Doctor of Philosophy, The Ohio State University, 2003, Mechanical Engineering

Electromagnetic actuators (EMA), which incorporate solenoids, are increasingly becoming the actuator of choice in industry lately, due to their ruggedness, low cost, and relative ease of control. Latest applications of solenoid based EMA's include Electromagnetic Valve Actuation (EMV) systems. This application presents challenges that require the improvement of the dynamic characteristics of the EMA. Some of these problems include, but are not limited to, quiet operation, reduced bounce, less energy consumption, trajectory shaping with a minimum number of measurements, and high actuation speeds. These demands, coupled with the nonlinear dynamics of the EMA, make the use of classical control strategies a less attractive option. A possible attempt to arrive at intermediate solutions to these problems should include some amount of model based robust control strategy. This includes the development of an accurate but simple control based model and a robust digital control strategy. In this study a basic nonlinear model for a solenoidal EMA will be developed, and validated, which will include bounce, leakage inductance and temperature effects. The model is formulated for the linear legion (region before saturation) of the actuator dynamics, but validation will include operation in the saturation region as well. This effectively means that a nonlinear model will be developed that is simple but accurate enough for control, neglecting hysteresis and magnetic saturation. Next, an EMV will be designed and built. A nonlinear model for the EMV will be developed and validated. This model will include secondary nonlinearities like saturation, hysteresis, mutual inductance and bounce. In this study a variable that is easier and cheaper to measure, current, will be measured and the information of the position and velocity variables will be estimated from this measurement. The position estimate will be used for control. This is called Sensorless Control. The control objective is to r (open full item for complete abstract)

Committee: Gregory Washington (Advisor) Subjects:

Doctor of Philosophy, The Ohio State University, 2007, Electrical Engineering

Flow control refers to the ability to manipulate fluid flow so as to achieve a desired change in its behavior, which offers many potential technological benefits, such as reducing fuel costs for vehicles and improving effectiveness of industrial processes. An interesting case of flow control is cavity flow control, which has been the motivation of this study: When air flow passes over a shallow cavity a strong resonance is produced by a natural feedback mechanism, scattering acoustic waves that propagate upstream and reach the shear layer, and developing flow structures. These cause many practical problems including damage and fatigue in landing gears and weapons bays in aircrafts. Presently there is a lack of sufficient mathematical analysis and control design tools for flow control problems. This includes mathematical models that are amenable to control design. Recently reduced-order modeling techniques, such as those based on proper orthogonal decomposition (POD) and Galerkin projection (GP), have come to interest. However, a main issue with these models is that the effect of boundary conditions, which is where the control input is, gets embedded into system coefficients. This results in a form quite different from what one deals with in standard control systems framework, which is a set of ordinary differential equations (ODE) where the input appears as an explicit term. Another issue with the standard POD/GP models is that they do not yield to systems that have any apparent structure in their coefficients. This leaves one with little choice other than to neglect the nonlinearities of the models and employ standard linear control theory based designs. The research presented in this thesis makes an effort at closing the gaps mentioned above by 1) presenting a reduced-order modeling method utilizing a novel technique for input separation on POD/GP models, 2) introducing a technique based on averaging theory and center manifold theory so as to reveal certain struct (open full item for complete abstract)

Committee: Andrea Serrani (Advisor) Subjects:

Doctor of Philosophy (Ph.D.), University of Dayton, 2022, Electrical Engineering

In this work, we investigate predictive control techniques for linear and nonlinear systems subject to an exogenous disturbance. We start by implementing a hybrid Model Predictive Control algorithm for a generator system connected to a constant power load. The majority of this work focuses on an extensive formal proof of stability for a Predictive Reference Governor algorithm that has been previously demonstrated on the same generator system. We also develop a robust stability proof that accounts for model mismatch between the PRG prediction model and the inner loop system. Finally, a constraint management procedure is incorporated into the PRG and the modified PRG algorithm is tested on the generator system. We show a significant performance improvement on the generator system with the PRG compared to not using the PRG.

