Supervisory control

About: Supervisory control is a research topic. Over the lifetime, 7128 publications have been published within this topic receiving 129025 citations.

Vehicle Propulsion Systems: Introduction to Modeling and Optimization

Abstract: Introduction.- Vehicle Energy and Fuel Consumption - Basic Concepts.- IC-Engine-Based Propulsion Systems.- Models of Fuel-Cell Propulsion Systems.- Supervisory Control Algorithms.- Appendix I - Case Studies.- Appendix II - Optimal Control Theory.

Trust, self-confidence, and operators' adaptation to automation

Abstract: The increasing use of automation to supplant human intervention in controlling complex systems changes the operators' role from active controllers (directly involved with the system) to supervisory controllers (managing the use of different degrees of automatic and manual control). This paper examines the relationship between trust in automatic controllers, self-confidence in manual control abilities, and the use of automatic controllers in operating a simulated semi-automatic pasteurization plant. Trust, combined with self-confidence, predicted the operators' allocation strategy. A Multitrait-multimethod matrix and logit functions showed how trust and self-confidence relate to the use of automation. An ARMAV time series model of the dynamic interaction of trust and self-confidence, combined with individual biases, accounted for 60.9-86.5% of the variance in the use of the three automatic controllers. In general, automation is used when trust exceeds self-confidence, and manual control when the opposite is true. Since trust and self-confidence are two factors that guide operators' interactions with automation, the design of supervisory control systems should include provisions to ensure that operators' trust reflects the capabilities of the automation and operators' self-confidence reflects their abilities to control the system manually.

Trajectory-Tracking and Path-Following of Underactuated Autonomous Vehicles With Parametric Modeling Uncertainty

Abstract: We address the problem of position trajectory-tracking and path-following control design for underactuated autonomous vehicles in the presence of possibly large modeling parametric uncertainty. For a general class of vehicles moving in either 2- or 3-D space, we demonstrate how adaptive switching supervisory control can be combined with a nonlinear Lyapunov-based tracking control law to solve the problem of global boundedness and convergence of the position tracking error to a neighborhood of the origin that can be made arbitrarily small. The desired trajectory does not need to be of a particular type (e.g., trimming trajectories) and can be any sufficiently smooth bounded curve parameterized by time. We also show how these results can be applied to solve the path-following problem, in which the vehicle is required to converge to and follow a path, without a specific temporal specification. We illustrate our design procedures through two vehicle control applications: a hovercraft (moving on a planar surface) and an underwater vehicle (moving in 3-D space). Simulations results are presented and discussed.

On observability of discrete-event systems

Abstract: The observability of discrete-event systems is investigated. A discrete-event system G is modeled as the controlled generator of a formal language L m ( G ) in the framework of Ramadge and Wonham. To control G, a supervisor S is developed whose action is to enable and disable the controllable events of G according to a record of occurrences of the observable events of G, in such a way that the resulting closed-loop system obeys some prespecified operating rules embodied in a given language K. A necessary and sufficient condition is found for the existence of a supervisor S such that L m ( S/G ) = K . Based on this condition, a solution of the supervisory control and observation problem (SCOP) is obtained. Two examples are provided.

Trust in automation. I: Theoretical issues in the study of trust and human intervention in automated systems

Abstract: Today many systems are highly automated. The human operator's role in these systems is to supervise the automation and intervene to take manual control when necessary. The operator's choice of automatic or manual control has important consequences for system performance, and therefore it is important to understand and optimize this decision process. One important determinant of operators' choice of manual or automatic control may be their degree of trust in the automation. However, there have been no experimental tests of this hypothesis until recently, nor is there a model of human trust in machines to form a theoretical foundation for empirical studies. In this paper a model of human trust in machines is developed, taking models of trust between people as a starting point, and extending them to the human-machine relationship. The resulting model defines human trust in machines and specifies how trust changes with experience on a system, providing a framework for experimental research on trust a...