Event-triggering architectures for adaptive control of uncertain dynamical systems

In this dissertation, new approaches are presented for the design and implementation of networked adaptive control systems to reduce the wireless network utilization while guaranteeing system stability in the presence of system uncertainties. Specifically, the design and analysis of state feedback adaptive control systems over wireless networks using event-triggering control theory is first presented. The state feedback adaptive control results are then generalized to the output feedback case for dynamical systems with unmeasurable state vectors. This event-triggering approach is then adopted for large-scale uncertain dynamical systems. In particular, decentralized and distributed adaptive control methodologies are proposed with reduced wireless network utilization with stability guarantees. In addition, for systems in the absence of uncertainties, a new observer-free output feedback cooperative control architecture is developed. Specifically, the proposed architecture is predicated on a nonminimal state-space realization that generates an expanded set of states only using the filtered input and filtered output and their derivatives for each vehicle, without the need for designing an observer for each vehicle. Building on the results of this new observer-free output feedback cooperative control architecture, an event-triggering methodology is next proposed for the output feedback cooperative control to schedule the exchanged output measurements information between the agents in order to reduce wireless network utilization. Finally, the output feedback cooperative control architecture is generalized to adaptive control for handling exogenous disturbances in the follower vehicles. For each methodology, the closed-loop system stability properties are rigorously analyzed, the effect of the user-defined event-triggering thresholds and the controller design parameters on the overall system performance are characterized, and Zeno behavior is shown not to occur with the proposed algorithms --Abstract, page iv

Event-triggered consensus of multi-agent systems under directed topology based on periodic sampled-data

The event-triggered consensus problem of first-order multi-agent systems under directed topology is investigated. The event judgements are only implemented at periodic time instants. Under the designed consensus algorithm, the sampling period is permitted to be arbitrarily large. Another advantage of the designed consensus algorithm is that, for systems with time delay, consensus can be achieved for any finite delay only if it is bounded by the sampling period. The case of strongly connected topology is first investigated. Then, the result is extended to the most general topology which only needs to contain a spanning tree. A novel method based on positive series is introduced to analyze the convergence of the closed-loop systems. A numerical example is provided to illustrate the effectiveness of the obtained theoretical results

Event-triggered output consensus for linear multi-agent systems via adaptive distributed observer

summary:This paper investigates the distributed event-triggered cooperative output regulation problem for heterogeneous linear continuous-time multi-agent systems (MASs). To eliminate the requirement of continuous communication among interacting following agents, an event-triggered adaptive distributed observer is skillfully devised. Furthermore, a class of closed-loop estimators is constructed and implemented on each agent such that the triggering times on each agent can be significantly reduced while at the same time the desired control performance can be preserved. Compared with the existing open-loop estimators, the proposed estimators can provide more accurate state estimates during each triggering period. It is further shown that the concerned cooperative output regulation problem can be effectively resolved under the proposed control scheme and the undesirable Zeno behavior can be excluded. Finally, the effectiveness of the proposed results is verified by numerical simulations

Event-Triggered Algorithms for Leader-Follower Consensus of Networked Euler-Lagrange Agents

This paper proposes three different distributed event-triggered control algorithms to achieve leader-follower consensus for a network of Euler-Lagrange agents. We firstly propose two model-independent algorithms for a subclass of Euler-Lagrange agents without the vector of gravitational potential forces. By model-independent, we mean that each agent can execute its algorithm with no knowledge of the agent self-dynamics. A variable-gain algorithm is employed when the sensing graph is undirected; algorithm parameters are selected in a fully distributed manner with much greater flexibility compared to all previous work concerning event-triggered consensus problems. When the sensing graph is directed, a constant-gain algorithm is employed. The control gains must be centrally designed to exceed several lower bounding inequalities which require limited knowledge of bounds on the matrices describing the agent dynamics, bounds on network topology information and bounds on the initial conditions. When the Euler-Lagrange agents have dynamics which include the vector of gravitational potential forces, an adaptive algorithm is proposed which requires more information about the agent dynamics but can estimate uncertain agent parameters. For each algorithm, a trigger function is proposed to govern the event update times. At each event, the controller is updated, which ensures that the control input is piecewise constant and saves energy resources. We analyse each controllers and trigger function and exclude Zeno behaviour. Extensive simulations show 1) the advantages of our proposed trigger function as compared to those in existing literature, and 2) the effectiveness of our proposed controllers.Comment: Extended manuscript of journal submission, containing omitted proofs and simulation