Design of a 6-DOF Robotic Gait Training System With Closed-Chain Foot Initiated Kinematics Control

Robotic assisted rehabilitation, taking advantage of neuroplasticity, has been shown to be helpful in regaining some degree of gait performance. Robot-applied movement along with voluntary efferent motor commands coordinated with the robot allows optimization of motion training. We present the design and characteristics of a novel foot-based 6-degree-of-freedom (DOF) robot-assisted gait training system where the limb trajectory mirrored the normal walking gait. The goal of this study was to compare robot-assisted gait to normal walking gait, where the limb moved independently without robotics. Motion analysis was used to record the three-dimensional kinematics of the right lower extremity. Walking motion data were determined and transferred to the robotic motion application software for inclusion in the robotic trials where the robot computer software was programmed to produce a gait pattern in the foot equivalent to the gait pattern recorded from the normal walking gait trial. Results demonstrated that ankle; knee and hip joint motions produced by the robot are consistent with the joint motions in walking gait. We believe that this control algorithm provides a rationale for use in future rehabilitation, targeting robot-assisted training in people with neuromuscular disabilities such as stroke.

Tracking Control of Non-Minimum Phase Systems Using Filtered Basis Functions: A NURBS-Based Approach

This paper proposes an approach for minimizing tracking errors in systems with non-minimum phase (NMP) zeros by using filtered basis functions. The output of the tracking controller is represented as a linear combination of basis functions having unknown coefficients. The basis functions are forward filtered using the dynamics of the NMP system and their coefficients selected to minimize the errors in tracking a given trajectory. The control designer is free to choose any suitable set of basis functions but, in this paper, a set of basis functions derived from the widely-used non uniform rational B-spline (NURBS) curve is employed. Analyses and illustrative examples are presented to demonstrate the effectiveness of the proposed approach in comparison to popular approximate model inversion methods like zero phase error tracking control.

Simultaneous Stabilization of Pitch and Yaw of a Gliding Robotic Fish Using Sliding Mode Control

Oceanic sustainability has been a growing global concern due to the increase of potential threats to the integrity of aquatic ecosystems. As a result, more attention has been paid to the monitoring of such environments, leading to the need for autonomous aquatic robots that are capable of monitoring them in an efficient and accurate manner. A gliding robotic fish is a type of underwater robot that stems from combining the energy-efficient underwater glider with the highly maneuverable robotic fish. For accurate trajectory control and precise sensor measurement, stabilization of both pitch and yaw during gliding is of great importance. In this paper we propose a multi-input-multi-output sliding mode controller for simultaneous stabilization of pitch and yaw. In this design, the outputs of both actuators, tail angle and center of gravity, are determined by the errors in both pitch and yaw. The effectiveness of the proposed approach is demonstrated via simulation with comparison to several alternative designs, including a pair of sliding mode controllers dealing with yaw and pitch separately, and a PI controller.

Velocity Control of a Cylindrical Rolling Robot by Surface-Morphing

A mathematical model is developed for a rolling robot with a cylindrically-shaped, elliptical outer surface that has the ability to alter its shape as it rolls, resulting in a torque imbalance that accelerates or decelerates the robot. A control scheme is implemented, whereby angular position and angular velocity are used as feedback to trigger and define morphing actuation. The goal of the control is to direct the robot to follow a given angular velocity profile. Equations of motion for the rolling robot are formulated and solved numerically. Results show that by automatically morphing its shape in a periodic fashion, the rolling robot is able to start from rest, achieve constant average velocity and slow itself in order to follow a desired velocity profile with significant accuracy.

Model Structure Adaptation: A Gradient-Based Approach

A gradient-based method of symbolic adaptation is introduced for a class of continuous dynamic models. The proposed Model Structure Adaptation Method (MSAM) starts with the first-principles model of the system and adapts its structure after adjusting its individual components in symbolic form. A key contribution of this work is its introduction of the model’s parameter sensitivity as the measure of symbolic changes to the model. This measure, which is essential to defining the structural sensitivity of the model, not only accommodates algebraic evaluation of candidate models in lieu of more computationally expensive simulation-based evaluation, but also makes possible the implementation of gradient-based optimization in symbolic adaptation. The applicability of the proposed method is evaluated in application to several models which demonstrate its potential utility.

