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In the present paper, a fuzzy codesign approach is proposed to deal with the controller and scheduler design for a networked control system which is physically distributed with a shared communication network. The proposed fuzzy controller is applied to generate the control with different sampling-actuation periods, the configuration supposes a strict actuation period disappears the jitter. The proposed fuzzy scheduling is designed to select the sampling-actuation period. So, the fuzzy codesign reduces the rate of transmission when the system is stable through the scheduler while the controller adjusts the control signal. The fuzzy codesign guarantees the stability of all the system if the network uncertainties do not exceed an upper bound and is a low computational cost method implemented with an embedded system. An unstable, nonlinear system is used to evaluate the proposed approach and compared to a hybrid control, the results show greater robustness to multiple lost packets and time delays much larger than the sampling period. (This paper is an extension of [20]. Reprinted (partial) and extended, with permission based on License Number 4275590998661 IEEE, from "Electrical Engineering, Computing Science and Automatic Control, 2017 14th International Conference on")

The ability of bacteria and archaea to modulate metabolic process, defensive response, and pathogenic capabilities depend on their repertoire of genes and capacity to regulate the expression of them. Transcription factors (TFs) have fundamental roles in controlling these processes. TFs are proteins dedicated to favor and/or impede the activity of the RNA polymerase. In prokaryotes these proteins have been grouped into families that can be found in most of the different taxonomic divisions. In this work, the association between the expansion patterns of 111 protein regulatory families was systematically evaluated in 1351 non-redundant prokaryotic genomes. This analysis provides insights into the functional and evolutionary constraints imposed on different classes of regulatory factors in bacterial and archaeal organisms. Based on their distribution, we found a relationship between the contents of some TF families and genome size. For example, nine TF families that represent 43.7% of the complete collection of TFs are closely associated with genome size, i.e., in large genomes, members of these families are also abundant, but when a genome is small, such TF family sizes are decreased. In contrast, almost 102 families (56.3% of the collection) do not exhibit or show only a low correlation with the genome size, suggesting that a large proportion of duplication or gene loss events occur independently of the genome size and that various yet-unexplored questions about the evolution of these TF families remain. In addition, we identified a group of families that have a similar distribution pattern across Bacteria and Archaea, suggesting common functional and probable coevolution processes, and a group of families universally distributed among all the genomes. Finally, a specific association between the TF families and their additional domains was identified, suggesting that the families sense specific signals or make specific protein-protein contacts to achieve the regulatory roles.

A Networked Control System (NCS) is a control system closed over communication networks, this network generates negative effects that degenerate the performance of whole system. This paper introduces a fuzzy controller for networked control systems with synchronization at the actuation instant to fix for the negative effects of the network. Using an actuation instant model, the system obtains robustness to delays longer than a sampling period and variable sampling intervals.

This chapter shows models for time delays and others network imperfections generated into NCS and how they are integrated into control, scheduling or codesign algorithms. First, a time delay model is presented using a generalized exponential distribution based function with data collect from non-deterministic networks. After, three NCS models are presented, each incorporates information about the network imperfections with the ultimate aim of generating a corrective action. We present models based on control, communication and codesign methodologies. Finally, a neuro-fuzzy identification is presented to model the system states and estimate the parameters of the NCS based on multi-sampling periods.

In this chapter, an extension of distributed systems considering mobile computing is reviewed. First, A review of some scheduling algorithms is presented to get a good approximation to bounded time delays considering the spend time in all stages of communication and compute. A review of the most important algorithms in terms of consensus and routing is presented. A computer network design is presented from a selection of real-time features, scheduler, task handlers, priorities, precedencies and consensus. Finally, a brief review of control design for mobile conditions is presented with the most representative algorithms in the literature.

Distributed systems have a lot of applications, commonly with multitasks where the information exchange is high through a communication network, this exchange presents inherent time delays that degrade the system. For obtaining an acceptable performance in the distributed system, time delays need to be taken into consideration during design. This chapter is devoted to reviewing some representations of time delays in function of some features as the operative system, network, scheduling. These include the scalability, concurrency, feasibility and extensibility. Several situations are reviewed, such as aperiodic communications or consensus needs, among other situations. Finally, an introduction to a helicopter dynamic model is given, focusing on real-time representation with a relationship matrix of transmission frequencies through True-time simulation.

This chapter presents the control strategies and codesign based on the NCS models presented in Chap. 2. Three methodologies are proposed focusing on vanishing the perturbations generated by the network, such as time delays larger than a sampling period and lost packets. First, an adaptive fuzzy control is developed according to the scheduling algorithm and the known and bounded time delay, the stategy us a fuzzy model and a LQR control design to modify the control input according to the time delay. A sampling frequency control is presented where the transmission frequiencies are modified into a region according the quality of services into the network. Finally, a codesign strategy is reviewed where the quality of service and the quality of control are trade-off with two fuzzy model, one fuzzy model modifies the input control based on the current sampling period and other fuzzy model modifies the next sampling period based on the time delays and lost packets in a time-lapse.

This chapter introduces a brief description of Networked Control Systems. A formal review of this book is given, describing the key issues within each chapter. A review of the strategies present in the literature is made to study and compensate for the network imperfections presenting three main methodologies. The control methodology focuses on generating control signals that counteract the effects of the network imperfections through modelling its dynamics or considering them as uncertainties. The communication methodology aims to improve the transmission of information and minimise imperfections through the scheduling and synchronisation of the nodes present in the network as a function of the system performance. The co-design methodology considers increasing the advantages of the various methodologies with the purpose of increasing system performance and minimising the effects of network imperfections. It is also presented the time delay modelling in nondeterministic networks, the main imperfection of the network. Finally, the maximum allowed transfer interval term is described which is the maximum bound for time imperfections of the network.

A fuzzy codesign approach is proposed to deal with the controller — scheduler design for a networked control system which is physically distributed using a shared communication network. In the proposed approach, a fuzzy controller is applied to generate a control law for different sampling periods, the configuration supposes a strict actuation period vanishing the jitter. A neural uncertainty estimator is designed to actively compensate for the time delays and packet loss. A novel fuzzy scheduling is designed to schedule the sampling period of the sensor node and reduces the transmission packets into the network. The fuzzy codesign guarantees the stability of all the system if the network uncertainties do not exceed an upper bound. The codesign is a low computational cost approach implemented with an embedded system. An unstable, nonlinear system is used to evaluate the proposed approach.

In a networked control system, several nodes exchange information through a network, to achieve specific control goals and thus increasing network traffic. This affects the overall system performance. Several approaches try to satisfy requirements of both control and communication performance. Particularly, some methodologies have been proposed to save bandwidth. One of such methodologies has been scheduling, which has been studied in depth through the last decade. Commonly, the objective of using scheduling to save bandwidth is to accurately use the computing resources. This paper shows two scheduling strategies, one performing static scheduling and the other carrying out dynamic scheduling, in order to expose the advantages of using dynamic scheduling in an ad hoc implementation. Both strategies execute on a real-time distributed system, and both are able to modify the frequency of transmission as well as the periods of tasks in individual components. Hence, both of them tend to impact on the quality of performance of the system, due to network use. The first scheduling strategy modifies the periods of task, and network access is assigned through a static scheduling algorithm. On the other hand, the second strategy, schedulability, is dynamically achieved by controlling the rate of frequency transmission into a frequency region, bounded by minimum and maximum transmission rates. Numerical simulations are used as implementations of both strategies.

Varying time delays, packet loss and variable sampling interval degrade the performance of control loops closed over communication networks, (Networked Control System, NCS), the problem is more complicated with is necessary to detect faults because is possible to confuse a fault with a network imperfection effect. To compensate the negative effects of network and get good fault detection, in this paper we propose a fuzzy observer for networked control systems with synchronization at the actuation node to detect fault. Using the one-shot model has some benefits such as consider delays longer than actuation period instead of consider delays less than sampling period and variable sampling interval.

This paper presents a fuzzy controller for class of non-linear networked control systems; the varying time delays and packet loss are taken as a variable sampling period of the system. The variable sampling period is estimated using a time stamped and probability density function. A fuzzy model smoothly switches to estimate the system state; the antecedent input is the estimated sampling period and the consequent part is formed by linear models discretized with specific sampling periods. The fuzzy controller generates a control input using the estimated states to ensure system stability for a wide range of sampling periods. A two-degree-of-freedom helicopter is used to show the applicability and effectiveness of the controller with robustness to traffic.

This work presents a supervisory control strategy for Networked Control Systems (NCSs). This shows the identification and control of the plant using the fuzzy theory. The identification was perform through input-output data. The fuzzy model incorporates the delay dynamics within the fuzzy rules. The controller is composed by a nominal control (PID) and a supervisory control which proposed from the delay dynamics. A system of magnetic levitation is presented as a case study.