The objective of adaptive control is to design a controller that can adjust its behaviour to tolerate uncertain or time-varying parameters. An adaptive controller typically consists of a linear time-invariant (LTI) compensator together with a tuning mechanism which adjusts the compensator parameters and yields a nonlinear controller. Because of the nonlinearity, the transient closed-loop behaviour is often poor and the control signal may become unduly large. Although the initial objective of adaptive control was to deal with time-varying plant parameters, most classical adaptive controllers cannot handle rapidly changing parameters. Recently, the use of a linear periodic (LP) controller has been proposed as a new approach in the field of model reference adaptive control [1]. In this new approach, instead of estimating plant parameters, the “ideal control signal” (what the control signal would be if the plant parameters and states were measurable) is estimated. The resulting controller has a number of desirable features: (1) it handles rapid changes in the plant parameters, (2) it provides nice transient behaviour of the closed-loop system, (3) it guarantees that the effect of the initial conditions declines to zero exponentially, and (4) it generates control signals which are modest in size. Although the linear periodic controller (LPC) has the above advantages, it has some imperfections. In order to achieve the desirable features, a rapidly varying control signal and a small sampling period are used. The rapidly time-varying control signal requires fast actuators which may not be practical. The second weakness of the LPC [1] is poor noise rejection behaviour. The small sampling period results in large controller gains and correspondingly poor noise sensitivity, since there is a clear trade-off between tracking and noise tolerance. As the last drawback, this controller requires knowledge of the exact plant relative degree. Here we extend this work in several directions: (i) In [1], the infinity-norm is used to measure the signal size. Here we redesign the controller to yield a new version which provides comparable results when the more common 2-norm is used to measure signal size, (ii) A key drawback of the controller of [1] is that the control signal moves rapidly. Here we redesign the control law to significantly alleviate this problem, (iii) The redesigned controller can handle large parameter variation and in the case that the sign of high frequency gain is known, the closed-loop system is remarkably noise-tolerant, (iv) We prove that in an important special case, we can replace the requirement of knowledge of the exact relative degree with that of an upper bound on the relative degree, at least from the point of view of providing stability, and (v) A number of approaches to improve the noise behaviour of the controller are presented. Reference: [1] D. E. Miller, “A New Approach to Model Reference Adaptive Control”, IEEE Transaction on Automatic Control, Vol. 48, No. 5, pages 743-756, May 2003.

Precise machining of bearing rings is integral to finished bearing assembly quality. The output accuracy of center-based machining systems such as lathes or magnetic chuck grinders relates directly to the accuracy of part centering before machining. Traditional tooling and methods for centering on such machines are subject to wear, dimensional inaccuracy, setup time (hard tooling) and human error (manual centering).A flexible system for initial part centering is developed based on a single measurement system and actuator, whereby the part is placed by hand onto the machine table, rotated and measured to identify center of geometry offset from center of rotation, then moved by a series of controlled impacts or pushes to align the centers. The prototype centering system is developed as a demonstration platform for research in a number of mechanical engineering areas, particularly: Characterization of optimal state estimators through analysis of accuracy and computational efficiency; Distributed communication and control, efficient transfer of information in a real-time environment, and information sharing between processes; Modeling of sliding dynamics and the interaction of friction with compliant body dynamic models; Motion path planning through both deterministic geometric transforms and through frequency domain command manipulation.A vision is created for future work not only in the described areas, but also in the areas of advanced controller design incorporating multiple variables, derived machine diagnostic information, and application of the distributed communication architecture to information flow throughout the manufacturing organization. The guiding motivation for this research is reduction of manufacturing processing costs in the face of global competition. The technologies researched, developments made, and directions prescribed for future research aid in enabling this goal.