Extended Kalman Filter Based Quadrotor State Estimation Based on Asynchronous Multisensor Data

For effective navigation and tracking applications involving Unmanned Aerial Vehicles (UAVs), data fusion from multiple sensors is utilized. However, asynchronous nature of the sensors, coupled with loss of data and communication delays, makes this process not very reliable. For a better estimation of the data, some sort of filtering scheme is needed. This paper presents an Extended Kalman Filter (EKF) based quadrotor state estimation by exploiting the dynamic model of the UAV. The data coming from the sensors is noisy and intermittent. The EKF filters and provides estimated data for the missing timestamps. An indoor flight test establishes the accuracy of the EKF, and another outdoor flight test validates the developed scheme for the real world scenario.

Performance Evaluation of an Extended Kalman Filter for State Estimation of a Pseudo-2D Thermal-Electrochemical Lithium-Ion Battery Model

Fast and accurate state estimation is one of the major challenges for designing an advanced battery management system based on high-fidelity physics-based model. This paper evaluates the performance of a modified extended Kalman filter (EKF) for on-line state estimation of a pseudo-2D thermal-electrochemical model of a lithium-ion battery under a highly dynamic load with 16C peak current. The EKF estimation on the full model is shown to be significantly more accurate (< 1% error on SOC) than that on the single-particle model (10% error on SOC). The efficiency of the EKF can be improved by reducing the order of the discretised model while maintaining a high level of accuracy. It is also shown that low noise level in the voltage measurement is critical for accurate state estimation.

Numeric and Analytic Investigations of Mean-Square Exponential Stability for Stochastically Timed Systems

This paper is motivated by the problems posed in feedback control design when actuators, sensors, and/or computational nodes connect via unreliable or unpredictable communications channels. In these cases, there is a degree of stochastic uncertainty to the timing of the system’s discretizing elements, such as digital-to-analog converters. Several theorems related to the stability of non-uniformly sampled discrete dynamical systems have recently been proposed; here we examine through numeric investigation the characteristics of systems which are mean square exponentially stable (MSES). In particular we present a method to compute the range of mean and variance that a nonuniformly discretized feedback control system may tolerate while remaining MSES. Several examples are presented.

Hierarchical Control of Multi-Domain Power Flow in Mobile Systems: Part II — Aircraft Application

This two-part paper presents the development of a hierarchical control framework for the control of power flow throughout large-scale systems. Part II presents the application of the graph-based modeling framework and three-level hierarchical control framework to the power systems of an aircraft. The simplified aircraft system includes an engine, electrical, and thermal systems. A graph based approach is used to model the system dynamics, where vertices represent capacitive elements such as fuel tanks, heat exchangers, and batteries with states corresponding to the temperature and state of charge. Edges represent power flows in the form of electricity and heat, which can be actuated using control inputs. The aircraft graph is then partitioned spatially into systems and subsystems, and temporally into fast, medium, and slow dynamics. These partitioned graphs are used to develop models for each of the three levels of the hierarchy. Simulation results show the benefits of hierarchical control compared to a centralized control method.

Diesel Engine Fuel Injection Control Using a Model-Guided Extremum-Seeking Method

Diesel engine fuel injection control is presented as a feedback based online optimization problem. Extremum seeking (ES) approach is used to address the online optimization formulation. The cost function is synthesized from extensive experimental investigations such that the indicated thermal efficiency of the engine is maximized while minimizing the NOx emissions under external boundary conditions. Knowledge of the physical combustion and emission formation process based on a pre-calibrated non-linear engine model output is used to determine the ES initial control input to minimize the seeking time. The control is demonstrated on a hardware-in-the-loop engine simulator bench.