Committee: Raúl Ordóñez (Advisor); Brandon Hencey (Committee Member); Keigo Hirakawa (Committee Member); Malcolm Daniels (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy (PhD), Wright State University, 2020, Electrical Engineering

The pulse-width modulated (PWM) dc-dc converters play a vital role in several industrial applications that include motor drives, electric vehicles, dc distribution systems, and consumer electronics. The switched-mode power converters step the input voltage up or down based on their typology and provide a regulated output voltage. The stability and regulation performance of a power converter can tremendously be improved via a suitable control design. However, due to the nonlinearity of the power converters and the presence of the line and load disturbances, the design of a robust and low-cost control circuit becomes a challenging task. The sliding-mode control of the dc-dc converters has been studied for decades because of its robustness, design simplicity, and suitability for variable structure systems. Despite the merits of the sliding-mode control method, the linear controllers are still dominant and attractive to the commercial applications since they require less design efforts and can be implemented using simple analogue circuits. This research aims to develop simplified sliding-mode control circuits for the classical PWM dc-dc converters in continuous-conduction mode (CCM). The control objectives are to maintain a constant switching frequency, enhance the transient response, provide wide operating range, and track the desired reference voltage under large disturbances. In order to design and test the control circuit, an accurate power converter model should be derived. Hence, large-signal non-ideal averaged models of dc-dc buck and boost converters in CCM are developed. The models are simulated in MATLAB/SIMULINK and compared with the corresponding circuits in SaberRD simulator for validation purpose. Next, PWM-based simplified sliding-mode voltage and current control schemes are designed for the dc-dc buck and boost converters in CCM, respectively. The design procedure and the analogue realization of the control equations are presented, where the control c (open full item for complete abstract)

Committee: Marian K. Kazimierczuk Ph.D. (Advisor); Raúl Ordóñez Ph.D. (Committee Member); Saiyu Ren Ph.D. (Committee Member); Yan Zhuang Ph.D. (Committee Member); Xiaodong Zhang Ph.D. (Committee Member) Subjects: Electrical Engineering

MS, University of Cincinnati, 2018, Engineering and Applied Science: Aerospace Engineering

Aerospace applications are composed of many dynamic systems which are coupled, nonlinear, and difficult to control. Fuzzy logic (FL) systems provides a means by which to encode expert knowledge into a set of rules which can produce highly nonlinear control signals; this is possible because FL, like many other soft computational methods is a universal approximator. While FL systems alone excel at encapsulating expert knowledge bases, when coupled with genetic algorithms (GA), they can learn the knowledge base from evolutionary repetition. It is the goal of this work to present the efficacy of hybrid genetic fuzzy systems (GFS) in a variety of applications. This will be achieved through exploring three specific use cases. First, a variation of a benchmark problem presented at the 1990 American Control Conference is used to demonstrate the robustness of FL control as well as the utility of GAs in the learning process. The results are a controller that is far more resistant to even large changes in the plant dynamics compared to a linear controller and a process by which a class of controllers may be quickly tuned for changes to the plant system. The next problem applies the same approach to an elevator actuator for pitch control of an F-4 Phantom. This controller is tuned for a nominal case and ten subjected to the same plant with degraded aerodynamic coefficients. It is compared to a well-tuned PID controller. The effort culminates in a practical application of a FL system to guide a small unmanned aerial system (sUAS) to a precision landing on a target platform moving with uncertain velocity. This was accomplished using custom developed Python software for GFS control in conjunction with Robot Operating System (ROS) and a simulation environment called Gazebo. Heavy emphasis was placed on using only software components which can be easily implemented on popular hardware platforms. ROS was critical to meeting this goal, as well as the open source flight cont (open full item for complete abstract)

Committee: Kelly Cohen Ph.D. (Committee Chair); Manish Kumar Ph.D. (Committee Member); George T. Black M.S. (Committee Member) Subjects: Engineering

Doctor of Philosophy (PhD), Wright State University, 2017, Engineering PhD

The research on distributed multi-agent systems has received increasing attention due to its broad applications in numerous areas, such as unmanned ground and aerial vehicles, smart grid, sensor networks, etc. Since such distributed multi-agent systems need to operate reliably at all time, despite the possible occurrence of faulty behaviors in some agents, the development of fault-tolerant control schemes is a crucial step in achieving reliable and safe operations. The objective of this research is to develop a distributed adaptive fault-tolerant control (FTC) scheme for nonlinear uncertain multi-agent systems under intercommunication graphs with asymmetric weights. Under suitable assumptions, the closed-loop system's stability and leader-follower cooperative tracking properties are rigorously established. First, a distributed adaptive fault-tolerant control method for nonlinear uncertain first-order multi-agent systems is developed. Second, this distributed FTC method is extended to nonlinear uncertain second-order multi-agent systems. Next, adaptive-approximation-based FTC algorithms are developed for two cases of high-order multi-agent systems, i.e., with full-state measurement and with only limited output measurement, respectively. Finally, the distributed adaptive fault-tolerant formation tracking algorithms for first-order multi-agent systems are implemented and demonstrated using Wright State's real-time indoor autonomous robots test environment. The experimental formation tracking results illustrate the effectiveness of the proposed methods.