Combustion instabilities are a significant problem in combustion systems, particularly in Low NOx Gas Turbine combustors. These instabilities result in large-scale pressure oscillations in the combustor, leading to degraded combustor performance, shortened lifetime, and catastrophic combustor failure. The objective of this research was to develop a practical adaptive active control system that, coupled with an appropriate actuator, is capable of controlling the combustor pressure oscillations without a priori knowledge of the combustor design, operating conditions or instability characteristics. The adaptive controller utilizes an observer that determines the frequencies, phases and amplitudes of the dominant modes of the oscillations in real time. The research included development and testing of the adaptive controller on several combustors and on an unstable acoustic feedback system in order to analyze its performance. The research also included investigations of combustor controllability and combustor stability margin, which are critical issues for practical implementation of an active control system on an industrial combustor. The results of this research are directly applicable to a variety of combustors and can be implemented on full-scale industrial combustion systems.

This dissertation presents novel adaptive control laws in both state feedback and output feedback forms. In the setting of state feedback adaptive control K-modification provides a tunable stiffness term that results in a frequency dependent filtering effect, smoother transient responses, and time delay robustness in an adaptive system. K-modification is combined with the recently developed Kalman filter (KF) based adaptive control and derivative-free (DF) adaptive control. K-modification and its combinations with KF adaptive control and DF adaptive control preserve the advantages of each of these methods and can also be combined with other modification methods such as - and e-modification. An adaptive output feedback control law based on a state observer is also developed. The main idea behind this approach is to apply a parameter dependent Riccati equation to output feedback adaptive control. The adaptive output feedback approach assumes that a state observer is employed in the nominal controller design. The observer design is modified and employed in the adaptive part of the design in place of a reference model. This is combined with a novel adaptive weight update law. The weight update law ensures that estimated states follow both the reference model states and the true states so that both state estimation errors and state tracking errors are bounded. Although the formulation is in the setting of model following adaptive control, the realization of the adaptive controller uses the observer of the nominal controller in place of the reference model to generate an error signal. Thus the only components that are added by the adaptive controller are the realizations of the basis functions and the weight adaptation law. The realization is even less complex than that of implementing a model reference adaptive controller in the case of state feedback. The design procedure of output feedback adaptive control is illustrated with two examples: a simple wingrock dynamics model and a more complex aeroelastic aircraft transport model.

Fluid power technology has been widely used in industrial practice; however, its energy efficiency became a big concern in the recent years. Much progress has been made to improve fluid power energy efficiency from many aspects. Among these approaches, using a valve-less system to replace a traditional valve-controlled system showed eminent energy reduction. This thesis studies the valve-less solution-pump displacement controlled actuators- from the view of controls background. Singular perturbations have been applied to the fluid power to account for fluid stiffness; and a novel hydraulic circuit for single rod cylinder has been presented to increase the hydraulic circuit stabilities. Recursive Least Squares has been applied to account for measurement noise thus the parameters have fast convergence rate, square root algorithm has further applied to increase the controller's numerical stability and efficiency. It was showed that this technique is consistent with other techniques to increase controller's robustness. The developed algorithm is further extended to a hybrid adaptive control scheme to achieve desired trajectory tracking for general cases. A hardware test-bed using the invented hydraulic circuit was built up. The experimental results are presents and validated the proposed algorithms and the circuit itself. The end goal of this project is to develop control algorithms and hydraulic circuit suitable for industrial practice.

