Control and dynamics of a flexible spacecraft during stationkeeping maneuvers

A case study of a spacecraft having flexible solar arrays is presented. A stationkeeping attitude control mode using both earth and rate gyro reference signals and a flexible vehicle dynamics modeling and implementation is discussed. The control system is designed to achieve both pointing accuracy and structural mode stability during stationkeeping maneuvers. Reduction of structural mode interactions over the entire mode duration is presented. The control mode using a discrete time observer structure is described to show the convergence of the spacecraft attitude transients during Delta-V thrusting maneuvers without preloading thrusting bias to the onboard control processor. The simulation performance using the three axis, body stabilized nonlinear dynamics is provided. The details of a five body dynamics model are discussed. The spacecraft is modeled as a central rigid body having cantilevered flexible antennas, a pair of flexible articulated solar arrays, and to gimballed momentum wheels. The vehicle is free to undergo unrestricted rotations and translations relative to inertial space. A direct implementation of the equations of motion is compared to an indirect implementation that uses a symbolic manipulation software to generate rigid body equations

On adaptive control and particle filtering in the automatic administration of medicinal drugs

Automatic feedback methodologies for the administration of medicinal drugs offer undisputed potential benefits in terms of cost reduction and improved clinical outcomes. However, despite several decades of research, the ultimate safety of many--it would be fair to say most--closed-loop drug delivery approaches remains under question and manual methods based on clinicians' expertise are still dominant in clinical practice. Key challenges to the design of control systems for these applications include uncertainty in pharmacological models, as well as intra- and interpatient variability in the response to drug administration. Pharmacological systems may feature nonlinearities, time delays, time-varying parameters and non-Gaussian stochastic processes. This dissertation investigates a novel multi-controller adaptive control strategy capable of delivering safe control for closed-loop drug delivery applications without impairing clinicians' ability to make an expert assessment of a clinical situation. Our new feedback control approach, which we have named Robust Adaptive Control with Particle Filtering (RAC-PF), estimates a patient's individual response characteristic in real-time through particle filtering and uses the Bayesian inference result to select the most suitable controller for closed-loop operation from a bank of candidate controllers designed using the robust methodology of mu-synthesis. The work is presented as four distinct pieces of research. We first apply the existing approach of Robust Multiple-Model Adaptive Control (RMMAC), which features robust controllers and Kalman filter estimators, to the case-study of administration of the vasodepressor drug sodium nitroprusside and examine benefits and drawbacks. We then consider particle filtering as an alternative to Kalman filter-based methods for the real-time estimation of pharmacological dose-response, and apply this to the nonlinear pharmacokinetic-pharmacodynamic model of the anaesthetic drug propofol. We ultimately combine particle filters and robust controllers to create RAC-PF, and test our novel approach first in a proof-of-concept design and finally in the case of sodium nitroprusside. The results presented in the dissertation are based on computational studies, including extensive Monte-Carlo simulation campaigns. Our findings of improved parameter estimates from noisy observations support the use of particle filtering as a viable tool for real-time Bayesian inference in pharmacological system identification. The potential of the RAC-PF approach as an extension of RMMAC for closed-loop control of a broader class of systems is also clearly highlighted, with the proposed new approach delivering safe control of acute hypertension through sodium nitroprusside infusion when applied to a very general population response model. All approaches presented are generalisable and may be readily adapted to other drug delivery instances

Fault tolerant control for nonlinear aircraft based on feedback linearization

The thesis concerns the fault tolerant flight control (FTFC) problem for nonlinear aircraft by making use of analytical redundancy. Considering initially fault-free flight, the feedback linearization theory plays an important role to provide a baseline control approach for de-coupling and stabilizing a non-linear statically unstable aircraft system. Then several reconfigurable control strategies are studied to provide further robust control performance:- A neural network (NN)-based adaption mechanism is used to develop reconfigurable FTFC performance through the combination of a concurrent updated learninglaw. - The combined feedback linearization and NN adaptor FTFC system is further improved through the use of a sliding mode control (SMC) strategy to enhance the convergence of the NN learning adaptor. - An approach to simultaneous estimation of both state and fault signals is incorporated within an active FTFC system.The faults acting independently on the three primary actuators of the nonlinear aircraft are compensated in the control system.The theoretical ideas developed in the thesis have been applied to the nonlinear Machan Unmanned Aerial Vehicle (UAV) system. The simulation results obtained from a tracking control system demonstrate the improved fault tolerant performance for all the presented control schemes, validated under various faults and disturbance scenarios.A Boeing 747 nonlinear benchmark model, developed within the framework of the GARTEUR FM-AG 16 project “fault tolerant flight control systems”,is used for the purpose of further simulation study and testing of the FTFC scheme developed by making the combined use of concurrent learning NN and SMC theory. The simulation results under the given fault scenario show a promising reconfiguration performance

Stabilizing control design of a motorcycle

This thesis solves the stabilizing control of an autonomous motorcycle. The control of an autonomous motorcycle is a challenging and interesting problem in the field because the plant is under-actuated, unstable and nonlinear. Two major problems that have not been considered in the literature are explicitly solved in our work: (i) the robust control problem of the plant subject to uncertainty and exogenous disturbance; (ii) the non-local stabilization of the nonlinear plant. To achieve the first goal, we propose a robust H_infty controller based on the linearized system, which provides a significant improvement in dealing model uncertainty and disturbance attenuation in comparison with those controllers given by classical linear design tools. To achieve the second goal, we propose a nonlinear controller based on the combination of a nonlinear forwarding method with several other methods for the nonlinear plant through identifying an appropriate upper triangular structure of the nonlinear system. This yields a stability region, the whole upper space above the level ground, such that the trajectory starting from any position in the upper hemi-sphere with arbitrary initial velocities converges to the upright position. Both results are novel and first results of their kinds in control of an autonomous motorcycle. Computer simulations verify the effectiveness of the proposed controllers

Load frequency controllers considering renewable energy integration in power system

Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency