On general systems with network-enhanced complexities

In recent years, the study of networked control systems (NCSs) has gradually become an active research area due to the advantages of using networked media in many aspects such as the ease of maintenance and installation, the large flexibility and the low cost. It is well known that the devices in networks are mutually connected via communication cables that are of limited capacity. Therefore, some network-induced phenomena have inevitably emerged in the areas of signal processing and control engineering. These phenomena include, but are not limited to, network-induced communication delays, missing data, signal quantization, saturations, and channel fading. It is of great importance to understand how these phenomena influence the closed-loop stability and performance properties

Cost Function based Event Triggered Model Predictive Controllers - Application to Big Data Cloud Services

International audienceHigh rate cluster reconfigurations is a costly issue in Big Data Cloud services. Current control solutions manage to scale the cluster according to the workload, however they do not try to minimize the number of system reconfigurations. Event-based control is known to reduce the number of control updates typically by waiting for the system states to degrade below a given threshold before reacting. However, computer science systems often have exogenous inputs (such as clients connections) with delayed impacts that can enable to anticipate states degradation. In this paper, a novel event-triggered approach is proposed. This triggering mechanism relies on a Model Predictive Controller and is defined upon the value of the optimal cost function instead of the state or output error. This controller reduces the number of control changes, in the normal operation mode, through constraints in the MPC formulation but also assures a very reactive behavior to changes of exogenous inputs. This novel control approach is evaluated using a model validated on a real Big Data system. The controller efficiently scales the cluster according to specifications, meanwhile reducing its reconfigurations

Neural Models of Normal and Abnormal Behavior: What Do Schizophrenia, Parkinsonism, Attention Deficit Disorder, and Depression Have in Common?

Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333

Asynchronous sampling for decentralized periodic event-triggered control

Decentralized periodic event-triggered control(DPETC) strategies are an attractive solution for wireless cyber-physical systems where resources such as network bandwidthand sensor power are scarce. This is because these strategieshave the advantage of preventing unnecessary data transmis-sions and therefore reduce bandwidth and energy requirements,however the sensor sampling regime remains synchronous.Typically the action of sampling leads almost immediately toa transmission on an event being detected. If the sampling issynchronous, multiple transmission requests may be raised atthe same time which further leads to bursty traffic patterns.Bursty traffic patterns are critical to the DPETC systemsperformance as the probability of collisions and the amount ofrequested bandwidth resources become high ultimately causingdelays. In this paper, we propose an asynchronous samplingscheme for DPETC. The scheme ensures that at each samplingtime, no more than one transmission request can be generatedwhich prevents the occurrence of network traffic collision.At the same time, for the DPETC system with asynchronoussampling a pre-designed global exponential stability andL2-gain performance can still be guaranteed. We illustrate theeffectiveness of the approach through a numerical example

Short-term plasticity as cause-effect hypothesis testing in distal reward learning

Asynchrony, overlaps and delays in sensory-motor signals introduce ambiguity as to which stimuli, actions, and rewards are causally related. Only the repetition of reward episodes helps distinguish true cause-effect relationships from coincidental occurrences. In the model proposed here, a novel plasticity rule employs short and long-term changes to evaluate hypotheses on cause-effect relationships. Transient weights represent hypotheses that are consolidated in long-term memory only when they consistently predict or cause future rewards. The main objective of the model is to preserve existing network topologies when learning with ambiguous information flows. Learning is also improved by biasing the exploration of the stimulus-response space towards actions that in the past occurred before rewards. The model indicates under which conditions beliefs can be consolidated in long-term memory, it suggests a solution to the plasticity-stability dilemma, and proposes an interpretation of the role of short-term plasticity.Comment: Biological Cybernetics, September 201

Robust sampled-data implementation of PID controller

We study a sampled-data implementation of the PID controller. Since the derivative is hard to measure directly, it is approximated using a finite difference giving rise to a delayed sampled-data controller. We suggest a novel method for the analysis of the resulting closed-loop system that allows to use only the last two measurements, while the existing results used a history of measurements. This method also leads to essentially larger sampling period. We show that, if the sampling period is small enough, then the performance of the closed-loop system under the sampled-data PID controller is preserved close to the one under the continuous-time PID controller. The maximum sampling period is obtained from LMIs derived using an appropriate Lyapunov-Krasovskii functional. These LMIs allow to consider systems with uncertain parameters. Finally, we develop an event-triggering mechanism that allows to reduce the amount of sampled control signals used for stabilization