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A multivariable control technique is proposed for a type of nonlinear system with parameter intervals. The control is based upon the feedback linearization scheme called Generic Model Control, and alters the control calculation by utilizing parameter intervals, employing an adaptive step, averaging control predictions, and applying an interval problem solution. The proposed approach is applied in controlling both a linear and a nonlinear arc welding system as well in other simulations of scalar and multivariable systems.

A two-level optimal coordination control approach for energy storage and conventional generation consisting of advanced frequency control and stochastic optimal dispatch is proposed to deal with the real power balancing control problem introduced by variable renewable energy sources (RESs) in power systems. In the proposed approach, the power and energy constraints on energy storage are taken into account in addition to the traditional power system operational constraints such as generator output limits and power network constraints. The advanced frequency control level which is based on the robust control theory and the decentralized static output feedback design is responsibl e for the system frequency stabilization and restoration, whereas the stochastic optimal dispatch level which is based on the concept of stochastic model predictive control (SMPC) determines the optimal dispatch of generation resources and energy storage under uncertainties introduced by RESs as well as demand. In the advanced frequency control level, low-order decentralized robust frequency controllers for energy storage and conventional generation are simultaneously designed based on a state-space structure-preserving model of the power system and the optimal controller gains are solved via an improved linear matrix inequality algorithm. In the stochastic optimal dispatch level, various optimization decomposition techniques including both primal and dual decompositions together with two different decomposition schemes (i.e. scenario-based decomposition and temporal-based decomposition) are extensively investigated in terms of convergence speed due to the resulting large-scale and computationally demanding SMPC optimization problem. A two-stage mixed decomposition method is conceived to achieve the maximum speedup of the SMPC optimization solution process. The underlying control design philosophy across the entire work is the so-called time-scale matching principle, i.e. the conventional generators are mainly responsible to balance the low frequency components of the power variations whereas the energy storage devices because of their fast response capability are employed to alleviate the relatively high frequency components. The performance of the proposed approach is tested and evaluated by numerical simulations on both the WECC 9-bus system and the IEEE New England 39-bus system.

In this thesis, we study fault tolerant control (FTC) for the decentralized robust servomechanism problem (DRSP) of a colocated large flexible space structure (LFSS) under sensor and actuator failures (SAF). The control objective is to devise a decentralized controller that maintains the stability of the LFSS, tracks a constant reference for healthy outputs, regulates against an unknown constant disturbance for healthy outputs, and is robust against parametric uncertainties, so that ``spillover effects'' do not occur. Two FTC frameworks are considered: An active FTC framework that assumes SAF are known, and a passive FTC framework for which SAF are unknown. The active FTC framework extends existing work on DRSP of a nominal LFSS, and applies a PID controller that has fault-dependent adjustments. Necessary and sufficient conditions for a solution to exist are determined, notably an easy-to-test rank condition. For the passive FTC framework, a PD controller that stabilizes an LFSS under unknown SAF is found. Although perfect tracking and regulation are not attained under the PD controller, by applying high gains, the errors for healthy outputs can be reduced to any desired level. However, outputs with failed sensors and healthy actuators can reach undesirably high magnitude under high gains. To improve performance under low gains, insights on steady-state outputs are applied to develop a feed-forward control that has good performance in tracking, but not regulation. Further analysis on the PD controller reveals a method to diagnose SAF using steady-state outputs. As a result, the PD controller and PID controller are found to have complementary advantages, leading to an 3-stage integrated FTC procedure. First, the PD controller can stabilize the LFSS under unknown SAF (passive FTC). Next, fault diagnosis is performed while the LFSS is stabilized. Finally, a reconfigured PID controller applying diagnosed SAF enables healthy outputs to meet control objectives (active FTC). Three examples, including a benchmark space platform with 200 states obtained by finite-element analysis, are used to illustrate the results throughout this thesis.

