The dynamic behavior of a vehicle is predominantly controlled by the forces and moments generated at the contact patch between the tire and the road surface. As a result, tire characteristics can dramatically change vehicle response, especially during maneuvers that yields the tires to reach to the limits of its adhesion capacity. To assist the driver in such cases and to prevent other possible instability scenarios, various vehicle control systems e.g. anti-lock brakes (ABS), stability controllers (ESP, ESC) or rollover mitigation schemes are introduced, which are generally known as active vehicle safety systems. Based on the above facts, one can easily come to the conclusion that to improve upon the current control algorithms developed for the technology in use; a vehicle control system design requires accurate knowledge of the tire states. This study proposes the use of a smart tire system that can provide information on momentary variation of tire features through the sensor units attached directly on the tire and develops control algorithms based on this information to assure the match-up between tire and controller dynamics. A prototype smart tire system was developed for field testing and for detailed analysis of its potential. Based on the collected prototype data, novel observer and controller schemes were developed to obtain dynamic tire state information and to improve vehicle handling performance. The proposed algorithms were implemented and evaluated using numerical analysis in Matlab/SimulinkR environment. For a more realistic simulation environment, vehicle models were integrated from Mechanical Simulations CarSimR® software suite. / Ph. D.

Advances in transistor-integration technology and multi-core technology of the latest microprocessors have driven transient requirements to become more and more stringent. Rather than relying on the bulky output capacitors as energy-storage devices, increasing the control bandwidth (BW) of the multiphase voltage regulator (VR) is a more cost-effective and space-saving approach. However, it is found that the stability margin of current-mode control in high-BW design is very sensitive to operating conditions and component tolerance, depending on the performance of the current-sensing techniques, modulation schemes, and interleaving approaches. The primary objective of this dissertation is to investigate an advanced multiphase current-mode control, which provides accurate current sensing, enhances the stability margin in high-BW design, and adaptively compensates the parameter variations. Firstly, an equivalent circuit model for generic current-mode controls using DCR current sensing is developed to analyze the impact of component tolerance in high-BW design. Then, the existing state-of-the-art auto-tuning method used to improve current-sensing accuracy is reviewed, and the deficiency of using this method in a multiphase VR is identified. After that, enlightened by the proposed model, a novel auto-tuning method is proposed. This novel method features better tuning performance, noise-insensitivity, and simpler implementation than the state-of-the-art method. Secondly, the current state-of-the-art adaptive current-mode control based on constant-frequency PWM is reviewed, and its inability to maintain adequate stability margin in high-BW design is recognized. Therefore, a new external ramp compensation technique is proposed to keep the stability margin insensitive to the operating conditions and component tolerance, so the proposed high-BW constant-frequency control can meet the transient requirement without the presence of bulky output capacitors. The control scheme is generic and can be used in various kinds of constant-frequency controls, such as peak-current-mode, valley-current-mode, and average-current-mode configurations. Thirdly, an interleaving technique incorporating an adaptive PLL loop is presented, which enables the variable-frequency control to push the BW higher than proposed constant-frequency control, and avoids the beat-frequency input ripple. A generic small-signal model of the PLL loop is derived to investigate the stability issue caused by the parameter variations. Then, based on the proposed model, a simple adaptive control is developed to allow the BW of the PLL loop to be anchored at the highest phase margin. The adaptive PLL structure is applicable to different types of variable-frequency control, including constant on-time control and ramp pulse modulation. Fourthly, a hybrid interleaving structure is explored to simplify the implementation of the adaptive PLL structure in an application with more phases. It combines the adaptive PLL loop with a pulse-distribution technique to take the advantage of the high-BW design and fast transient response without adding a burden to the controller implementation. As a conclusion, based on the proposed analytical models, effective control concepts, systematic optimization strategies, viable implementations are fully investigated for high-BW current-mode control using different modulation techniques. Moreover, all the modeling results and the system performance are verified through simulation with a practical output filter model and an advanced mixed-signal experimental platform based on the latest MHz VR design on the laptop motherboard. In consequence, the multiphase VRs in future computation systems can be scalable easier with proposed multiphase configurations, increase the system reliability with proposed adaptive loop compensation, and minimize the total system footprint of the VR with the superior transient performance. / Ph. D.

This dissertation is concerned with the active control of local sound fields, as applied to an active headrest system. Using loudspeakers and microphones, an active headrest is capable of attenuating ambient noise and providing a comfortable acoustic environment for an occupant. A finite element (FE) model of an active headrest is built and analyzed such that the expected noise reduction levels could be quantified for various geometries as well as primary sound field conditions. Both plane wave and diffuse primary sound fields are considered and it is shown that the performance deteriorates for diffuse sound fields. It is then demonstrated that virtual sensing can greatly improve the spatial extent of the quiet zones as well as the attenuation levels. A prototype of the active headrest was constructed, with characteristics similar to those of the FE model, and tested in both anechoic and reverberant sound fields. Multichannel feedforward and feedback control architectures are implemented in real-time and it is shown that adaptive feedback systems are capable of attenuating band-limited disturbances. The spatial attenuation pattern surrounding the head is also measured by shifting the head to various positions and measuring the attenuation at the ears. Two virtual sensing techniques are compared in both feedback and feedforward architectures. The virtual microphone arrangement, which assumes that the primary sound field is equivalent at the physical and virtual locations, results in the best performance when used in a feedback system attenuating broadband disturbances. The remote microphone technique, which accounts for the transfer response between the physical and virtual locations, offers the best performance for tonal primary sound fields. In broadband sound fields, a causal relationship rarely exists between the physical and virtual microphones, resulting in poor performance. / PHD

