Globally Convergent Adaptive Tracking of Angular Velocity and Inertia Identification for a 3-DOF Rigid Body

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/57820/1/AdaptiveTrackingTCST2006.pd

Adaptive Tracking of Angular Velocity for a Planar Rigid Body With Unknown Models for Inertia and Input Nonlinearity

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/57795/1/AdaptiveTrackingTACTTCST1D.pd

Adaptive Identification and Control for Underwater Vehicles: Theory and Comparative Experimental Evaluations

This Thesis reports several novel algorithms for state observation, parameter identification, and control of second-order plants. A stability proof for each novel result is included. The primary contributions are adaptive algorithms for underwater vehicle (UV) plant parameter identification and model-based control. Where possible, comparative experimental evaluations of the novel UV algorithms were conducted using the Johns Hopkins University Hydrodynamic Test Facility. The UV adaptive identification (AID) algorithms reported herein estimate the plant parameters (hydrodynamic mass, quadratic drag, gravitational force, and buoyancy parameters) of second-order rigid-body UV plants under the influence of actuator forces and torques. Previous adaptive parameter identification methods have focused on model-based adaptive tracking controllers; however, these approaches are not applicable when the plant is either uncontrolled, under open-loop control, or using any control law other than a specific adaptive tracking controller. The UV AID algorithms reported herein do not require simultaneous reference trajectory-tracking control, nor do they require instrumentation of linear acceleration or angular acceleration. Thus, these results are applicable in the commonly occurring cases of uncontrolled vehicles, vehicles under open-loop control, vehicles using control methods prescribed to meet other application-specific considerations, and vehicles not instrumented to measure angular acceleration. In comparative experimental evaluations, adaptively identified plant models (AIDPMs) were shown to accurately model experimentally measured UV performance. The UV model-based control (MBC) and adaptive model-based control (AMBC) algorithms reported herein provide asymptotically exact trajectory-tracking for fully coupled second-order rigid-body UV plants. In addition, the AMBC algorithm estimates the plant parameters (hydrodynamic mass, quadratic drag, gravitational force, and buoyancy parameters) for this class of plants. A two-step AMBC algorithm is also reported which first identifies gravitational plant parameters to be used in a separate AMBC algorithm for trajectory-tracking. We report a comparative experimental analysis of proportional derivative control (PDC) and AMBC during simultaneous motion in all degrees-of-freedom. This analysis shows AMBC (i.e. simultaneous adaptation of all plant parameter estimates) can be unstable in the presence of unmodeled thruster dynamics; two-step AMBC is robust to the presence of unmodeled thruster dynamics; and two-step AMBC provides 30% better position tracking performance and 8% worse velocity tracking performance over PDC. To the best of our knowledge, the reported comparative experimental evaluation of AMBC and PDC is the first to consider trajectory-tracking performance during simultaneous motion in all degrees-of-freedom

Inertia-Free Spacecraft Attitude Tracking with Disturbance Rejection and Almost Global Stabilization

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76737/1/AIAA-41565-705.pd

Integrated Optimal and Robust Control of Spacecraft in Proximity Operations

With the rapid growth of space activities and advancement of aerospace science and technology, many autonomous space missions have been proliferating in recent decades. Control of spacecraft in proximity operations is of great importance to accomplish these missions. The research in this dissertation aims to provide a precise, efficient, optimal, and robust controller to ensure successful spacecraft proximity operations. This is a challenging control task since the problem involves highly nonlinear dynamics including translational motion, rotational motion, and flexible structure deformation and vibration. In addition, uncertainties in the system modeling parameters and disturbances make the precise control more difficult. Four control design approaches are integrated to solve this challenging problem. The first approach is to consider the spacecraft rigid body translational and rotational dynamics together with the flexible motion in one unified optimal control framework so that the overall system performance and constraints can be addressed in one optimization process. The second approach is to formulate the robust control objectives into the optimal control cost function and prove the equivalency between the robust stabilization problem and the transformed optimal control problem. The third approach is to employ the è-D technique, a novel optimal control method that is based on a perturbation solution to the Hamilton-Jacobi-Bellman equation, to solve the nonlinear optimal control problem obtained from the indirect robust control formulation. The resultant optimal control law can be obtained in closedorm, and thus facilitates the onboard implementation. The integration of these three approaches is called the integrated indirect robust control scheme. The fourth approach is to use the inverse optimal adaptive control method combined with the indirect robust control scheme to alleviate the conservativeness of the indirect robust control scheme by using online parameter estimation such that adaptive, robust, and optimal properties can all be achieved. To show the effectiveness of the proposed control approaches, six degree-offreedom spacecraft proximity operation simulation is conducted and demonstrates satisfying performance under various uncertainties and disturbances

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

Dynamic modeling, property investigation, and adaptive controller design of serial robotic manipulators modeled with structural compliance

Research results on general serial robotic manipulators modeled with structural compliances are presented. Two compliant manipulator modeling approaches, distributed and lumped parameter models, are used in this study. System dynamic equations for both compliant models are derived by using the first and second order influence coefficients. Also, the properties of compliant manipulator system dynamics are investigated. One of the properties, which is defined as inaccessibility of vibratory modes, is shown to display a distinct character associated with compliant manipulators. This property indicates the impact of robot geometry on the control of structural oscillations. Example studies are provided to illustrate the physical interpretation of inaccessibility of vibratory modes. Two types of controllers are designed for compliant manipulators modeled by either lumped or distributed parameter techniques. In order to maintain the generality of the results, neither linearization is introduced. Example simulations are given to demonstrate the controller performance. The second type controller is also built for general serial robot arms and is adaptive in nature which can estimate uncertain payload parameters on-line and simultaneously maintain trajectory tracking properties. The relation between manipulator motion tracking capability and convergence of parameter estimation properties is discussed through example case studies. The effect of control input update delays on adaptive controller performance is also studied