In this thesis, we study fault tolerant control (FTC) for the decentralized robust servomechanism problem (DRSP) of a colocated large flexible space structure (LFSS) under sensor and actuator failures (SAF). The control objective is to devise a decentralized controller that maintains the stability of the LFSS, tracks a constant reference for healthy outputs, regulates against an unknown constant disturbance for healthy outputs, and is robust against parametric uncertainties, so that ``spillover effects'' do not occur. Two FTC frameworks are considered: An active FTC framework that assumes SAF are known, and a passive FTC framework for which SAF are unknown. The active FTC framework extends existing work on DRSP of a nominal LFSS, and applies a PID controller that has fault-dependent adjustments. Necessary and sufficient conditions for a solution to exist are determined, notably an easy-to-test rank condition. For the passive FTC framework, a PD controller that stabilizes an LFSS under unknown SAF is found. Although perfect tracking and regulation are not attained under the PD controller, by applying high gains, the errors for healthy outputs can be reduced to any desired level. However, outputs with failed sensors and healthy actuators can reach undesirably high magnitude under high gains. To improve performance under low gains, insights on steady-state outputs are applied to develop a feed-forward control that has good performance in tracking, but not regulation. Further analysis on the PD controller reveals a method to diagnose SAF using steady-state outputs. As a result, the PD controller and PID controller are found to have complementary advantages, leading to an 3-stage integrated FTC procedure. First, the PD controller can stabilize the LFSS under unknown SAF (passive FTC). Next, fault diagnosis is performed while the LFSS is stabilized. Finally, a reconfigured PID controller applying diagnosed SAF enables healthy outputs to meet control objectives (active FTC). Three examples, including a benchmark space platform with 200 states obtained by finite-element analysis, are used to illustrate the results throughout this thesis.

Recent advances in sensor and microcomputer technology and in control and aeroydynamics theories has made small unmanned aerial vehicles a reality. The small size, low cost and manoueverbility of these systems has positioned them to be potential solutions in a large class of applications. However, the small size of these vehicles pose significant challenges. The small sensors used on these systems are much noisier than their larger counterparts.The compact structure of these vehicles also makes them more vulnerable to environmental effects. This work develops several different control strategies for two sUAV platforms and provides the rationale for judging each of the controllers based on a derivation of the dynamics, simulation studies and experimental results where possible. First, the coaxial helicopter platform is considered. This sUAV’s dual rotor system (along with its stabilizer bar technology) provides the ideal platform for safe, stable flight in a compact form factor. However, the inherent stability of the vehicle is achieved at the cost of weaker control authority and therefore an inability to achieve aggressive trajectories especially when faced with heavy wind disturbances. Three different linear control strategies are derived for this platform. PID, LQR and H∞ methods are tested in simulation studies. While the PID method is simple and intuitive, the LQR method is better at handling the decoupling required in the system. However the frequency domain design of the H∞ control method is better at suppressing disturbances and tracking more aggressive trajectories. The dynamics of the quadrotor are much faster than those of the coaxial helicopter. In the quadrotor, four independent fixed pitch rotors provide the required thrust. Differences between each of the rotors creates moments in the roll, pitch and yaw directions. This system greatly simplifies the mechanical complexity of the UAV, making quadrotors cheaper to maintain and more accessible. The quadrotor dynamics are derived in this work. Due to the lack of any mechanical stabilization system, these quadrotor dynamics are not inherently damped around hover. As such, the focus of the controller development is on using nonlinear techniques. Linear quadratic regulation methods are derived and shown to be inadequate when used in zones moderately outside hover. Within nonlinear methods, feedback linearization techniques are developed for the quadrotor using an inner/outer loop decoupling structure that avoids more complex variants of the feedback linearization methodology. Most nonlinear control methods (including feedback linearization) assume perfect knowledge of vehicle parameters. In this regard, simulation studies show that when this assumption is violated the results of the flight significantly deteriorate for quadrotors flying using the feedback linearization method. With this in mind, an adaptation law is devised around the nonlinear control method that actively modifies the plant parameters in an effort to drive tracking errors to zero. In simple cases with sufficiently rich trajectory requirements the parameters are able to adapt to the correct values (as verified by simulation studies). It can also adapt to changing parameters in flight to ensure that vehicle stability and controller performance is not compromised. However, the direct adaptive control method devised in this work has the added benefit of being able to modify plant parameters to suppress the effects of external disturbances as well. This is clearly shown when wind disturbances are applied to the quadrotor simulations. Finally, the nonlinear quadrotor controllers devised above are tested on a custom built quadrotor and autopilot platform. While the custom quadrotor is able to fly using the standard control methods, the specific controllers devised here are tested on a test bench that constrains the movement of the vehicle. The results of the tests show that the controller is able to sufficiently change the necessary parameter to ensure effective tracking in the presence of unmodelled disturbances and measurement error.