In order to function safely in a dynamic environment with humans and obstacles, robots require active compliance control with force feedback. In these applications the control law typically includes full dynamics compensation to decouple the joints and cancel out nonlinearities, for which a high-fidelity model of the robot is required. In the case of a 3D-printed robot, components cannot be easily modeled due non-uniform densities, inconsistencies among the 3D printers used in manufacturing, and the use of different plastics with mechanical properties that are not widely known. To address this issue, this thesis presents an adaptive control framework which modifies the model parameters online in order to achieve satisfactory tracking performance. The inertial properties are estimated by adapting with respect to functions of the unknown parameters. This is achieved by rewriting the robot dynamics equations as the product of a matrix of known nonlinear functions of the joint states and a vector of constant unknowns. The result is a nonlinear system linearly parameterized in terms of the of the unknowns, which can be estimated using adaptation laws derived from Lyapunov stability theory. The proposed control system consists of an outer-loop impedance controller to regulate deviations from the nominal trajectory in the presence of disturbances, and an inner-loop force controller to track the joint torques commanded by the outer-loop. The proposed system is evaluated on an early prototype consisting of a 3DOF leg, and two actuator test setups for the low-level controller. / Master of Science / In order to function safely in a dynamic environment with humans and obstacles, a robot must be able to actively control its interaction forces with the outside environment. In these applications a high-fidelity model of the robot is required. In the case of a 3D-printed robot, the components in the robot cannot be easily modeled due non-uniform densities, inconsistencies among the 3D printers used in manufacturing, and the use of different plastics with mechanical properties that are not widely known. To address this issue, this thesis presents an adaptive control framework which actively modifies the model parameters in order to achieve satisfactory tracking performance. In this work, the equations of motion of the robot are manipulated in such a way that the unknown quantities are separated from the known quantities. The unknowns are updated in real time using adaptive laws derived from Lyapunov stability theory. The proposed control system consists of a high-level torque controller to regulate deviations from the nominal trajectory, and a low-level force controller to track the joint torques commanded at the high-level. The proposed system is evaluated on an early prototype of the robot consisting of a 3 degree of freedom leg, and two actuator test setups for the low-level controller.

The polynomial-chaos expansion (PCE) approach to modeling provides an estimate of the probabilistic response of a dynamic system with uncertainty in the system parameters. A novel adaptive parameter estimation method exploiting the polynomial-chaos representation of a general quarter-car model is presented. Because the uncertainty was assumed to be concentrated in the sprung mass parameter, a novel pseudo mass matrix was developed for generating the state-space PCE model. In order to implement the PCE model in a real-time adaptation routine, a novel technique for representing PCE output equations was also developed. A simple parameter estimation law based on the output error between measured accelerations and PCE acceleration estimates was developed and evaluated through simulation and experiment. Simulation results of the novel adaptation algorithm demonstrate the estimation convergence properties as well as its limitations. The simulation results are further verified by a real-time experimental implementation on a quarter-car test rig. This work presents the first truly real-time implementation of a PCE model. The experimental real-time implementation of the novel adaptive PCE estimation method shows promising results by its ability to converge and maintain a stable estimate of the unknown parameter. / Master of Science

Adaptive controllers allow robots to perform a wide variety of tasks, but the extensive computations required have generated an interest in developing decentralized adaptive controllers. Horner has designed an adaptive controller for a four-degree-of-freedom mobile robot and tested it through simulations. The study described in this thesis uses the techniques described by Horner to design and test a decentralized model reference adaptive controller (DMRAC) for a physical four-degree-of-freedom mobile robot. The study revealed several difficulties in implementing this design. Most notably, the robot available for the research did not allow for the measurement of joint velocity, so it was necessary to estimate the velocity as the derivative of the position measurement. The noise created by this estimation made completion of testing impossible. Future research should be performed on a robot that provides joint velocity measurement. Alternatively, a study could include state estimation as part of the controller, thus reducing and possibly eliminating the need for velocity measurement. / Master of Science

