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.

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.

A Control-Oriented Jet Ignition Combustion Model for an SI Engine

A turbulent jet ignition system of a spark ignited (SI) engine consists of pre-combustion and main-combustion chambers, where the combustion in the main-combustion chamber is initiated by turbulent jets of reacting products from the pre-combustion chamber. If the gas exchange and combustion processes are accurately controlled, the highly distributed ignition will enable very fast combustion and improve combustion stability under lean operations, which leads to high thermal efficiency, knock limit extension, and near zero NOx emissions. For model-based control, a precise combustion model is a necessity. This paper presents a control-oriented jet ignition combustion model, which is developed based on simplified fluid dynamics and thermodynamics, and implemented into a dSPACE based real-time hardware-in-the-loop (HIL) simulation environment. The two-zone combustion model is developed to simulate the combustion process in two combustion chambers. Correspondingly, the gas flowing through the orifices between two combustion chambers is divided into burned and unburned gases during the combustion process. The pressure traces measured from a rapid compression machine (RCM), equipped with a jet igniter, are used for initial model validation. The HIL simulation results show a good agreement with the experimental data.

Two-Stage Design of Linear Feedback Controllers for a Proton Exchange Membrane Fuel Cell

The paper considers the eighth-order proton exchange membrane (PEM) fuel-cell mathematical model and shows that it has a multi-time scale property, indicating that the dynamics of three model state space variables operate in the slow time scale and the dynamics of five state variables operate in the fast time scale. This multi-scale nature allows independent controllers to be designed in slow and fast time scales using only corresponding reduced-order slow (of dimension three) and fast (of dimension five) sub-models. The presented design facilitates the design of hybrid controllers, for example, the linear-quadratic optimal controller for the slow subsystem and the eigenvalue assignment controller for the fast subsystem. The design efficiency and its high accuracy are demonstrated via simulation on the considered PEM fuel cell model.

Meal Detection and Meal Size Estimation for Type 1 Diabetes Treatment: A Variable State Dimension Approach

To compensate the glucose variability caused by meals is essential in developing Artificial Pancreas for type 1 diabetes. Most existing algorithms rely on meal announcements and determine the insulin doses based on an Insulin-to-Carbohydrate ratio (I:C ratio). However, patients, especially young patients, often forget to provide meal information under natural living conditions. A Variable State Dimension (VSD) based algorithm is developed to detect meals which are unknown to the controller (unannounced meals). The algorithm is evaluated using an FDA-approved UVa/Padova simulator and has demonstrated to achieve 95% success rate in meal detection with less than 17% false alarm rate. In addition, the average meal size estimation error is no more than 13%. We then integrate the VSD-based meal detection and estimation algorithm with our previous published glucose dynamics model consisting of both insulin and carbohydrate inputs. The goodness of fit for 30min-ahead glucose predictions using meal information provided by the VSD-based algorithm has increased by 86% in average compared to the prediction using a model without meal input based on plasma blood glucose (BG) data. Simulation results also show that compared to several meal detection/estimation algorithms in the literature, the VSD-based algorithm has comparable or shorter detection time.