Modeling the human as a controller in a multitask environment

Modeling the human as a controller of slowly responding systems with preview is considered. Along with control tasks, discrete noncontrol tasks occur at irregular intervals. In multitask situations such as these, it has been observed that humans tend to apply piecewise constant controls. It is believed that the magnitude of controls and the durations for which they remain constant are dependent directly on the system bandwidth, preview distance, complexity of the trajectory to be followed, and nature of the noncontrol tasks. A simple heuristic model of human control behavior in this situation is presented. The results of a simulation study, whose purpose was determination of the sensitivity of the model to its parameters, are discussed

Optimal control methods for simulating the perception of causality in young infants

There is a growing debate among developmental theorists concerning the perception of causality in young infants. Some theorists advocate a top-down view, e.g., that infants reason about causal events on the basis of intuitive physical principles. Others argue instead for a bottom-up view of infant causal knowledge, in which causal perception emerges from a simple set of associative learning rules. In order to test the limits of the bottom-up view, we propose an optimal control model (OCM) of infant causal perception. OCM is trained to find an optimal pattern of eye movements for maintaining sight of a target object. We first present a series of simulations which illustrate OCM's ability to anticipate the outcome of novel, occluded causal events, and then compare OCM's performance with that of 9-month-old infants. The impications for developmental theory and research are discusse

Path tracking control for inverse problem of vehicle handling dynamics

A path tracking controller based on active disturbance rejection control(ADRC) theory is presented in this paper to solve path tracking problem in inverse vehicle handling dynamics. The basic idea behind the work is to design an active disturbance rejection controller according to yaw rate and lateral displacement during a vehicle travels along a prescribed path to generate an expected trajectory which guarantees minimum clearance to the prescribed path. Aiming at this purpose, using preview follower theory, a linear extended state observer based on lateral displacement is designed. Considering yaw angle of vehicle, a non-linear combination function combined error of lateral displacement as well as error of yaw angle is designed according to monotone bounded hyperbolic of tangent function. Finally, a real vehicle test is executed to verify the rationality of the path tracking controller. At the same time, according to characteristics of pavement file in Carsim, a 3-D virtual pavement model is established and ride comfort simulation of random pavement is carried out in the software model. The results show that the minimum lateral position error of the generated path tracking trajectory can be good indicators of successful solving of the path tracking problem in inverse vehicle handling dynamics for ADRC. More precisely, there is higher calculation accuracy for the algorithm of the ADRC to solve the path tracking problem. The study can help drivers easily identify safe lane-keeping trajectories and area

Knowledge Transfer Between Robots with Similar Dynamics for High-Accuracy Impromptu Trajectory Tracking

In this paper, we propose an online learning approach that enables the inverse dynamics model learned for a source robot to be transferred to a target robot (e.g., from one quadrotor to another quadrotor with different mass or aerodynamic properties). The goal is to leverage knowledge from the source robot such that the target robot achieves high-accuracy trajectory tracking on arbitrary trajectories from the first attempt with minimal data recollection and training. Most existing approaches for multi-robot knowledge transfer are based on post-analysis of datasets collected from both robots. In this work, we study the feasibility of impromptu transfer of models across robots by learning an error prediction module online. In particular, we analytically derive the form of the mapping to be learned by the online module for exact tracking, propose an approach for characterizing similarity between robots, and use these results to analyze the stability of the overall system. The proposed approach is illustrated in simulation and verified experimentally on two different quadrotors performing impromptu trajectory tracking tasks, where the quadrotors are required to accurately track arbitrary hand-drawn trajectories from the first attempt.Comment: European Control Conference (ECC) 201

Discrete-time optimal preview control

There are many situations in which one can preview future reference signals, or future disturbances. Optimal Preview Control is concerned with designing controllers which use this preview to improve closed-loop performance. In this thesis a general preview control problem is presented which includes previewable disturbances, dynamic weighting functions, output feedback and nonpreviewable disturbances. It is then shown how a variety of problems may be cast as special cases of this general problem; of particular interest is the robust preview tracking problem and the problem of disturbance rejection with uncertainty in the previewed signal. . (', The general preview problem is solved in both the Fh and Beo settings. The H2 solution is a relatively straightforward extension ofpreviously known results, however, our contribution is to provide a single framework that may be used as a reference work when tackling a variety of preview problems. We also provide some new analysis concerning the maximum possible reduction in closed-loop H2 norm which accrues from the addition of preview action. / Name of candidate: Title of thesis: I DESCRIPTION OF THESIS Andrew Hazell Discrete-Time Optimal Preview Control The solution to the Hoo problem involves a completely new approach to Hoo preview control, in which the structure of the associated Riccati equation is exploited in order to find an efficient algorithm for computing the optimal controller. The problem tackled here is also more generic than those previously appearing in the literature. The above theory finds obvious applications in the design of controllers for autonomous vehicles, however, a particular class of nonlinearities found in typical vehicle models presents additional problems. The final chapters are concerned with a generic framework for implementing vehicle preview controllers, and also a'case study on preview control of a bicycle.Imperial Users onl

Optimal path-tracking of virtual race-cars using gain-scheduled preview control

In the search for a more capable minimum-lap-time-prediction program, the presence of an alternative solution has been introduced, which requires the development of a high-quality path-tracking controller. Preview Discrete Linear Quadratic Regulator (DLQR) theory has been used to generate optimal tracking control gains for a given car model. The calculation of such gains are performed off-line, reducing the computational burden during simulated tracking trials. A simple car model was used to develop limit-tracking control strategies, first for an understeering and then for an oversteering car, travelling at a constant forward speed. Adaptation in the controller, with respect to front-/rear-lateral-slip ratio, facilitated superior tracking performance over the non-adaptive counterpart in a number of challenging tracking manoeuvres. Once complete, development work was focused on the control of a complex car model. Such a model required an extension to the preview DLQR theory, to allow variable speed, two-channel (x,y) optimal path tracking. Significant benefits were observed when using an adaptive control strategy, firstly scheduling with respect to forward ground speed and then including adaptation with respect to mean front-lateral-slip ratio. A variable weighting strategy was used to suppress oscillations in the tracking controller when operating near the limit of the car. Such a strategy places a higher cost on control effort expenditure, relative to tracking error, as the car approaches the limit of the front axle. Further oscillatory behaviour, due to the presence of lightly-damped eigenmodes, was suppressed by increasing the car’s suspension stiffness and damping parameters. The tracking controller, that has resulted from the work documented by this thesis, has demonstrated high-quality tracking when operating in a number of different scenarios, including lateral limit tracking. Variable speed limit tracking is suggested as the next development step, which will then allow the controller to be implemented in initial learning trials. Successful development of the speed and path optimisers in such trials will complete the development of a novel solution to the minimum lap-time problem