Due to a recent number of undesired rollovers in the field for the studied vehicle, rollover mitigation strategies have been investigated and developed. This research begins with the study of the tire, as it is the single component on the vehicle responsible for generating all of the non-inertial forces to direct the motion of the vehicle. Tire force and moment behavior has been researched extensively and several accurate tire models exist. However, not much research has been performed on off-road tire models. This research develops an off-road tire model for the studied vehicle by first using data from rolling road testing to develop a Pacejka Magic Formula tire model and then extending it to off-road surfaces through the use of scaling factors. The scaling factors are multipliers in the Magic Formula that describe how different aspects of the force and moment curves scale when the tire is driven on different surfaces. Scaling factors for dirt and gravel driving surfaces were obtained by using an existing portable tire test rig to perform force and moment tests on a passenger tire driven on these surfaces. The off-road tire model was then used as a basis for developing control algorithms to prevent vehicle rollover on off-road terrain. Specifically, a direct yaw control (DYC) algorithm based on Lyapunov direct method and an emergency roll control (ERC) algorithm based on a rollover coefficient were developed. Emergency evasive maneuvers were performed in a simulation environment on the studied vehicle driven on dry asphalt, dirt, and gravel for the controlled and uncontrolled cases. Results show that the proposed control algorithms significantly improve vehicle stability and prevent rollover on a variety of driving surfaces. / Master of Science

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Quadrupedal robots play a vital role in various applications, from search and rescue operations to exploration in challenging terrains. However, locomotion tasks involving unknown payload transportation on rough terrains pose significant challenges, requiring adaptive control strategies to ensure stability and performance. This dissertation contributes to the advancement of adaptive motion planning and control solutions that enable quadrupedal robots to traverse unknown rough environments while tasked with transporting unknown payloads. In the first project, a novel hierarchical planning and control framework for robust payload transportation by quadrupedal robots is developed. This framework integrates an adaptive model predictive control (AMPC) algorithm with a gradient-descent-based adaptive updating law applied to reduced-order locomotion (i.e., template) models. At the high level of the control hierarchy, an indirect adaptive law estimates unknown parameters of the reduced-order locomotion model under varying payloads, ensuring stability during trajectory planning. The optimal trajectories generated by the AMPC are then passed to a low-level and full-order nonlinear whole-body controller (WBC) for tracking. Extensive numerical investigations and hardware experiments on the A1 quadru[pedal robot validate the framework's capabilities, showcasing significant improvements in payload transportation on both flat and rough terrains compared to conventional MPC strategies. Specifically, the robot demonstrates proficiency in transporting unmodeled, unknown static payloads up to 109% of its own mass in experiments on flat terrains and 91% on rough experimental terrains. Moreover, the robot successfully manages dynamic payloads with 73% of its mass on rough terrains. Adaptive controllers must also address external disturbances inherent in real-world environments. Therefore, the second project introduces a hierarchical planning and control scheme with an adaptive L1 nonlinear model predictive control (ANMPC) at the high level, which integrates nonlinear MPC (NMPC) with an L1 adaptive controller. The prescribed optimal state and control input profiles generated by the ANMPC are then fed to the low-level nonlinear WBC. This approach aims to stabilize locomotion gaits in the presence of parametric uncertainties and external disturbances. The proposed controller is analyzed to accommodate uncertainties and external disturbances. Comprehensive numerical simulations and experimental validations on the A1 quadrupedal robot demonstrate its effectiveness on rough terrains. Numerical results suggest that ANMPC significantly improves the stability of the gaits in the presence of uncertainties and external disturbances compared to NMPC and AMPC. The robot can carry payloads up to 109% of its own mass on its trunk on flat and rough terrains. Simulation results show that the robot achieves a maximum payload capacity of 26.3 (kg), which is equivalent to 211% of its own mass on rough terrains with uncertainties and disturbances. / Doctor of Philosophy / In the rapidly advancing domain of robotics, there is a growing demand for intelligent robotic systems capable of adeptly addressing novel and unforeseen scenarios, such as uneven paths or external forces applied to the robots, like kicks and hits. This necessitates robots with the capability to handle diverse tasks with precision, particularly in the domains of object transportation and navigation through unknown terrains in applications such as search and rescue operations or cargo handling. This dissertation introduces innovative motion planning and control frameworks designed to imbue robots with adaptive capabilities, enabling them to adapt to real-world unanticipated scenarios and uncertainties during their movement, particularly when carrying unknown payloads. In the first project, a new framework is developed to enhance payload transportation by quadrupedal robots. This framework integrates an adaptive model predictive control (AMPC) algorithm with a gradient-descent-based adaptive updating law. Through extensive experiments and simulations, the framework shows remarkable improvements in payload transportation on both flat and rough terrains. The robot successfully transports payloads exceeding its own mass by up to 109% on flat terrains and 91% on rough terrains. Recognizing the need to address uncertainties in real-world environments, the second project introduces a hierarchical planning and control scheme with adaptive L1 nonlinear model predictive control (ANMPC). This approach stabilizes legged locomotion in the presence of uncertainties and disturbances. Results demonstrate that ANMPC significantly improves gait stability compared to existing methods. The robot achieves a payload capacity of up to 109% of its own mass on both experimental flat and rough terrains and reaches a maximum of 26.3 kg (around 212% of its own mass) on rough terrain simulations with uncertainties and disturbances.