Position Error Compensation via a Variable Reluctance Sensor Applied to a Hybrid Vehicle Electric Machine

In the automotive industry, electromagnetic variable reluctance (VR) sensors have been extensively used to measure engine position and speed through a toothed wheel mounted on the crankshaft. In this work, an application that already uses the VR sensing unit for engine and/or transmission has been chosen to infer, this time, the indirect position of the electric machine in a parallel Hybrid Electric Vehicle (HEV) system. A VR sensor has been chosen to correct the position of the electric machine, mainly because it may still become critical in the operation of HEVs to avoid possible vehicle failures during the start-up and on-the-road, especially when the machine is used with an internal combustion engine. The proposed method uses Chi-square test and is adaptive in a sense that it derives the compensation factors during the shaft operation and updates them in a timely fashion

An In-Depth Analysis of Sliding Mode Control and Its Application to Robotics

In this study, a sliding mode control scheme with a bounded region and its convergence analysis are explained to the finest detail and are applied to robotic manipulators which represent the best examples for strongly coupled, highly nonlinear, time-varying dynamical systems. Simulation studies have been applied separately to two different control systems in order to demonstrate the feasibility, performance, and effectiveness of the proposed control methodology through the design of the sliding mode controller: firstly, the position control of an armature-controlled dc servo motor subject to a varying external disturbance, and secondly, a two-link robot manipulator that were also analyzed in terms of its robustness by adding extra mass to one of the joints to be able to maintain the trajectory in the sliding surface. Simulations show that a fast convergence rate, and therefore quick response, the ability to reject the varying external disturbances, and the robustness against the model uncertainty assumed to be unbounded and fast-varying have all achieved its purpose entirely. This study also examines the advantages of SMC and PID comparably. The results given here do not contradict the view that one can use it instead of the other without losing too much performance, and confirm the success of the presented approach

A Heuristically Generated Metric Approach to the Solution of Chase Problem

In this work, heuristic, hyper-heuristic, and metaheuristic approaches are reviewed. Distance metrics are also examined to solve the “puzzle problems by searching” in AI. A viewpoint is brought by introducing the so-called Heuristically Generated Angular Metric Approach (HAMA) through the explanation of the metrics world. Distance metrics are applied to “cat and mouse” problem where cat and mouse makes smart moves relative to each other and therefore makes more appropriate decisions. The design is built around Fuzzy logic control to determine route finding between the pursuer and prey. As the puzzle size increases, the effect of HAMA can be distinguished more clearly in terms of computation time towards a solution. Hence, mouse will gain more time in perceiving the incoming danger, thus increasing the percentage of evading the danger. ‘Caught and escape percentages vs. number of cats’ for three distance metrics have been created and the results evaluated comparatively. Given three termination criteria, it is never inconsistent to define two different objective functions: either the cat travels the distance to catch the mouse, or the mouse increases the percentage of escape from the cat