The study presented in this thesis comprises the development of the longitudinal autopilot algorithm for a ramjet powered air-to-surface missile. Ramjet Missiles have short time-of-flight, however they suffer from limited angle of attack margins due to poor operational-region characteristics of the ramjet engine. Because of such limitations and presence of uncertainties involved, Robust Control Techniques are used for the controller design. Robust Control Techniques not only provide an easy limitation/uncertainty/performance handling for MIMO systems, but also, robust controllers promise stability and performance even in the presence of uncertainties of a pre-defined class. All the design process is carried out in such a way that at the end of the study a tool has been developed, that can process raw aerodynamic data obtained by Missile DATCOM program, linearize the equations of motion, construct the system structure and design sub-optimal H&amp / #8734 / controllers to meet the requirements provided by the user. An autopilot which is designed by classical control techniques is used for performance and robustness comparison, and a non-linear simulation is used for validation. It is concluded that the code, which is very easy to modify for the specifications of other missile systems, can be used as a reliable tool in the preliminary design phases where there exists uncertainties/limitations and still can provide satisfactory results while making the design process much faster.

This thesis presents the design, development, and testing of a multi-agent systems laboratory that will enable the experimental investigation of Networked Control Systems. Networked Control Systems (NCS) are integrations of computation, networking, and physical dynamics, in which embedded devices are networked to sense, monitor, execute collaborative tasks, and interact with the physical world. As the potential for applications of NCS has increased, so has the research interest in this area. Possible applications include search and rescue, scientific data collection, and health care monitoring systems. One of the primary challenges in applying NCS is designing distributed algorithms that will enable the networked devices to achieve global objectives. Another challenge is in ensuring that distributed algorithms have the necessary robustness to achieve those global objectives in dynamic and unpredictable environments. A multi-agent systems laboratory provides the researcher with a means to observe the behavior and performance of distributed algorithms as they are executed on a set of networked devices. Through this observation, the researcher may discover robustness issues that were not present in computer simulation. The objective of this research is to design and implement the infrastructure for a multi-agent systems laboratory to observe distributed algorithms implemented on networked devices.

The main objective of this thesis is to apply ideas and techniques from modern control theory, especially from robust state estimation and model validation, to various important problems in computer vision. Robust model validation is used in texture recognition where new approaches for classifying texture samples and segmenting textured images are developed. Also, a new model validation approach to motion primitive recognition is demonstrated by considering the motion segmentation problem for a mobile wheeled robot. A new approach to image inpainting based on robust state estimation is proposed where the implementation presented here concerns with recovering corrupted frames in video sequences. Another application addressed in this thesis based on robust state estimation is video-based tracking. A new tracking system is proposed to follow connected regions in video frames representing the objects in consideration. The system accommodates tracking multiple objects and is designed to be robust towards occlusions. To demonstrate the performance of the proposed solutions, examples are provided where the developed methods are applied to various gray-scale images, colored images, gray-scale videos and colored videos. In addition, a new algorithm is introduced for motion estimation via inverse polynomial interpolation. Motion estimation plays a primary role within the video-based tracking system proposed in this thesis. The proposed motion estimation algorithm is also applied to medical image sequences. Motion estimation results presented in this thesis include pairs of images from a echocardiography video and a robot-assisted surgery video.

Quantitative Feedback Theory (QFT) is a control system design methodology founded on the premise that feedback is necessary only because of system uncertainty. Articulating flexible structures, such as flexible manipulators, present a difficult closed-loop control problem. In such servo systems, the coupling of the rigid and flexible modes and the non-minimum phase dynamics severely limit system stability and performance. The difficulties in controlling these structures is exacerbated by the denumerably infinite number of flexible modes and associated difficulties in developing accurate dynamic models for controller design. As such, the control of articulating flexible structures presents a non-trivial testbed for the design of QFT based robust control systems. This dissertation examines the multi-input multi-output (MIMO) QFT based control of an articulating flexible structure and presents an enhancement of the theoretical basis for the MIMO QFT design methodologies. The control problem under consideration is the active vibration control of an articulating single-link flexible manipulator. This is facilitated by an actuation scheme comprised of a combination of spatially discrete actuation, in the form of a DC motor to perform articulation, and spatially distributed actuation, in the form of a piezoelectric transducer for active vibration control. In the process of developing and experimentally validating the QFT based control system, shortcomings in the theoretical basis for the MIMO QFT design methodologies are addressed. Robust stability theorems are developed for the two main MIMO QFT design methodologies, namely the sequential and non-sequential MIMO QFT design methodologies. The theorems complement and extend the existing theoretical basis for the MIMO QFT design methodologies. The dissertation results expose salient features of the MIMO QFT design methodologies and provide connections to other multivariable design methodologies.