Feedback Control Design and Stability Analysis of One Dimensional Evacuation System

This paper presents design of nonlinear feedback controllers for two different models representing evacuation dynamics in one dimension. The models presented here are based on the laws of conservation of mass and momentum. The first model is the classical one equation model for a traffic flow based on conservation of mass with a prescribed relationship between density and velocity. The other model is a two equation model in which the velocity is independent of the density. This model is based on conservation of mass and momentum. The equations of motion in both cases are described by nonlinear partial differential equations. We address the feedback control problem for both models. The objective is to synthesize a nonlinear distributed feedback controller that guarantees stability of a closed loop system. The problem of control and stability is formulated directly in the framework of partial differential equations. Sufficient conditions for Lyapunov stability for distributed control are derived

Feedback Control of Macroscopic Crowd Dynamic Models

This paper presents design of nonlinear feedback controllers for two different macroscopic models for two- dimensional pedestrian dynamics. The models presented here are based on the laws of conservation of mass and momentum. These models have been developed by extending one-dimension macroscopic vehicle traffic flow models that use two-coupled partial deferential equations (PDEs). These models modify the vehicle traffic models so that bi-directional controlled flow is possible. Both models satisfy the conservation principle and are classified as nonlinear, time-dependent, hyperbolic PDE systems. The equations of motion in both cases are described by nonlinear partial differential equations. We address the feedback control problem for both models in the framework of partial differential equations. The objective is to synthesize nonlinear distributed feedback controllers that guarantee stability of a closed loop system

Stochastic Learning Feedback Hybrid Automata for Dynamic Power Management in Embedded Systems

Dynamic power management (DPM) refers to the strategies employed at system level to reduce energy expenditure (i.e. to prolong battery life) in embedded systems. The trade-off involved in DPM techniques is between the reductions of energy consumption and latency suffered by the tasks. Such trade-offs need to be decided at runtime, making DPM an on-line problem. We formulate DPM as a hybrid automaton control problem and integrate stochastic control. The control strategy is learnt dynamically using stochastic learning hybrid automata (SLHA) with feedback learning algorithms. Simulation-based experiments show the expediency of the feedback systems in stationary environments. Further experiments reveal that SLHA attains better trade-offs than several former predictive algorithms under certain trace data

Intelligent Control of Vehicles: Preliminary Results on the Application of Learning Automata Techniques to Automated Highway System

We suggest an intelligent controller for an automated vehicle to plan its own trajectory based on sensor and communication data received. Our intelligent controller is based on an artificial intelligence technique called learning stochastic automata. The automaton can learn the best possible action to avoid collisions using the data received from on-board sensors. The system has the advantage of being able to work in unmodeled stochastic environments. Simulations for the lateral control of a vehicle using this AI method provides encouraging results

Simulation Study of Learning Automata Games in Automated Highway Systems

One of the most important issues in Automated Highway System (AHS) deployment is intelligent vehicle control. While the technology to safely maneuver vehicles exists, the problem of making intelligent decisions to improve a single vehicle’s travel time and safety while optimizing the overall traffic flow is still a stumbling block. We propose an artificial intelligence technique called stochastic learning automata to design an intelligent vehicle path controller. Using the information obtained by on-board sensors and local communication modules, two automata are capable of learning the best possible (lateral and longitudinal) actions to avoid collisions. This learning method is capable of adapting to the automata environment resulting from an unmodeled physical environment. Although the learning approach taken is capable of providing a safe decision, optimization of the overall traffic flow is required. This is achieved by studying the interaction of the vehicles. The design of the adaptive vehicle path planner based on local information is extended to the interaction of multiple intelligent vehicles. By analyzing the situations consisting of conflicting desired vehicle paths, we extend our design by additional decision structures. The analysis of the situations and the design of the additional structures are made possible by treatment of the interacting reward-penalty mechanisms in individual vehicles as automata games. The definition of the physical environment of a vehicle as a series of discrete state transitions associated with a “stationary automata environment” is the key to this analysis and to the design of the intelligent vehicle path controller

Multiple Stochastic Learning Automata for Vehicle Path Control in an Automated Highway System

This paper suggests an intelligent controller for an automated vehicle planning its own trajectory based on sensor and communication data. The intelligent controller is designed using the learning stochastic automata theory. Using the data received from on-board sensors, two automata (one for lateral actions, one for longitudinal actions) can learn the best possible action to avoid collisions. The system has the advantage of being able to work in unmodeled stochastic environments, unlike adaptive control methods or expert systems. Simulations for simultaneous lateral and longitudinal control of a vehicle provide encouraging result

Pedestrian Dynamics: Feedback Control of Crowd Evacuation

Effective evacuation of people from closed spaces is an extremely important topic, since it can save real lives in emergency situations that can be brought about by natural and human made disasters. Usually there are static maps posted at various places at buildings that illustrate routes that should be taken during emergencies. However, when disasters happen, some of these routes might not be valid because of structural problems due to the disaster itself and more importantly because of the distribution of congestion of people spread over the area. The average flow of traffic depends on the traffic density. Therefore, if all the people follow the same route, or follow a route without knowing the congestion situation, they can end up being part of the congestion which results in very low flow rate or worse a traffic jam. Hence it becomes extremely important to design evacuations that inform people how fast and in which direction to move based on real-time information obtained about the people distribution using various sensors. The sensors used can include cameras, infra red sensors etc., and the technology used to inform people about the desired movement can be communicated using light matrix, small speakers, and in the future using wireless PDAs. This book provides mathematical models of pedestrian movements that can be used specifically for designing feedback control laws for effective evacuation. The book also provides various feedback control laws to accomplish the effective evacuation. The book uses the hydrodynamic hyperbolic PDE macroscopic pedestrian models since they are amenable to feedback control design. The control designs are obtained through different nonlinear techniques including Lyapunov functional techniques, feedback linearization in the distributed model, and some discretized techniques

Dynamic Programming Solution for a Class of Pursuit Evasion Problems: The Herding Problem

A herding dog and sheep problem is studied where the agent “dog” is considered the control action for moving the agent “sheep” to a fixed location using the dynamics of their interaction. The problem is solved for the deterministic case using dynamic programming. Proofs are provided for the correctness of the algorithms. The algorithm is analyzed for its complexity. A software package developed for experimentation is described

Stochastic Learning Feedback Hybrid Automata for Power Management in Embedded Systems

In this paper we show that stochastic learning automata based feedback control switching strategy can be used for dynamic power management (DPM) employed at the system level. DPM strategies are usually incorporated at the operating systems of embedded devices to exploit multiple power states available in today\u27s ACPI compliant devices. The idea is to switch between power states depending on the device usage, and since device usage times are not deterministic, probabilistic techniques are often used to create stochastic strategies, or strategies that make decisions based on probabilities of device usage spans. Previous work (Irani et al., 2001) has shown how to approximate the probability distribution of device idle times and dynamically update them, and then use such knowledge in controlling power states. Here, we use stochastic learning automata (SLA) which interacts with the environment to update such probabilities, and then apply techniques similar to (Irani et al., 2001) to optimize power usage with minimal effect on response time for the devices