Committee: Xiaodong Zhang Ph.D. (Advisor); Kuldip Rattan Ph.D. (Committee Member); Pradeep Misra Ph.D. (Committee Member); Yongcan Cao Ph.D. (Committee Member); Raul Ordonez Ph.D. (Committee Member); Mark Mears Ph.D. (Committee Member) Subjects: Electrical Engineering; Engineering

Doctor of Philosophy, The Ohio State University, 2017, Civil Engineering

Natural disasters are one of the constant challenges for designing new and strengthening existing infrastructures. Such hazards in the past have incurred significant loss of life and economic damage; therefore, further research is warranted in this area to enhance the health and minimize the cost of maintaining and upgrading infrastructures, improve residents' comfort, and enable achieving higher levels of life safety. To this end, the field of hazard mitigation and control focuses on performance improvement, safety, and cost effectiveness of structures mostly through minimizing large deformations of seismic-excited structures and suppressing the damage and collapse in dynamic systems due to excessive vibrations. Past developments in active and semi-active control designs, such as the commonly used state space controllers (e.g. linear quadratic regulator for fully observed systems and linear quadratic Gaussian for partially observed systems), consider linear feedback strategies. Meanwhile, such control strategies require linearization, and the system is usually linearized based on linear elastic properties. The control force is proportional to the state space vector and the dynamics and constraints of control devices are mainly ignored. The objective functions have restrictive forms, and are solely dependent on a second order convex function of the response variables. To overcome the aforementioned shortcomings, this dissertation develops new stochastic control algorithms for active and semi-active control strategies. This research concentrates on the development of frameworks that incorporate nonlinearity of the system, uncertainty of the excitation, and constraints and dynamics of the control device. Control designs are developed based on different objective functions such as higher order polynomials of response variables, reliability of the structure, and life cycle cost of the system considering hazard risks in seismic prone areas. In particular, a nonlinear (open full item for complete abstract)

Committee: Abdollah Shafieezadeh Dr. (Advisor); Natassian Brenkus Dr. (Committee Member); Halil Sezen Dr. (Committee Member); Wei Zhang Dr. (Committee Member) Subjects: Civil Engineering

Doctor of Philosophy, Case Western Reserve University, 2016, EECS - System and Control Engineering

The closed-loop optimal control of multiple model linear systems with unknown parameters is investigated. The Bellman equation is modified to include the discrete random variable of the system mode conditioned on the measurements, and is then used to determine the optimal state feedback or dynamic output feedback controllers. Dynamic programming with the modified Bellman equation is used to calculate the optimal cost with the dual covariance. The dual covariance quantifies the probing aspects of the controller and is demonstrated that the closed-loop state or dynamic output feedback controllers have the dual property for the discrete-time multiple model linear systems with unknown parameters studied in this work. Monte Carlo simulations are used to show that the closed-loop control with state or dynamic output feedback always performs better than controllers such as the Certainty Equivalence or DUL controllers. Finally, the direct discrete-time implementation of the dual dynamic output feedback controller developed in this work is applied to the control of the nonlinear F-16 aircraft. The dual regulator is designed for stability augmentation in the context of reconfigurable control using the multiple model formulation integrated with flight and propulsion to accommodate sensor, actuator, and engine faults. The design process is explained in the context of trim, linearization, calculation of the mode probabilities, and tuning of the Kalman filters and includes the implementation of a six-stage dual regulator with a bank of parallel Kalman filters. The flight simulation results are presented for cases such as speed and pitch rate sensor faults, 1.5% and 3% losses of elevator actuator power, and 4% loss of engine power during steady-state level flight of the nonlinear F-16 aircraft model.

