Control logic

Abstract: The state of the practice traffic signal control strategies mainly rely on infrastructure based vehicle detector data as the input for the control logic. The infrastructure based detectors are generally point detectors which cannot directly provide measurement of vehicle location and speed. With the advances in wireless communication technology, vehicles are able to communicate with each other and with the infrastructure in the emerging connected vehicle system. Data collected from connected vehicles provides a much more complete picture of the traffic states near an intersection and can be utilized for signal control. This paper presents a real-time adaptive signal phase allocation algorithm using connected vehicle data. The proposed algorithm optimizes the phase sequence and duration by solving a two-level optimization problem. Two objective functions are considered: minimization of total vehicle delay and minimization of queue length. Due to the low penetration rate of the connected vehicles, an algorithm that estimates the states of unequipped vehicle based on connected vehicle data is developed to construct a complete arrival table for the phase allocation algorithm. A real-world intersection is modeled in VISSIM to validate the algorithms. Results with a variety of connected vehicle market penetration rates and demand levels are compared to well-tuned fully actuated control. In general, the proposed control algorithm outperforms actuated control by reducing total delay by as much as 16.33% in a high penetration rate case and similar delay in a low penetration rate case. Different objective functions result in different behaviors of signal timing. The minimization of total vehicle delay usually generates lower total vehicle delay, while minimization of queue length serves all phases in a more balanced way.

Abstract: A learning model predictive controller for iterative tasks is presented. The controller is reference-free and is able to improve its performance by learning from previous iterations. A safe set and a terminal cost function are used in order to guarantee recursive feasibility and nondecreasing performance at each iteration. This paper presents the control design approach, and shows how to recursively construct terminal set and terminal cost from state and input trajectories of previous iterations. Simulation results show the effectiveness of the proposed control logic.

Abstract: The problem of encoding the states of a synchronous finite state machine (FSM) so that the area of a two-level implementation of the combinational logic is minimized is addressed. As in previous approaches, the problem is reduced to the solution of the combinatorial optimization problems defined by the translation of the cover obtained by a multiple-valued logic minimization or by a symbolic minimization into a compatible Boolean representation. The authors present algorithms for this solution, based on a novel theoretical framework that offers advantages over previous approaches to develop effective heuristics. The algorithms are part of NOVA, a program for optimal encoding of control logic. Final areas averaging 20% less than other state assignment programs and 30% less than the best random solution have been obtained. Literal counts averaging 30% less than the best random solutions have been obtained. >

Abstract: In one aspect, a control circuit to control a speed of a motor includes a control logic circuit connected to a multifunction port. The control logic circuit is configured to receive a control signal provided at the multifunction port and to provide response signals based on the control signal to place the motor in at least two of a sleep mode, a brake mode and a pulse-width modulation (PWM) mode. The motor control circuit also includes an H-bridge circuit configured to control the motor based on the response signals.