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Energy consumption of a vehicle is greatly influenced by its driving behavior in highly interacting urban traffic. Strategies for fuel efficient driving have been studied and experimented with in various conceptual frameworks. This paper presents a novel control system to drive a vehicle efficiently on roads containing varying traffic and signals at intersections for improved fuel economy. The system measures the relevant information of the current road and traffic, predicts the future states of the preceding vehicle, and computes the optimal vehicle control input using model predictive control (MPC). A typical control objective is chosen to maximize fuel economy by regulating a safe head-distance or cruising at the optimal velocity under bounded driving torque condition. The proposed vehicle control system is evaluated in urban traffic containing thousands of diverse vehicles using the microscopic traffic simulator AIMSUN. Simulation results show that the vehicles controlled by the proposed MPC method significantly improve their fuel economy. [ABSTRACT FROM PUBLISHER]

The performance of PID and fuzzy controllers is compared in a commercial device normally used in model airplanes: the S3004 Futaba Servo. The controller circuit, the power amplifier and the sensor are modified, and then the motor is observed. Next, a root-locus method is used to tune a PID for angular position control. Simulations and control are made in Matlab's Simulink, and also a data acquisition card (PCI 6024E) is used. Results are analyzed at the end of the document: although the PID controller works better, the fuzzy controller shows a similar performance. [ABSTRACT FROM AUTHOR]

In this paper the design and application of a control algorithm is discussed to control the test conditions within plenum chamber and the test section of a supersonic blow-down, variable throat wind tunnel at the University of Alabama. The artificially intelligent controller algorithm was designed using a gain scheduled Proportional-Integral-Differential (PID) control approach. The PID controller was augmented to work with time variant properties of the control problem by determining a functional form of the integral term of the controller from the governing equations of the tunnel. The controller was optimized using genetic algorithms (GA) on a neural network (NN) model of the tunnel and was compared to a conventional PID controller using the same NN model. The process was repeated for different throat settings to find the control gains for each setting. The controller algorithm was next applied to the actual wind tunnel at different throat settings and the results were compared. The optimized controller is proven to work very well at every throat setting. [ABSTRACT FROM AUTHOR]

This article presents a practical approach to engineering a robot to effectively navigate in an urban environment. Inherent in this approach is the use of relatively simple sensors, actuators, and processors to generate robot vision, intelligence, and planning. Sensor data are fused from multiple low-cost, two-dimensional laser scanners with an innovative rotational mount to provide three-dimensional coverage with image processing using both range and intensity data. Information is combined with Doppler radar returns to yield a world view processed by a context-based reasoning control system to yield tactical mission commands forwarded to traditional proportional-integral-derivative (PID) control loops. As an example of simplicity and robustness, steering control successfully utilized a relatively simple follow-the-carrot guidance approach that has been successfully demonstrated at speeds of 60 mph (97 km/h). The approach yielded a robot that reached the finals of the Urban Challenge and completed approximately 2 h of the event before being forced to withdraw as a result of a global positioning system data failure. © 2008 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Focuses on the PID controllers in the U.S. Illustration of the typical dynamic behavior of control loop variables of an over-damped PID controlled process; Characteristics of the PID controllers; Types of PID controllers.