Committee: Kenneth Loparo PhD (Advisor); Marc Buchner PhD (Committee Member); Vira Chankong PhD (Committee Member); Richard Kolacinski PhD (Committee Member) Subjects: Aerospace Engineering; Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2015, Electrical and Computer Engineering

In the past two decades, there has been a renewed and sustained effort devoted to modeling the dynamics of air-breathing hypersonic vehicles, for both simulation and control design purposes. The highly nonlinear characteristics of flight dynamics in hypersonic regimes and the consequent significance variability of the response with the operating conditions requires the development of innovative flight control solutions, hence the development of suitable model of the vehicle dynamics that are amenable to design, validation and rapid calibration of control algorithms. In this dissertation, a control-oriented and a simulation model of a generic hypersonic vehicle were derived to support the design and calibration of model-based flight controllers. A nonlinear robust adaptive controller was developed on the basis of the control-oriented model, that was shown to provide stable trajectory tracking in higher fidelity computer simulations. The first stage of this research was the development of a control design model (CDM) for the 6-degree-of-freedom dynamics of an air-breathing hypersonic aircraft based on an available high-fidelity first principle model. A method that incorporates the theory of compressible fluid dynamics and system identification methods, was proposed and implemented. The development of the CDM is based on curve fit approximation of the forces and moments acting on the vehicle, making the model suitable for control design. Kriging and Least Squares methods were used to find the most appropriate curve-fitted model of the aerodynamic forces for both the control design and the control simulation models. It was shown that the 6-DOF model can be both categorized as an under-actuated mechanical system, as well as an over-actuated system with respect to a chosen in- put/output pair of interest. An important contribution of this work is the development of a nonlinear adaptive controller for the 6-DOF control design model. The controller was endowed with a mod (open full item for complete abstract)

Committee: Andrea Serrani (Advisor); Vadim Utkin (Committee Member); Kevin Passino (Committee Member); Can Koksal (Committee Member) Subjects: Electrical Engineering

Master of Sciences, Case Western Reserve University, 2015, EECS - Electrical Engineering

This paper introduces an innovative robust and nonlinear control design methodology for high-performance motor control in optical telescopes less than one meter in diameter. The dynamics of optical telescopes typically vary according to azimuth and altitude angles, temperature, friction, speed, and acceleration leading to nonlinearities and plant parameter uncertainty. The methodology proposed in this paper combines robust Quantitative Feedback Theory (QFT) techniques, the describing function method, and optimal control with nonlinear switching strategies that achieve simultaneously the best characteristics of a set of very active (fast) robust QFT controllers, very stable (slow) robust QFT controllers, and a pair of controllers designed around system limit cycles for high precision. A general dynamic model and a variety of specifications from several different commercially available amateur Newtonian telescopes are used for the controller design as well as the simulation and validation. It is also proven that the nonlinear/switching controller is stable for any switching strategy and switching velocity, according to described frequency conditions based on common quadratic Lyapunov functions and the circle criterion.

Committee: Mario Garcia-Sanz Ph.D (Advisor); Mario Garcia-Sanz Ph.D (Committee Chair); Francis Merat Ph.D (Committee Member); Marc Buchner Ph.D (Committee Member) Subjects: Electrical Engineering; Engineering

Doctor of Philosophy, The Ohio State University, 2014, Electrical and Computer Engineering

This work presents the development of an integrated flight controller for a generic model of a hypersonic air-breathing vehicle. The flight control architecture comprises a guidance and trajectory planning module and a nonlinear inner-loop adaptive controller. The emphasis of the controller design is on achieving stable tracking of suitable reference trajectories in the presence of a specific engine fault (inlet unstart), in which sudden and drastic changes in the vehicle aerodynamics and engine performance occur. First, the equations of motion of the vehicle for a rigid body model, taking the rotation of the Earth into account, is provided. Aerodynamic forces and moments and engine data are provided in lookup-table format. This comprehensive model is used for simulations and verification of the control strategies. Then, a simplified control-oriented model is developed for the purpose of control design and stability analysis. The design of the guidance and nonlinear adaptive control algorithms is first carried out on a longitudinal version of the vehicle dynamics. The design is verified in a simulation study aiming at testing the robustness of the inner-loop controller under significant model uncertainty and engine failures. At the same time, the guidance system provides reference trajectories to maximize the vehicle's endurance, which is cast as an optimal control problem. The design is then extended to tackle the significantly more challenging case of the 6-degree-of-freedom (6-DOF) vehicle dynamics. For the full 6-DOF case, the adaptive nonlinear flight controller is tested on more challenging maneuvers, where values of the flight path and bank angles exceed the nominal range defined for the vehicle. Simulation studies show stable operation of the closed-loop system in nominal operating conditions, unstart conditions, and during transition from sustained scramjet propulsion to engine failure mode.