We present real-time autonomous implementations of a practical distributed formation control scheme for a multi-quadrotor system for two different cases: parameters of linearized dynamics are exactly known, and uncertain system parameters. For first case, we design a hierarchical, decentralized controller based on the leader-follower formation approach to control a multi-quadrotor swarm in rigid formation motion. The proposed control approach has a two-level structure: high-level and low-level. At the high level, a distributed control scheme is designed with respect to the relative and global position information of the quadrotor vehicles. In the low-level, we analyze each single quadrotor control design in three parts. The first is a linear quadratic controller for the pitch and roll dynamics of quadrotors. The second is proportional controller for the yaw motion. The third is proportional-integral-derivative controller in altitude model. For the second case, where inertial uncertainties in the pitch and roll dynamics of quadrotors are considered, we design an on-line parameter estimation with the least squares approach, keeping the yaw, altitude and the high-level controllers the same as the first case. An adaptive linear quadratic controller is then designed to be used with lookup table based on the estimation of uncertain parameters. Additionally, we study on enhancement of self and inter-agent relative localization of the quadrotor agents using a single-view distance-estimation based localization methodology as a practical and inexpensive tool to be used in indoor environments for future works. Throughout the formation control implementations, the controllers successfully satisfy the objective of formation maintenance for non-adaptive and adaptive cases. Simulations and experimental results are presented considering various scenarios, and positive results obtained for the effectiveness of our algorithm.

abstract: Microbial fuel cells(MFC) use micro-organisms called anode-respiring bacteria(ARB) to convert chemical energy into electrical energy. This process can not only treat wastewater but can also produce useful byproduct hydrogen peroxide(H2O2). Process variables like anode potential and pH play important role in the MFC operation and the focus of this dissertation are pH and potential control problems. Most of the adaptive pH control solutions use signal-based-norms as cost functions, but their strong dependency on excitation signal properties makes them sensitive to noise, disturbances, and modeling errors. System-based-norm( H-infinity) cost functions provide a viable alternative for the adaptation as they are less susceptible to the signal properties. Two variants of adaptive pH control algorithms that use approximate H-infinity frequency loop-shaping (FLS) cost metrics are proposed in this dissertation. A pH neutralization process with high retention time is studied using lab scale experiments and the experimental setup is used as a basis to develop a first-principles model. The analysis of such a model shows that only the gain of the process varies significantly with operating conditions and with buffering capacity. Consequently, the adaptation of the controller gain (single parameter) is sufficient to compensate for the variation in process gain and the focus of the proposed algorithms is the adaptation of the PI controller gain. Computer simulations and lab-scale experiments are used to study tracking, disturbance rejection and adaptation performance of these algorithms under different excitation conditions. Results show the proposed algorithm produces optimum that is less dependent on the excitation as compared to a commonly used L2 cost function based algorithm and tracks set-points reasonably well under practical conditions. The proposed direct pH control algorithm is integrated with the combined activated sludge anaerobic digestion model (CASADM) of an MFC and it is shown pH control improves its performance. Analytical grade potentiostats are commonly used in MFC potential control, but, their high cost (>$6000) and large size, make them nonviable for the field usage. This dissertation proposes an alternate low-cost($200) portable potentiostat solution. This potentiostat is tested using a ferricyanide reactor and results show it produces performance close to an analytical grade potentiostat. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2018

M.Ing. (Mechanical Engineering) / This dissertation describes the performance of an Adaptive controller and a Neural Network controller for water level control in a steam drum, and compares them to the performance of a conventional PIO controller in the same application. The control problem is in essence the regulation of the speed of a boiler feed pump in order to maintain a constant level in the drum of a small model of a boiler. As a starting point, the hydraulics and dynamics of the system are analyzed and the system is shown to be nonlinear. A nonlinear computer simulation is created and its response is compared with that of the real plant. The simulation proves to be a close representation of the real plant and it is used as an aid in creating a linear mathematical model. A set of control specifications are drawn up and a PIO controller is designed for the plant. With the aid of a root locus diagram it is shown that the plant cannot be controlled within specifications under PID control. This shortcoming is then demonstrated on both the linear mathematical model and the nonlinear plant. Consequently, advanced control techniques are investigated in an attempt to control the plant within specifications. Different methods of adaptive control are discussed and a direct model reference adaptive controller is designed. The least squares algorithm for parameter adjustment is discarded in favour of the slower gradient algorithm when it becomes apparent that the wave motion inside the drum has an adverse effect on the former algorithm. The control results obtained with both the linear model and the real plant proves adaptive control to be superior to PIO control in this application. Additionally, the application of neural networks in control systems is discussed. An adaptive neural network controller is designed but is discarded due to instability caused by imperfect modelling of the system...

