Michael D. Lemmon

Event-Triggering in Distributed Networked Control Systems

This paper examines event-triggered data transmission in distributed networked control systems with packet loss and transmission delays. We propose a distributed event-triggering scheme, where a subsystem broadcasts its state information to its neighbors only when the subsystems local state error exceeds a specified threshold. In this scheme, a subsystem is able to make broadcast decisions using its locally sampled data. It can also locally predict the maximal allowable number of successive data dropouts (MANSD) and the state-based deadlines for transmission delays. Moreover, the designers selection of the local event for a subsystem only requires information on that individual subsystem. Our analysis applies to both linear and nonlinear subsystems. Designing local events for a nonlinear subsystem requires us to find a controller that ensures that subsystem to be input-to-state stable. For linear subsystems, the design problem becomes a linear matrix inequality feasibility problem. With the assumption that the number of each subsystems successive data dropouts is less than its MANSD, we show that if the transmission delays are zero, the resulting system is finite-gain Lp stable. If the delays are bounded by given deadlines, the system is asymptotically stable. We also show that those state-based deadlines for transmission delays are always greater than a positive constant.

Self-Triggered Feedback Control Systems With Finite-Gain

This paper examines a class of real-time control systems in which each control task triggers its next release based on the value of the last sampled state. Prior work used simulations to demonstrate that self-triggered control systems can be remarkably robust to task delay. This paper derives bounds on a tasks sampling period and deadline to quantify how robust the control systems performance will be to variations in these parameters. In particular we establish inequality constraints on a control tasks period and deadline whose satisfaction ensures that the closed-loop systems induced L 2 gain lies below a specified performance threshold. The results apply to linear time-invariant systems driven by external disturbances whose magnitude is bounded by a linear function of the system states norm. The plant is regulated by a full-information H infin controller. These results can serve as the basis for the design of soft real-time systems that guarantee closed-loop control system performance at levels traditionally seen in hard real-time systems.

{\cal L}_{2}

In this paper, we combine inertial sensing and sensor network technology to create a pedestrian dead reckoning system. The core of the system is a lightweight sensor-and-wireless-embedded device called NavMote that is carried by a pedestrian. The NavMote gathers information about pedestrian motion from an integrated magnetic compass and accelerometers. When the NavMote comes within range of a sensor network (composed of NetMotes), it downloads the compressed data to the network. The network relays the data via a RelayMote to an information center where the data are processed into an estimate of the pedestrian trajectory based on a dead reckoning algorithm. System details including the NavMote hardware/software, sensor network middleware services, and the dead reckoning algorithm are provided. In particular, simple but effective step detection and step length estimation methods are implemented in order to reduce computation, memory, and communication requirements on the Motes. Static and dynamic calibrations of the compass data are crucial to compensate the heading errors. The dead reckoning performance is further enhanced by wireless telemetry and map matching. Extensive testing results show that satisfactory tracking performance with relatively long operational time is achieved. The paper also serves as a brief survey on pedestrian navigation systems, sensors, and techniques.

Stability

In this paper, the supervisory control of hybrid systems is introduced and discussed at length. Such control systems typically arise in the computer control of continuous processes, for example, in manufacturing and chemical processes, in transportation systems, and in communication networks. A functional architecture of hybrid control systems consisting of a continuous plant, a discrete-event controller, and an interface is used to introduce and describe analysis and synthesis concepts and approaches. Our approach highlights the interaction between the continuous and discrete dynamics, which is the cornerstone of any hybrid system study. Discrete abstractions are used to approximate the continuous plant. Properties of the discrete abstractions to be appropriate representations of the continuous plant are presented, and important concepts such as determinism and controllability are discussed. Supervisory control design methodologies are presented to satisfy control specifications described by formal languages. Several examples are used throughout the paper to illustrate our approach.

Design of a wireless assisted pedestrian dead reckoning system - the NavMote experience

Hybrid control systems contain two distinct types of systems, continuous state and discrete-state, that interact with each other. Their study is essential in designing sequential supervisory controllers for continuous-state systems, and it is central in designing control systems with high degree of autonomy.

Supervisory control of hybrid systems

This paper describes a method for constructing a Petri net feedback controller for a discrete event system modeled by a Petri net. The controller enforces a set of linear constraints on the plant and consists of places and arcs. It is computed using the concept of Petri net place invariants. The size of the controller is proportional to the number of constraints which must be satisfied. The method is very attractive computationally, and it makes possible the systematic design of Petri net controllers for complex industrial systems.<<ETX>>

Hybrid System Modeling and Autonomous Control Systems

Networked control systems often send information across the communication network in a periodic manner. The selected period, however, must assure adequate system performance over a wide range of operating conditions and this conservative’ choice may result in significant over-provisioning of the communication network. This observation has motivated the use of sporadic transmission across the network’s feedback channels. Event-triggering represents one way of generating such sporadic transmissions. In event-triggered feedback, a sensor transmits when some internal measure of the novelty in the sensor information exceeds a specified threshold. In particular, this means that when the gap between the current and the more recently transmitted sensor measurements exceeds a state-dependent threshold, then the information is transmitted across the channel. The state-dependent thresholds are chosen in a way that preserves commonly used stability concepts such as input-to-state stability or \({\mathcal L}_2\) stability. This approach for threshold selection therefore provides a systematic way of triggering transmissions that provides some guarantees on overall control system performance. While early work in event-triggering focused on control applications, this technique can also be used in distributed estimation and distributed optimization. This chapter reviews recent progress in the use of state-dependent event-triggering in embedded control, networked control systems, distributed estimation, and distributed optimization.

Feedback control of Petri nets based on place invariants

This paper derives the power spectral density (PSD) of the output generated by a discrete-time feedback control system in which feedback measurements are dropped with a known probability, /spl epsi/. This class of systems is a model for soft real-time control systems in which the feedback path is implemented on a nondeterministic computer network. The PSD computed in this paper is a function of the dropout probability. The dropout probability is taken as a measure of the network quality of service (QoS). Based on the derived PSD, the power semi-norm of the output is predicted. So a direct way of linking control system performance(as measured by the power semi-norm of the output) to the networks QoS (as measured by the dropout probability) is provided.

Event-Triggered Feedback in Control, Estimation, and Optimization

This paper examines optimal compensation for dropped feedback measurements in a networked control system. A common policy for handling such lost data is to simply use the past data. This case was treated in. This paper extends that prior work to cover a more general class of dropout compensation. The papers principal result shows that determining the optimal dropout compensator can be posed as a constrained generalized regulator problem. An example compares the performance of a networked control system using the optimal dropout compensator against more commonly used heuristic dropout policies. The comparison shows that the optimal compensator works better than these heuristic policies.

Robust performance of soft real-time networked control systems with data dropouts

This paper studies the event design in event-triggered feedback systems with asymptotic stability. A new event-triggering scheme is presented that may postpone the occurrence of events over previously proposed methods. Our approach pertains to nonlinear state-feedback systems. The resulting event-triggered feedback systems are guaranteed to be asymptotically stable, provided that the continuous systems are stabilizable. We also show that the task periods and deadlines generated by our scheme are bounded strictly away from zero if the continuous systems are input-to-state stable with respect to measurement errors. Simulation results indicate that our event-triggered scheme has a much larger average period compared with the prior work. Moreover, our scheme also appears to be robust to task delays.