Human sensorimotor control is remarkably adept at utilising contextual information to learn and recall systematic sensorimotor transformations. Here, we investigate the motor representations that underlie such learning, and examine how motor memories acquired based on different contextual information interact. Using a novel three-dimensional robotic manipulandum, the 3BOT, we examined the spatial transfer of learning across various movement directions in a 3D environment, while human subjects performed reaching movements under velocity-dependent force field. The obtained pattern of generalisation suggested that the representation of dynamic learning was most likely defined in a target-based, rather than an extrinsic, coordinate system. We further examined how motor memories interact when subjects adapt to force fields applied in orthogonal dimensions. We found that, unlike opposing fields, learning two spatially orthogonal force fields led to the formation of separate motor memories, which neither interfered with nor facilitated each other. Moreover, we demonstrated a novel, more general aspect of the spontaneous recovery phenomenon using a two-dimensional force field task: when subjects learned two orthogonal force fields consecutively, in the following phase of clamped error feedback, the expression of adaptation spontaneously rotated from the direction of the second force field, towards the direction of the first force field. Finally, we examined the interaction of sensorimotor memories formed based on separate contextual information. Subjects performed reciprocating reaching and object manipulation tasks under two alternating contexts (movement directions), while we manipulated the dynamics of the task in each context separately. The results suggested that separate motor memories were formed for the dynamics of the task in different contexts, and that these motor memories interacted by sharing error signals to enhance learning. Importantly, the extent of interaction was not fixed between the context-dependent motor memories, but adaptively changed according to the task dynamics to potentially improve overall performance. Together, our experimental and theoretical results add to the understanding of mechanisms that underlie sensorimotor learning, and the way these mechanisms interact under various tasks and different dynamics.

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.

The optimal steady-state control, and suboptimal adaptive control, of disturbed single-input-output systems are introduced, and the class of systems considered is defined. It is noted that the stochastic tracking problem divides into a deterministic tracking problem and a stochastic regulator problem; the solutions to these two problems are shown to be independent but formally similar. The continuous regulator problem is approached via both frequency and time domain methods: the former method is extended to cover unstable systems; the latter method is extended to include systems with input delay. The two regulators are shown to be externally equivalent. The frequency domain method is briefly described for discrete systems, and shown to include the minimum variance regulator of Åström and Peterka as a special case. Some systems which allow measurement noise to be treated as a system disturbance for the purposes of optimal controller design are investigated. A novel class of control laws is described in both continuous and discrete time; in the same way as the minimum variance regulator forms the basis of the self-tuning regulator of Åström and Wittenmark, these minimum variance controllers from the basis of a self-tuning controller. These minimum variance controllers have a number of advantages over the minimum-variance regulator, and are open to a number of interpretations including: a model following control law, and an extension of classical control laws to systems with delay. The optimality of this class of control laws is investigated, and analogies drawn with the previously considered k-step-ahead control laws; some examples are given to illustrate the method. An adaptive control law combining the above minimum variance controllers with a linear least-squares algorithm is proposed and shown to be self-tuning. These self-tuning controllers are only slightly more complex than the self-tuning regulator of Åström and Wittenmark, but have a number of advantages. Intuitive justification is given for the conjecture that some methods of Ljung, developed for the analysis of the self-tuning regulator, are applicable to the self-tuning controller. Simulated examples are given which compare and contrast the performance of the self-tuning controller with that of the self-tuning regulator. The first steps towards a quasi-continuous self-tuning controller are outlined.

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This study presents comprehensive active cancellation of booming noise caused by the engine and the driveline inside a passenger car. In modern noise control systems for vehicles, booming noise caused by engine harmonics could be effectively suppressed by employing active noise control. However, practical attempts or studies for the active suppression of driveline booming noise are scarce. One of the reasons may be that since the booming noise caused by the driveline is not harmonic with the engine speed, reference signals cannot be generated conventionally. Thus, passive approaches are generally employed to improve the driveline noise. To address this limitation, we propose a method for generating reference signals from engine revolution speed to suppress the driveline noise, such as propeller shaft and tire noise. Reference signals for driveline noise suppression were generated using the information from the torque converter, gear ratio, and final drive ratio. A practical active noise control system was implemented in a six-cylindered large sedan to validate the proposed method. The experimental results showed that the engine firing order was suppressed by 8.0 dB. Moreover, the first order of the propeller shaft and the second and third orders of the tires were suppressed by 5.5 dB, 3.9 dB, and 2.3 dB for entire seat positions. Furthermore, the results presented in this study were considered effective for improving annoyance perception through subjective evaluation.