In this dissertation, two main topics are explored, the vehicle routing problem (VRP) and model reference adaptive control (MRAC) for unknown nonlinear systems. The VRP and its extension, the split delivery VRP (SVRP), are analyzed to determine the effects of using two different objective functions, the total cost objective, and the last delivery objective. A worst-case analysis suggests that using the SVRP can improve total costs by as much as a factor of 2 and the last delivery by a factor that scales with the number of vehicles over the classical VRP. To test the theoretical worst-cases against the solutions of benchmark datasets, a heuristic is developed based on embedding a random variable neighborhood search within an iterative local search heuristic. Results suggest that the split deliveries do in fact improve total cost and last delivery times over the classical formulation. The SVRP has been developed classically for use with vehicles such as trucks which have large payload capacities and typically long ranges for deliveries, but are limited to traversing on roads. Unmanned aerial vehicles (UAVs) are useful for their high maneuverability, but suffer from limited capacity for payloads and short ranges. The classical SVRP formulation is extended to one more suitable for UAVs by accounting for limited range, limited payloads, and the ability to swap batteries at known locations. Instead of Euclidean distances, path plans which are adjusted for a known, constant wind underlie the cost matrix of the optimization problem. The effects of payload on the vehicle's range are developed using propeller momentum theory, and simulations verify that the proposed approach could be used in a realistic scenario. Two novel MRAC laws are then developed. The first, MRAC laws for prescribed performance, exploits barrier Lyapunov functions and a 2-Layer approach to guarantee user-defined performance. This control law allows unknown nonlinear systems to verify a user-defined rate of convergence of the tracking error while verifying apriori control and tracking error constraints. Numerical simulations are performed on the roll dynamics of a delta-wing aircraft. The second novel MRAC law is MRAC for switched dynamical systems which is proven in two different mathematical frameworks. Applying the Caratheodory framework, it is proven that if the switching signal has an arbitrarily small, but non-zero, dwell-time, then solutions of both the trajectory tracking error's and the adaptive gains' dynamics exist, are unique, and are defined almost everywhere, and the trajectory tracking error converges asymptotically to zero. Employing the Filippov framework, it is proven that if the switching signal is Lebesgue integrable and has countably many points of discontinuity, then maximal solutions of both the trajectory tracking error and the adaptive gains dynamics exist and are defined almost everywhere, and the trajectory tracking error converges to zero asymptotically. The proposed MRAC law is experimentally verified in the case where a UAV with tilting propellers is tasked with mounting an unknown camera onto a wall. The previous results are then combined into a novel application in construction. A method for using a UAV to measure autonomously the moisture of an exterior precast concrete envelope is developed which can provide data feedback through contact-based measurements to improve safety and real-time data acquisition through the integration with the Building Information Model (BIM). To plan the path of the vehicle, the path planning and SVRP for UAV approaches developed in previous chapters are utilized. To enable the UAS to contact surfaces, a switched MRAC law is employed to control the vehicle throughout and guarantee successful measurements. A full physics-based simulation environment is developed, and the proposed framework is used to simulate taking multiple measurements. / Doctor of Philosophy / The main goal of this dissertation is to provide an implementable approach to the routing and control problem for unmanned aerial vehicles (UAVs) tasked with delivering payloads or taking images or videos of known locations. To plan routes for the fleet of vehicles, a split vehicle routing (SVRP) approach is utilized. UAVs are useful for their high maneuverability, but suffer from limited capacity for payloads and short ranges. Before extending the SVRP to a formulation more suitable for UAVs, we study the effects of using two different objective functions on the solutions to the optimization problem through a worst-case analysis. Namely, we study the minimum total cost function and the minimum last delivery function and their effects on both the classical vehicle routing problem (VRP), where only one vehicle can visit each customer, and the SVRP, where multiple vehicles can visit each customer. A custom heuristic is developed to solve several benchmark instances, and the results suggest that using the SVRP can save in total cost and last delivery over the VRP when using the same objective functions. The classical SVRP formulation is then extended to one more suitable for UAVs by accounting for limited range, limited payloads, and the ability to swap batteries at known locations. Instead of using straight line approaches to traversing between locations, a path planning approach is utilized and wind is accounted for. The effects of payload on the vehicle's range are also considered, and simulations verify that the proposed approach could be used in a realistic scenario. After developing a routing approach for UAVs, the control problem is considered. The first control approach developed is for unknown nonlinear systems which necessitate control and tracking error constraints that can be set before the start of the mission. This result is achieved using a novel model reference adaptive control (MRAC) approach. In addition to verifying the constraints, a drawback of classical MRAC approaches, the poor performance in the transient stages, is addressed by providing the ability to guarantee a user-defined rate of convergence of the system. Numerical simulations are performed on the roll dynamics of a delta-wing aircraft. A second MRAC approach is then developed for the cases in which the UAVs may be tasked with installing a payload at the customer location. An approach is used where the vehicles are considered to have different flight states, one where the vehicle is in free flight, and one where the vehicle contacts the wall. These types of systems are denoted as switched dynamical systems, and an adaptive control law is developed for unknown nonlinear switched plants that must follow the trajectory of user-defined linear switched reference models. The proposed MRAC law is experimentally verified in the case where a UAV with tilting propellers is tasked with mounting an unknown camera onto a wall. Finally, we seek to combine the new routing and control approach into an application to improve safety within a construction site. A method for using a UAV to measure autonomously the moisture of an exterior precast concrete envelope is developed which can provide data feedback through contact-based measurements to improve safety and real-time data acquisition through the integration with the Building Information Model (BIM). To plan the path of the vehicle, the path planning and SVRP for UAV approaches developed in previous chapters are utilized. To enable the UAS to contact surfaces, a switched MRAC law is employed to control the vehicle throughout and guarantee successful measurements. A full physics-based simulation environment is developed, and the proposed framework is used to simulate taking multiple measurements.