Committee: Andrea Serrani Prof. (Advisor); Umit Ozguner Prof. (Committee Member); Zhang Wei Prof. (Committee Member) Subjects: Aerospace Engineering; Computer Engineering; Electrical Engineering; Engineering

PhD, University of Cincinnati, 2007, Engineering : Electrical Engineering

In this research, we study the reduced-order robust adaptive control design problem for a class of uncertain SISO linear systems , that are subject to system and measurement noise. Two controller order reduction methodologies are obtained with different level of simplification as compared to the full-order design. The key technique for these two order-reduction methodologies both lies in the modification of a particular step of the backstepping design in the controller design part. The first order-reduction methodology reduces the controller structure by n-1 or n-2 integrators, depending on the eigen-structure of a particular feedback matrix. The second order-reduction methodology simplifies controller structure by n integrators, the trade-off for this order reduction is that the worst-case estimate for the expanded state vector has to be chosen as a suboptimal choice, rather than the optimal choice. It is shown that the resulting reduced-order adaptive controllers preserve the strong robustness properties of the full-order adaptive controller in disturbance attenuation, boundedness of closed-loop signals, and output tracking. Simulation results corroborate our theoretical findings. Furthermore, convergence analysis is investigated for the reduced-order adaptive control system achieved using the first order-reduction design methodology. We explore the conditions under which various closed-loop signals converge. We rigorously prove that, whenever the exogenous disturbance inputs is of finite energy and bounded, and the reference trajectory and its derivatives up to r-th order are bounded, r being the relative degree of the transfer function of the true system, then a set of closed-loop signals are of finite energy and converge to zero; the system states and their estimates exhibit asymptotic behaviors with certain formats. If the r-th order time derivative of the reference trajectory is uniformly continuous, then the r-th order noiseless derivative of the output asym (open full item for complete abstract)

Committee: Dr. Emmanuel Fernandez (Advisor) Subjects: Engineering, Electronics and Electrical

PhD, University of Cincinnati, 2007, Engineering : Electrical Engineering

Three control design results based on worst-case analysis approach are presented in this dissertation. The first result is on robust adaptive control design for SISO linear systems with zero relative degree under noisy output measurements. The proposed adaptive controller asymptotically cancels out, at the output, the effect of exogenous sinusoidal disturbance inputs with unknown magnitude, phase, and frequency. The second result is on robust adaptive controller design for SISO linear systems with noisy output measurements and partly measured disturbances. The proposed controller achieves a guaranteed disturbance attenuation level with respect to the exogenous disturbance inputs, where the ultimate attenuation lower bound is equal to the noise intensity in the measurement channel. The closed-loop system is totally stable with respect to the disturbance input and the initial condition. Furthermore, it achieves asymptotic tracking of the reference trajectory for all uniformly bounded disturbance inputs that are of bounded energy. In addition, when the relative degrees from the measured disturbances to the output are no less than that from the control input, the controllers designed achieve zero disturbance attenuation level with respect to the measured disturbance inputs. The asymptotic tracking objective is achieved even if the measured disturbance is only uniformly bounded, without requiring it to be of finite energy. The third result is on robust adaptive controller design for a special class of MIMO system, which is composed of two SISO linear subsystems under noisy output measurements, S1 and S2, sequentially interconnected with additional feedback. The closed-loop system admits a guaranteed disturbance attenuation level with respect to the exogenous disturbance inputs, where the ultimate attenuation lower bound for the achievable performance level is equal to the noise intensity in the measurement channel of S1. All these strong robustness properties are illustr (open full item for complete abstract)

Committee: Dr. Emmanuel Fernandez (Advisor) Subjects:

