For a single control loop with communication rate constraints, Event-triggered control often outperforms periodic control. When multiple loops are being controlled over a shared contention-based medium, however, the advantage of event-triggered policies is less well understood. In this paper, we consider event-triggered impulse control under lossy communication. The sampling events are determined by level crossings of the plant output. It is shown how a stochastic control criterion depends on the level thresholds and the packet loss probability for a class of integrator plants. For multiple control loops, this result is used to derive a design guideline on how to assign the levels that lead to optimal use of the available communication resources. It is shown that the structure of the event generator depends critically on the loss probability.

Abstract-Novel event-triggered sensing and actuation strategies are presented for networked control systems with limited communication resources. Two architectures are considered: one with the controller co-located with the sensor and one with the control co-located with the actuator. A stochastic control problem with an optimal stopping rule is shown to capture two interesting instances of these architectures. The solution of the problem leads to a parametrization of the control alphabet as piecewise constant commands. The execution of the control commands is triggered by stopping rules for the sensor. In simple situations, it is possible to analytically derive the optimal controller. Examples illustrate how the new eventbased control and sensing strategies outperform conventional time-triggered schemes.

A recent multivariable laboratory process is presented together with its use in a graduate control course. The process is called the Quadruple-Tank Process and demonstrates a multivariable level control problem. The multivariable zero dynamics of the system can be made both minimum phase and nonminimum phase by simply changing a valve. This makes the Quadruple-Tank Process suitable for illustrating many concepts in linear and nonlinear multivariable control. In this paper some of these are described together with the basic setup of the process. Two computer exercises and one laboratory exercise have been developed as part of a course in multivariable and nonlinear control. These are detailed and some experience from the course is presented.

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Abstract-In several multi agent control problems, the convergence properties and the convergence speed of the system depend on the algebraic connectivity of the graph. We present a novel distributed algorithm where the agents estimate this algebraic connectivity, obtaining a more accurate estimate at each iteration. This algorithm relies on the distributed computation of the powers of the adjacency matrix. We provide proofs of convergence and convergence rate of the algebraic connectivity estimation algorithm. In addition, we give upper and lower bounds at each iteration of the estimated algebraic connectivity. We apply this method to an event-triggered consensus scenario, where the most recent estimate of the algebraic connectivity is used for adapting the behavior of the average consensus algorithm. We show that both processes can be executed in parallel, i.e., the nodes do not need to wait for obtaining a good estimate of the algebraic connectivity before starting the averaging algorithm.