iv ABSTRACT ADAPTIVE CONTROL OF GUIDED MISSILES Tiryaki Kutluay, Kadriye Ph.D., Department of Aerospace Engineering Supervisor: Asst. Prof. Dr. Ilkay Yavrucuk February 2011, 147 Pages This thesis presents applications and an analysis of various adaptive control augmentation schemes to various baseline flight control systems of an air to ground guided missile. The missile model used in this research has aerodynamic control surfaces on its tail section. The missile is desired to make skid to turn maneuvers by following acceleration commands in the pitch and yaw axis, and by keeping zero roll attitude. First, a linear quadratic regulator baseline autopilot is designed for the control of the missile acceleration in pitch axis at a single point in the flight envelope. This baseline autopilot is then augmented with a Direct Model Reference Adaptive Control (DMRAC) scheme using Neural Networks for parameter estimation, and an L1 Adaptive Control scheme. Using the linearized longitudinal model of the missile at the design point, simulations are performed to analyze and demonstrate the performance of the two adaptive augmentation schemes. By injecting uncertainties to the plant model, the effects of adaptive augmentations on the linear baseline autopilot are examined. v Secondly, a high fidelity simulation software of the missile is used in order to analyze the performance of the adaptive augmentations in 6 DoF nonlinear flight simulations. For the control of the missile in three axis, baseline autopilots are designed using dynamic inversion at a single point in the flight envelope. A linearizing transformation is employed during the inversion process. These coarsely designed baseline autopilots are augmented with L1 adaptive control elements. The performance of the adaptive control augmentation system is tested in the presence of perturbations in the aerodynamic model and increase in input gain, and the simulation results are presented.