Experiments in identification and control of flexible-link manipulators

Interest in the study of flexible-link manipulators for space-based applications has risen strongly in recent years. Moreover, numerous experimental results have appeared for the various problems in the modeling, identification and control of such systems. Nevertheless, relatively little literature has appeared involving laboratory verification of tuning controllers for certain types of realistic flexible-link manipulators. Specifically flexible-link manipulators which are required to maintain endpoint accuracy while manipulating loads that are possibly unknown and varying as they undergo disturbance effects from the environment and workspace. Endpoint position control of flexible-link manipulators in these areas are discussed, with laboratory setups consisting of one and two-link manipulators

Dynamic modeling of a hybrid electric drivetrain for fuel economy, performance and driveability evaluations

ABSTRACT Both automakers and customers keep on pursuing better fuel economy, performance and driveability. A "mild" hybrid drivetrain is of great interest due to its potential capability on improving these targets. This drivetrain contains a spark ignition (SI) engine, an integrated starter/alternator (ISA), a torque converter (TC), a continuously variable transmission (CVT), a final drive (FD), a driveshaft, a brake-by-wire (BBW) system and wheels. While the challenge is to model and to develop an optimal control algorithm for this hybrid electric vehicle (HEV), this paper will focus only on the modeling aspect. Model-based control design and the nature of human perceptible driveability issues require low-frequency dynamic models. Therefore, a nonlinear control-oriented model which is sufficiently accurate but not excessively complicated is proposed here. Simulation results demonstrate that this model is effective to capture the main behaviors of vehicle dynamics and to evaluate fuel economy, performance and driveability objectively. NOMENCLATUR

Development of decision support system for the diagnosis of arthritis pain for rheumatic fever patients: Based on the fuzzy approach

Developing a Decision Support System (DSS) for Rheumatic Fever (RF) is complex due to the levels of vagueness, complexity and uncertainty management involved, especially when the same arthritis symptoms can indicate multiple diseases. It is this inability to describe observed symptoms precisely that necessitates our approach to developing a Decision Support System (DSS) for diagnosing arthritis pain for RF patients using fuzzy logic. In this paper we describe how fuzzy logic could be applied to the development of a DSS application that could be used for diagnosing arthritis pain (arthritis pain for rheumatic fever patients only) in four different stages, namely: Fairly Mild, Mild, Moderate and Severe. Our approach employs a knowledge-base that was built using WHO guidelines for diagnosing RF, specialist guidelines from Nepal and a Matlab fuzzy tool box as components to the system development. Mixed membership functions (Triangular and Trapezoidal) are applied for fuzzification and Mamdani-type is used for the fuzzy reasoning process. Input and output parameters are defined based on the fuzzy set rules

Architectures for Phase Variation Compensation in AFR Control

Abstract-To counteract the binary nature of an exhaust gas oxygen sensor and to improve the efficiency of a three way catalyst, the delivered fueling quantity is typically dithered around stoichiometry for gasoline internal combustion engines. Determining whether the switching sensor reads rich or lean because of the dithering or a disturbance is very difficult. This problem is often overcome by estimating the plant delay and tracking a reference signal. Without an accurate delay estimate, however, the benefits of this type of control architecture are limited. This paper compares two switching sensor based control architectures, phase lock loop and duty cycle control, for tracking a periodic reference air-to-fuel ratio signal while overcoming the uncertainty of a dynamic plant delay estimate