Zhaojian Li

Robust Control of Networked Systems With Variable Communication Capabilities and Application to a Semi-Active Suspension System

This paper is concerned with a robust control problem of a class of networked systems operated within a multiple communication channels (MCCs) environment. A practical scenario is considered that the active channel in such MCCs for the data communication is switched and the switching is governed by a Markov chain. For each channel, two network-induced imperfections, time delays, and packet dropouts with different characteristics are taken into account. Suppose that the practical plant is subject to energy-bounded disturbance and norm-bounded uncertainties, a robust controller is designed to ensure that the closed-loop system is robustly stable and achieves a disturbance attenuation index against the phenomenon of channel switching. A semi-active suspension system is introduced to illustrate the effectiveness, applicability of the proposed approach, and to demonstrate the advantages of the MCCs scheme within the channel-switching framework.

Cloud aided semi-active suspension control

This paper considers the problem of vehicle suspension control from the perspective of a Vehicle-to-Cloud-to-Vehicle (V2C2V) distributed implementation. A simplified variant of the problem is examined based on the linear quarter-car model of semi-active suspension dynamics. Road disturbance is modeled as a combination of a known road profile, an unmeasured stochastic road profile and potholes. Suspension response when the vehicle hits the pothole is modeled as an impulsive change in wheel velocity with magnitude linked to physical characteristics of the pothole and of the vehicle. The problem of selecting the optimal damping mode from a finite set of damping modes is considered, based on road profile data. The information flow and V2C2V implementation are defined based on partitioning the computations and data between the vehicle and the cloud. A simulation example is presented.

Model reduction of A class of Markov jump nonlinear systems with time-varying delays via projection approach

In this paper, the problem of H ∞ model reduction for a class of discrete-time Markov jump nonlinear systems with time-varying delays is investigated. Based on the Lyapunov-Krasovskii functional method, sufficient stability conditions in terms of strict LMIs are derived for the model error system. Projection approach is employed to construct a reduced-order model such that the model error system is asymptotically stable and preserves a guaranteed H ∞ performance. A numerical example is presented to illustrate the effectiveness of the developed theoretical results and to make comparisons among systems with different structures and parameters.

Decentralized fault prognosis of discrete event systems with guaranteed performance bound

We study the problem of decentralized fault prognosis of partially-observed discrete event systems. In order to capture the prognostic performance issue in the prognosis problem, we propose two new criteria: (1) all faults can be predicted K steps ahead; and (2) a fault will occur for sure within M steps once a fault alarm is issued; and we refer to ( M , K ) as the performance bound of the prognostic system. A necessary and sufficient condition for the existence of a decentralized supervisor satisfying these two criteria is provided, which is termed as ( M , K ) -coprognosability. A polynomial-time algorithm for the verification of ( M , K ) -coprognosability is also proposed. Finally, we show that the proposed approach is applicable to both disjunctive and conjunctive architectures. Our results generalize previous work on decentralized fault prognosis.

Distributed State Estimation of Sensor-Network Systems Subject to Markovian Channel Switching With Application to a Chemical Process

This paper addresses a distributed estimator design problem for linear systems deployed over sensor networks within a multiple communication channels (MCCs) framework. A practical scenario is taken into account such that the channel used for communication can be switched and the switching is governed by a Markov chain. With the existence of communicational imperfections and external disturbances, an estimation algorithm is proposed such that the developed distributed estimators are able to give accurate state estimates against the channel switching phenomenon. The distributed estimation framework is applied to a chemical process to illustrate the effectiveness of the proposed methodology and the superiority of the MCCs framework featured by channel switching.

Road Risk Modeling and Cloud-Aided Safety-Based Route Planning

This paper presents a safety-based route planner that exploits vehicle-to-cloud-to-vehicle (V2C2V) connectivity. Time and road risk index (RRI) are considered as metrics to be balanced based on user preference. To evaluate road segment risk, a road and accident database from the highway safety information system is mined with a hybrid neural network model to predict RRI. Real-time factors such as time of day, day of the week, and weather are included as correction factors to the static RRI prediction. With real-time RRI and expected travel time, route planning is formulated as a multiobjective network flow problem and further reduced to a mixed-integer programming problem. A V2C2V implementation of our safety-based route planning approach is proposed to facilitate access to real-time information and computing resources. A real-world case study, route planning through the city of Columbus, Ohio, is presented. Several scenarios illustrate how the “best” route can be adjusted to favor time versus safety metrics.

Cloud aided safety-based route planning.

This paper proposes a novel multi-objective route planning approach within the framework of a Vehicle-to-Cloud-to-Vehicle (V2C2V) architecture. Time and road risk index (RRI) are both considered as metrics. To evaluate road segment risk, an accident database from the Highway Safety Information System (HSIS) is processed to build a comprehensive road risk assessment model. Route planning is formulated as a multi-objective network flow problem and further reduced to a Mixed Integer Programming (MIP) problem. A real-world case study, route planning through the city of Columbus, Ohio, is presented. The Vehicle-to-Cloud-to-Vehicle (V2C2V) based implementation of our safety-based route planning approach is proposed to facilitate access to real-time information and computing resources.

Road Disturbance Estimation and Cloud-Aided Comfort-Based Route Planning

This paper investigates a comfort-based route planner that considers both travel time and ride comfort. We first present a framework of simultaneous road profile estimation and anomaly detection with commonly available vehicle sensors. A jump-diffusion process-based state estimator is developed and used along with a multi-input observer for road profile estimation. The estimation framework is evaluated in an experimental test vehicle and promising performance is demonstrated. Second, three objective comfort metrics are developed based on factors such as travel time, road roughness, road anomaly, and intersection. A comfort-based route planning problem is then formulated with these metrics and an extended Dijkstra’s algorithm is exploited to solve the problem. A cloud-based implementation of our comfort-based route planning approach is proposed to facilitate information access and fast computation. Finally, a real-world case study, comfort-based route planning from Ford Research and Innovation Center, Michigan to Ford Rouge Factory Tour, Michigan, is presented to illustrate the efficacy of the proposed route planning framework.

Reliable Decentralized Fault Prognosis of Discrete-Event Systems

We investigate the problem of reliable decentralized fault prognosis of partially-observed discrete-event systems. In this problem, n local prognosers are deployed to send their local prognostic decisions to a coordinator that calculates the final prognostic decision. However, only k (1≤ k ≤ n) local prognostic decisions are guaranteed to be available to the coordinator due to possible failures or communication losses of at most n - k local prognosers. We propose the notion of k-reliable decentralized prognoser in order to address this reliability issue. A necessary and sufficient condition for the existence of a k-reliable decentralized prognoser, which predicts faults prior to their occurrences, is presented. This condition is termed as k-reliable coprognosability. A polynomial-time algorithm for the verification of k-reliable coprognosability is presented. We also demonstrate how to compute the k-reliable reactive bound prior to any occurrence of faults.

A New Clustering Algorithm for Processing GPS-Based Road Anomaly Reports With a Mahalanobis Distance

This paper considers a new clustering algorithm for processing time-evolving road anomaly reports. Two cluster categories, main and outlier, are defined to deal with outliers as well as to capture the evolving nature of road anomalies. The Mahalanobis distance is exploited to quantify the similarity between a new report and the existing clusters. The clusters are maintained online and the Woodbury matrix inverse lemma is used for their recursive updates. The proposed clustering algorithm can localize isolated anomalies and compress information for densely distributed anomalies. A simulation is presented to demonstrate the efficacy of the proposed algorithm.