that has been developed and delivered for evaluation and operator feedback in an operational satellite control center. The current ASW hardware consists of a Sun Workstation and Macintosh II Workstation connected via an ethernet Network Hardware and Software, Laser Disk System, Optical Storage System, and Telemetry Data File Interface. The central mission of ASW is to provide an intelligent decision support and training environment for operator/analysts of complex systems such as satellites. There have been many workstation implementations recently which incorporate graphical telemetry displays and expert systems. ASW is a considerably broader look at intelligent, integrated environments for decision support, based upon the premise that the central features of such an environment are intelligent data access and integrated toolsets. A variety of tools have been constructed in support of this prototype environment including: an automated pass planner for scheduling vehicle support activities, architectural modeler for hierarchical simulation and analysis of satellite vehicle subsystems, multimedia-based information systems that provide an intuitive and easily accessible interface to Orbit Operations Handbook and other relevant support documentation, and a data analysis architecture that integrates user modifiable telemetry display systems, expert systems for background data analysis, and interfaces to the multimedia system via inter-process communication

Wireless technologies for Controlling a Traffic Lights Prototype

This paper presents a traffic light control system based on wireless communication technologies. Traffic density is increasing at an alarming rate in developing countries which calls for intelligent dynamic traffic light control systems to replace the conventional manual and time based ones. The approach followed in this paper is based in a secure wireless sensor network to feed real time data to the intelligent traffic light control. A physical prototype was implemented for experimental validation. The physical prototype showed robustness against local failures or unforeseen cases showing that the communication between modules keeps an acceptable packets received ratio

Wireless networks for traffic light control on urban and aerotropolis roads

This paper presents a traffic lights system based on wireless communication, providing a support infrastructure for intelligent control in smart cities and aerotropolis scope. An aerotropolis is a metropolitan subregion which infrastructure is centered around an airport [1]. Traffic intensity is increasing all over the world. Intelligent dynamic traffic lights system control are sought for replacing classic conventional manual and time based systems. In this work a wireless sensors network is designed and implemented to feed real time data to the intelligent traffic lights systems control. A physical prototype is implemented for experimental validation outside laboratory environment. The physical prototype shows robustness against unexpected issues or local failures. Results are positive in the scope of the experiences made and promising in terms of extending the tests to larger areas