Domagoj Tolić

Event-triggered scheduling for stochastic multi-loop networked control systems with packet dropouts

In this paper, we study event-triggered data scheduling for stochastic multi-loop control systems communicating over a shared network with communication uncertainties. We introduce a novel dynamic scheduling scheme which allocates the channel access according to an error-dependent policy. The proposed scheduler deterministically excludes subsystems with lower error values from the medium access competition in favor of those with larger errors. Subsequently, the scheduler probabilistically allocates the communication resource to the eligible entities. We model the overall network-induced error as a homogeneous Markov chain and show its boundedness in expectation over a multi time-step horizon. In addition, analytical upper bound for the associated average cost is derived. Furthermore, we show that our proposed policy is robust against packet dropouts. Numerical results demonstrate a significant performance improvement in terms of error level in comparison with periodic and random scheduling policies.

A geometric optimization approach to tracking maneuvering targets using a heterogeneous mobile sensor network

A methodology is developed to deploy a mobile sensor network for the purpose of detecting and capturing mobile targets in the plane. The mobile sensor network consists of a set of heterogeneous robotic sensors modeled as hybrid systems with individual processing capabilities. The targets are modeled by a Markov motion process that is commonly used in target tracking applications. Since the sensors are installed on mobile robots and have limited range, the geometry of their platforms and fields-of-view play a critical role in motion planning and obstacle avoidance. The methodology presented in this paper uses line transversals and cell decomposition in order to compute sensing and pursuit strategies that maximize the probability of detection, while minimizing energy consumption. The approach is demonstrated through progressive simulation scenarios involving multiple sensors installed on UGVs and UAVs, that are characterized by different sensing and motion capabilities, but are deployed to cooperatively detect, track, and pursue the same set of maneuvering targets.

Optimal self-triggering for nonlinear systems via Approximate Dynamic Programming

In this paper we investigate optimal intermittent feedback for nonlinear control systems. Using the currently available measurements from a plant, we develop a methodology that outputs when to update the controller with new measurements such that a given cost function is minimized. Our cost function captures trade-offs between the performance and energy consumption of the control system. The optimization problem is formulated as a Dynamic Programming problem, and Approximate Dynamic Programming is employed to solve it. Instead of advocating a particular approximation architecture for Approximate Dynamic Programming, we formulate properties that successful approximation architectures satisfy. In addition, we consider problems with partially observable states, and propose Particle Filtering to deal with partially observable states and intermittent feedback. Finally, our approach is applied to a mobile robot trajectory tracking problem.

Self-triggering in nonlinear systems: A small gain theorem approach

This paper investigates stability of nonlinear control systems under intermittent information. Building on the small-gain theorem, we develop self-triggered control yielding stable closed-loop systems. We take the violation of the small-gain condition to be the triggering event, and develop a sampling policy that precludes this event by executing the control law with up-to-date information. Based on the properties of the external inputs to the plant, the developed sampling policy yields regular stability, asymptotic stability and Lp-stability. Control loops are modeled as interconnections of hybrid systems, and novel results on Lp-stability of hybrid systems are presented. Prediction of the triggering event is achieved by employing Lp-gains over a finite horizon. In addition, Lp-gains over a finite horizon produce larger intersampling intervals when compared with standard Lp-gains. Furthermore, a novel approach for calculation of Lp-gains over a finite horizon is devised. Finally, our approach is successfully applied to a trajectory tracking control system.

Decentralized output synchronization of heterogeneous linear systems with fixed and switching topology via self-triggered communication

In this paper, we investigate a decentralized output synchronization problem of heterogeneous linear systems. Motivated by recent results in the literature, we develop a self-triggered output broadcasting policy for the interconnected systems. In other words, each system broadcasts its outputs only when necessary in order to achieve output synchronization. Consequently, the control signal of each system is updated based on currently available (but outdated) information received from the neighbors. These broadcasting time instants adapt to the current communication topology. For a fixed topology, our broadcasting policy yields global exponential output synchronization, and Lp-stable output synchronization in the presence of disturbances. Employing a converse Lyapunov theorem for impulsive systems, we provide an average dwell-time condition that yields disturbance-to-state stable output synchronization in case of switching topology. The proposed approach is applicable to directed and unbalanced communication topologies. Finally, our results are corroborated by numerical simulations.

Stabilizing Transmission Intervals for Nonlinear Delayed Networked Control Systems

In this note, we consider a nonlinear process with delayed dynamics to be controlled over a communication network in the presence of disturbances and study robustness of the resulting closed-loop system with respect to network-induced phenomena such as sampled, distorted, delayed and lossy data as well as scheduling protocols. For given plant-controller dynamics and communication network properties (e.g., propagation delays and scheduling protocols), we quantify the control performance level (in terms of Lp-gains) as the transmission interval varies. Maximally Allowable Transfer Interval (MATI) labels the greatest transmission interval for which a prespecified Lp-gain is attained. The proposed methodology combines impulsive delayed system modeling with Lyapunov-Razumikhin techniques to allow for MATIs that are smaller than the communication delays. Other salient features of our methodology are the consideration of variable delays, corrupted data and employment of model-based estimators to prolong MATIs. The present stability results are provided for the class of Uniformly Globally Exponentially Stable (UGES) scheduling protocols. The well-known Round Robin (RR) and Try-Once-Discard (TOD) protocols are examples of UGES protocols. Finally, a nonlinear example is provided to demonstrate the benefits of the proposed approach.