MS, University of Cincinnati, 2006, Engineering : Electrical Engineering

A helicopter is a complex nonlinear system and also an under actuated system with fewer independent control actuators than degrees of freedom to be controlled, making the control difficult. There is a growing interest in the modeling and control of such systems using nonlinear dynamic models and nonlinear control. Analytical techniques based on Lyapunov theory are then used to design the controller and still the design can become extremely complex. Hence the existing control methods use linearization techniques on the actual nonlinear dynamics of the plant and linear control techniques. The resulting performance may not be satisfactory, especially when the system is subjected to unknown and sudden disturbances. In this thesis, we present a new Nonlinear and Adaptive controller design which uses the actual nonlinear model of the helicopter and not a linearized version. The design methodology basically involves making the combined dynamics of the helicopter and the controller resemble the dynamics of a nonlinear time varying electrical circuit having the required properties using a process similar to reverse engineering. The circuit template in turn is formed from well defined time varying and/or nonlinear electrical elements and using proper interconnections. The kind of elements used and the general form of the dynamics derived will depend upon the application. For example in the helicopter case, the closed loop dynamics of the helicopter and the controller expressed in terms of the error variable should point to a NLTV circuit with only passive elements. For this, the reactive elements should have their relaxation points (the points where the stored energy is zero) at and only at the origin. Also the stored energy should be monotonically increasing. We can bring in any knowledge including the structure that we have about the plant being controlled in enhancing the circuit.

Committee: Dr. Panapakkam Ramamoorthy (Advisor) Subjects:

Doctor of Philosophy, The Ohio State University, 2010, Mechanical Engineering

Smart structures are currently utilized in many applications from precision positioning control of large space structures to active vibration control of machine components. Despite their relative easiness in controlling, positioning accuracy and longevity can be compromised by the hysteresis. In addition, current active control algorithms are limited predominantly to the control of a single or multiple sinusoidal waves and these are incapable of addressing more complicated multi-spectral signals such as modulated signatures. This dissertation introduces model-based and nonlinear control techniques aimed at the reduction of hysteretic effect and for their application to active motion control. A nonlinear energy-based hysteresis model is developed for a piezoelectric stack actuator and model predictive sliding mode control is applied to force the system state to reach a sliding surface in an optimal manner and to accurately track the reference signal. This method is employed on pre-stressed curved unimorph actuators (modeled by a second order differential equation) with an additional time delay term to describe the hysteretic effect. Simulations and experiments are conducted to validate this approach, and the results highlight significantly improved hysteresis reduction in the displacement control mode. Also, it has been verified that the performance of the novel control methods is not much affected by the accuracy of actuator model. Next, enhanced adaptive filtering algorithms are developed with application to active vibration control. A feedback loop with the model predictive sliding mode control is introduced in the adaptive filtering system. The goal of this study is to manage multi-spectral signals while achieving smooth and effective convergence, self-adaptability, and stability. The performance for new adaptive filtering algorithms is validated numerically and experimentally for different signals and other prevailing characteristics. The proposed algorithms are (open full item for complete abstract)

Committee: Gregory Washington PhD (Advisor); Rajendra Singh PhD (Advisor); Vadim Utkin PhD (Committee Member); Marcelo Dapino PhD (Committee Member) Subjects: Engineering

Doctor of Philosophy, The Ohio State University, 2010, Electrical and Computer Engineering

This dissertation presents the design of two nonlinear robust controllers for an air-breathing hypersonic vehicle model capable of providing stable tracking of velocity and altitude (or flight-path angle) reference trajectories. To overcome the analytical intractability of a dynamical model derived from first principles, a simplified control-oriented model is used for control design. The control-oriented model retains the most important features of the model from which it was derived, including the non-minimum phase characteristic of the flight-path angle dynamics and strong couplings between the engine and flight dynamics. The first control design considers as control inputs the fuel equivalence ratio and the elevator and canard deflections. A combination of nonlinear sequential loop-closure and adaptive dynamic inversion has been adopted for the design of a dynamic state-feedback controller. An important contribution given by this work is the complete characterization of the internal dynamics of the model has been derived for Lyapunov-based stability analysis of the closed-loop system, which includes the structural dynamics. The results obtained address the issue of stability robustness with respect to both parametric model uncertainty, which naturally arises in adopting reduced-complexity models for control design, and dynamic perturbations due to the flexible dynamics. In the second control design a first step has been taken in extending those results in the case in which only two control inputs are available, namely the fuel equivalence ratio and the elevator deflection. The extension of these results to this new framework is not trivial since several issues arise. First of all, the vehicle dynamics are characterized by exponentially unstable zero-dynamics when longitudinal velocity and flight-path angle are selected as regulated output. This non-minimum phase behavior arises as a consequence of elevator-to-lift coupling. In the previous design the canard was s (open full item for complete abstract)

