Nonlinear Feedback Control of Underactuated Mechanical Systems

This chapter presents control of a class of mechanical underactuated system using feedback linearization technique. The MIMO mechanical system is modeled by a set of nonlinear differential equations in which mathematical model is divided into two subsystems: one for actuated outputs and the other for unactuated outputs. The nonlinear feedback of states is used to “linearize” the closed-loop system. In other word, the control structure is constructed by linearly combining two components that are separately obtained from the nonlinear feedback of actuated and unactuated states. Lyapunov technique will be applied to investigate the system stability. As illustration example, nonlinear feedback control of a three-dimensional (3D) overhead crane is presented to investigate the proposed theory

Lane Departure Detecting with Classification of Roadway Based on Bezier Curve Fitting Using DGPS/GIS

Lane departure warning system plays an important role in safety driving by detecting a departure from a lane that is inadvertently operated on the driving trajectory. This paper suggestion detection algorithm when a vehicle departs lane boundary using GIS based on DGPS in the whole roadways. Lane segments obtained from the GIS are calculated their relative distances based on the vehicle position. Lane segments consist of consecutive straight lines and have a steady numerical error of design. In the curved section, the numerical error is bigger due to the characteristics. Accurate information about lane segments is required to reduce errors. Bezier curves are one way to extract lane segments from a curved section. The proposed lane departure algorithm is processed in two ways according to the lane type. Firstly, roads should be classified as lane type with straight and curved sections. Intersection points (IP) algorithm can easily classify the curve segments. Classified lane segments handle arithmetic relative distances for each algorithm. The lane segment of the base boundary, which is a straight lane section, has a virtual line based on the requirements of ISO 17361. The overlap area, consisting of a curve lane section and a Bezier curve, calculates the departure distance through the continuity of the driving characteristic and determines the lane departure from the curved roadways. To verify the proposed algorithm, the lane departure test led to two lane departures on each roadway. The comparison between visual sight and the departure alarm shows the driver within 0.1 second

Hierarchical Sliding Mode Control for a 2D Ballbot That Is a Class of Second-Order Underactuated System

2D Ballbot is an actual under-actuated system with second-order nonholonomic velocity constraints and input coupling case where only control input is employed to control two outputs of the system. Controlling such a system is not easy because it faces many changelings including nonlinearities, external disturbances, and uncertainties. This study proposed a robust control system for a Ballbot mobile robot. The proposed control scheme is constructed using the hierarchical sliding mode control technique. The kinematics and dynamics of the Ballbot are derived. A Lyapunov function is used to prove the stability of the closed-loop control system. The stabilizing and transferring problems are investigated through several simulations and experiments by using the actual Ballbot platform

Theoretical and experimental biomechanical analyses of the effects of age and peripheral neuropathy on unipedal balance.

The objective of this thesis was to test the hypothesis that the marked age-related decline in the ability of humans to stand on one leg was caused by age changes in biomechanical parameters. A servo-controlled platform was used to demonstrate that the threshold for detecting inversion/eversion ankle rotations at a 75% confidence level (TH\sb{75}) increased significantly with age from 0.1\sp\circ in healthy young adults (YA: 22 years) to 0.3\sp\circ in healthy elderly (OA: 72 years), and to 1.4\sp\circ in patients with peripheral neuropathy. Significant (50%) reductions in voluntary frontal plane (FP) ankle strengths and rates of developing moment (t\sb{r}) were found with age. Because of low foot-ground interface compliance, no significant changes in active or passive ankle FP rotational stiffness (ranging from 25 to 57 Nm/rad) were found with age. Three joint, FP, multi-link, direct and indirect dynamic biomechanical models were used to study the three movement phases and that constitute the transfer from bi- to unipedal stance. The 30% rate of the OA premature loss of balance following the Phase II transition can be ascribed to a failure to control the contralateral foot ground reaction impulse ("push-off") within 6 and 19% body weight. A linear auto-regressive model with external input (ARX) was used to estimate the closed loop control law used for regulating Phase III unipedal balance. The model was used to investigate tandem force place findings in 38 YA and 32 OA showing a 60% decrease on maximum time of unipedal balance and a 20% increase in the already significant FP ankle moments required for Phase III unipedal with age. Model sensitivity results showed these latter effects can be explained by the measured age-related increase in t\sb{r},\ TH\sb{75}, system dead time and decreased FP strengths, alone, or in concert with one another; they may lead to shortened OA unipedal balance times by advancing the onset of ankle muscle fatigue. Because system oscillations were found to be sensitive to position and velocity feedback gains, the OA difficult in balancing unipedally with eyes closed seems to be due to inadequate readjustment of these gains.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/103804/1/9409750.pdfDescription of 9409750.pdf : Restricted to UM users only

Intelligent robotic walker with actively controlled human interaction

In this study, we developed a robotic walker that actively controls its speed and direction of movement according to the user's gait intention. Sensor fusion between a low‐cost light detection and ranging (LiDAR) sensor and inertia measurement units (IMUs) helps determine the user's gait intention. The LiDAR determines the walking direction by detecting both knees, and the IMUs attached on each foot obtain the angular rate of the gait. The user's gait intention is given as the directional angle and the speed of movement. The two motors in the robotic walker are controlled with these two variables, which represent the user's gait intention. The estimated direction angle is verified by comparison with a Kinect sensor that detects the centroid trajectory of both the user's feet. We validated the robotic walker with an experiment by controlling it using the estimated gait intention

Walking Intent-Based Movement Control for JAIST Active Robotic Walker

This paper presents a novel interactive control for our assistive robotic walker, the JAIST Active Robotic Walker (JARoW), developed for elderly people in need of assistance. The aim of our research is to recognize characteristics of the user’s gait and to generate the movement of JARoW accordingly. Specifically, the proposed control enables JARoW to accurately generate the direction and velocity of its movement in a way that corresponds to the user’s variable walking behaviors. The algorithm and implementation of the control are explained in detail, and the effectiveness and usability of JARoW are verified through extensive experiments in everyday environments

Long-term knowledge acquisition using contextual information in a memory-inspired robot architecture

In this paper, we present a novel cognitive framework allowing a robot to form memories of relevant traits of its perceptions and to recall them when necessary. The framework is based on two main principles: on the one hand, we propose an architecture inspired by current knowledge inhuman memory organisation; on the other hand, we integrate such an architecture with the notion of context, which is used to modulate the knowledge acquisition process when consolidatingmemories and forming new ones, as well as with the notion of familiarity, which is employed to retrieve propermemories given relevant cues. Although much research has been carried out, which exploits Machine Learning approaches to provide robots with internal models of their environment (including objects and occurring events therein), we argue that such approaches may not be theright direction to follow if a long-term, continuous knowledge acquisition is to be achieved. As a case study scenario, we focus on both robot-environment and human-robot interaction processes. In case of robot？environment interaction, a robot performs pick and place movements using the objects in theworkspace, at the same time observing their displacement on a table in front of it, and progressively forms memories defined as relevant cues (e.g. colour, shape or relative position) in a context-aware fashion. As far as human-robot interaction is concerned, the robot can recall specific snapshots representing past events using both sensory informationand contextual cues upon request by humans