Decentralized event-based scheduling for shared-resource Networked Control Systems

In this paper, we introduce a decentralized event-triggered scheduling scheme for multi-loop Networked Control Systems (NCSs) in which the individual control loops are coupled through a shared communication channel. The proposed scheduling design combines deterministic and probabilistic attributes to efficiently allocate the limited communication resource among the control loops in an event-based fashion. Based on local error thresholds, each control loop determines whether to compete for the channel access. As a result, control loops with higher transmission priorities are more likely to utilize the channel, which in turn leads to more efficient usage of the limited resource. Each eligible subsystem then attempts to transmit at times specified by local waiting times, which are randomly distributed and local-error dependent. In this manner, the probability of data collisions in the communication channel is reduced. If the channel capacity is reached at some time instants, data packets are dropped. We demonstrate stochastic stability of such NCSs in terms of Lyapunov Stability in Probability (LSP). The numerical results show that our approach improves resource utilization and reduces the networked-induced error variance in comparison with time-triggered, random access, and centralized scheduling policies.

Stabilizing transmission intervals and delays for nonlinear Networked Control Systems: The large delay case

This paper proposes a methodology for computing Maximally Allowable Transfer Intervals (MATIs) that provably stabilize nonlinear Networked Control Systems (NCSs) in the presence of disturbances and signal delays. Accordingly, given a desired level of system performance (in terms of ℒp-gains), quantitative MATI vs. delay trade-offs are obtained. By combining impulsive delayed system modeling with Lyapunov-Razumikhin type of arguments, we are able to consider even the so-called large delays. Namely, the computed MATIs can be smaller than delays existent in NCSs. In addition, our stability results are provided for the class of Uniformly Globally Exponentially Stable (UGES) scheduling protocols. The well-known Round Robin (RR) and Transmit-Once-Discard (TOD) protocols are examples of UGES protocols. Apart from the inclusion of large delays, another salient feature of our methodology is the consideration of corrupted data. To that end, we propose the notion of ℒp-stability with bias. Furthermore, the Zeno-free property of our methodology is demonstrated. Finally, a comparison with the state-of-the-art work is provided utilizing the benchmark problem of batch reactor.

Robust event-based data scheduling for resource constrained Networked Control Systems

This paper modifies a recently proposed event-based probabilistic medium access for multi-loop Networked Control Systems (NCSs) over a shared communication channel subject to limited capacity and uncertainties and study its robustness. The novel design combines deterministic and probabilistic attributes to efficiently allocate the channel access among the control loops in the presence of network-induced phenomena such as packet dropouts and scheduling with delayed information update. Since the scheduler receives error information from a number of systems simultaneously, this sheer amount of information cannot always be processed in timely manner, which in turn gives rise to delays. Given the local error thresholds, the subsystems with error values lower than their corresponding thresholds are deterministically excluded from the medium access competition in favor of those with larger errors. In case of resource scarcity, the scheduler probabilistically allocates the channel to those that exceed the local thresholds according to an error-dependent priority measure. We show stochastic stability of such NCSs in terms of f-ergodicity of the network-induced error, which is modeled as a Markov chain. Numerical results validate our stability results in the presence of packet dropouts and delayed data update.

Consensus-based decentralized resource sharing between co-located Wireless Sensor Networks

With the emergence of various co-located Wireless Sensor Networks (WSNs) in applications such as smart buildings and smart cities, it becomes increasingly important to facilitate their interaction. The aim of inter-network collaboration is to leverage the performance of each WSN, as well as the quality of service of the overall system. In this paper, we propose a decentralized method for inter-network collaboration, where each node is able to initiate a consensus-reaching procedure among the nodes from its network and terminate it after the consensus is reached. After that, it exchanges the information with a node from a neighboring network. In order not to interfere with the main task of the WSNs (detecting and reporting interesting events), our consensus algorithm needs to be energy-efficient and fast. We emphasize the importance of our approach on a case study of a smart surveillance application, where a gas WSN and video WSN share the same physical environment. The performance of the consensus algorithm during intranetwork communication (prior to inter-network communication) is experimentally investigated, using off-the-shelf wireless sensor platforms. We introduce an energy-time factor that indicates the optimal communication rate in terms of energy- and time-efficiency of the consensus algorithm. In our experiments, the best performance is achieved for period of communication 0.1 s (i.e., 2% duty cycle).