Committee: Andrea Serrani (Advisor); Stephen Yurkovich (Committee Member); Kevin Passino (Committee Member); Scott Gaudi (Committee Member) Subjects: Electrical Engineering

Master of Science (MS), Ohio University, 2007, Electrical Engineering & Computer Science (Engineering and Technology)

This thesis presents an improvement in the performance of a three degrees of freedom differential thrust control testbed by considering the actuator dynamics. The testbed consists of three propellers that are used to produce thrust as well as attitude control for vertical takeoff and landing flight. Actuator dynamics consist of the motor-propeller dynamics and the nonlinear mapping relating the aerodynamic torques to the propeller speed. A previous controller was designed by neglecting the motor-propeller dynamics and the control allocation was done assuming a linear static relationship between aerodynamic torques and motor voltages. This work will determine the nonlinear control allocation mapping and model the motor-propeller dynamics as a first-order linear system. Simulation and real-time results showing an improvement in the performance of the testbed are presented by replacing the linear control allocation with nonlinear control allocation and by compensating for the motor-propeller dynamics. Further, the existing controller is redesigned considering the gyroscopic effects produced due to the spinning propellers.

Committee: Jianchao Zhu (Advisor) Subjects:

Doctor of Philosophy (PhD), Ohio University, 2007, Electrical Engineering & Computer Science (Engineering and Technology)

Advanced nonlinear control design methods usually depend on an analytical plant model, which in many practical applications, is often inaccurate or unavailable. Neural networks are a powerful tool to enhance a dynamic model if one is available but inaccurate, or to build a system dynamic model from experimental data. Trajectory linearization control (TLC) is a nonlinear control design method, which combines nonlinear dynamic inversion and linear time-varying (LTV) feedback stabilization to achieve robust tracking control for a broad class of nonlinear dynamic systems. In this dissertation, the research goal is to develop theories and methodologies to improve the understanding and applicability of TLC for inaccurate or lack of dynamic models by using neural networks. To this end, the following research objectives have been achieved. First, rigorous stability robustness analyses of TLC subject to the regular perturbation and singular perturbation are established. Second, a continuous-time nonlinear system identification method using neural network is developed. Third, a neural network trajectory linearization control (NNTLC) design procedure with stability analysis is proposed. Fourth, an adaptive neural network trajectory linearization control (ANNTLC) scheme is presented, in which the neural network control compensates for the system uncertainty adaptively. Illustrative examples of nonlinear applications of TLC, NNTLC and ANNTLC are also presented.

Committee: J. Zhu (Advisor) Subjects:

Master of Science (MS), Ohio University, 2001, Electrical Engineering & Computer Science (Engineering and Technology)

Mass movement mechanism for nonlinear, robust and adaptive control of flexible structures

Committee: Dennis Irwin (Advisor) Subjects:

Master of Science (MS), Ohio University, 2006, Electrical Engineering & Computer Science (Engineering and Technology)

This thesis presents derivation of a trajectory tracking and stair climbing stabilization controller for a 4x4 skid-steered wheeled mobile robot (SSWR). The robot vehicle is a sturdy platform actuated by DC motors capable of traversing difficult terrain. For trajectory tracking, an essential capability for autonomous operation, a reliable and robust controller is needed. In addition, as the vehicle is unstable with manual control while climbing stairs, the controller is required to stabilise it during stair/ramp climb. The robot vehicle is modelled with six degrees of freedom (6DOF) rigid body equations and an efficient control algorithm, called Trajectory Linearisation Control (TLC), is used to tackle the challenges posed by nonlinearities of the model. In TLC, state dynamics are linearised along the trajectory being tracked and PI control is used to stabilise tracking error dynamics. Kinematics and dynamics are controlled individually using feedback loops, where the former constitutes the outermost loop. The main contribution of this work is analysis of 6DOF physical model and a consolidated simple controller for planar tracking and stair climbing stabilization for an SSWR. Simulation results promise that a stable climb on 20° steep staircase is possible with current vehicle configuration. Monte Carlo simulations prove that the controller is robust to realistic dispersions of frictional and physical parameters. Effects of perturbations in these parameters have been studied and improvements in mechanical design are suggested.

Committee: Jianchao Zhu (Advisor) Subjects: