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This paper concerns the problem of networked control for vehicle lateral dynamic stability. Based on the Takagi-Sugeno (T-S) fuzzy representation of the vehicle, the state feedback and observer-based control designs are addressed. These controllers should guarantee the global stability of the resulting closed-loop fuzzy system with a prescribed H ∞ disturbance attenuation level. Digital communication network conditions, such as network-induced delays, data packet dropouts and limited communication capacity due to signal quantization are taken into consideration. Using fuzzy Lyapunov-Krasovski functional, we derive a less conservative delay-dependent criterion for stability analysis and control synthesis of networked control systems with a quantizer. Simulation results illustrate the effectiveness of the proposed approach.

This paper addresses the control-rate versus quantizer-resolution trade-off in networked control. The case presented considers the situation where the bit rate between controller and plant is constrained to a fixed number of bits per unit of time and an underlying fixed fast sampling rate is deployed to take measurements. However, a variable control update rate can be used between the controller and the plant. Inspired by the practical problem of inner loop power control in WCDMA, we assume the plant is an integrator. This assumption covers more general plants when fast sampling is used. Also, a restricted architecture in which linear filters are used for the encoder/decoder is considered and a quantizer with linear feedback is deployed. These choices give maximal insights into the underlying problem. It is shown that, in this case, it is best to use one bit per sample, in which case, the control update frequency is equal to the bit rate.

This paper reports novel algorithm for handling real time message scheduling on CAN bus which consider buffer occupancy and message type (alarm, periodic real time and non real time). This algorithm is derived to reduce burden of the distributed control unit (DCU) which have obligation to deliver message within strict time and also manage buffer storage occupancy. The algorithm works by changing the message priority which is attached in the identifier field of message frame. The priority adjustment is performed by online calculation prior to send the message on the CAN bus. Finally, this proposed algorithm is applied on large scale ship engine systems which consist of certain number of DCUs. In this networked control system, the proposed algorithm can decrease message drop. Furthermore, the important message alarm and periodic real time message can be delivered within the deadline.

Focusing on networked control systems with state delay and packet dropout, a state feedback control for a class of nonlinear networked control systems with system delays and packet dropout is proposed. Then an observer is designed to estimate the system state. Finally, we achieve stabilization of this class of systems through a dynamic output feedback without augmentation of the state space model. From an appropriate Lyapunov-Krasovskii function, sufficient conditions which guarantee the convergence of the state variables and state estimation errors to the origin are deduced and expressed in terms of simple LMIs. Usability and simplicity are the advantages of this approach. A numerical example is given to illustrate the effectiveness of the proposed approach.

Decentralized overlapping feedback control laws are designed for a vehicle-string bias system which controlled over networks in this paper. The dynamic model of vehicle-string bias system is treated as an interconnected system with overlapping states. Using the mathematic framework of the Inclusion Principle, the interconnected system is expanded into a higher dimensional space in which the subsystems appear to be disjoint. Then, for the locally extracted subsystems with respect to network-induced time- delay and packet-dropout, by modeling the networked control system (NCS) as an asynchronous dynamic system (ADS) with rate constraints on events, the state feedback controllers are designed and the sufficient exponential stability criterion is derived. The design procedure is based on linear matrix inequalities (LMI). As a final step, the decentralized controllers are contracted back to the original space for implementation. The simulation result is given to show the effectiveness of the method.

This paper proposes a self-organising networked controller for disturbance attenuation in multi-agent systems. A disturbance affecting a single agent has some effect on all neighbouring agents through the communication network. To avoid large disturbance effects, the proposed self-organising controller switches off the communication whenever the effect of the disturbance on the corresponding agent exceeds a given bound. As a consequence, the structure of the networked controller is adjusted to the current disturbance. It is proved that the proposed controller bounds the effect of arbitrary disturbances on all undisturbed agents. The results are illustrated by their application to a robot formation.

This brief studies the causal consensus problem of singular (descriptor) multi-agent systems with networked communication delays and agents described by general singular systems. For the studied systems, only the information of outputs are available through the network. An observer-based networked predictive control scheme is employed to compensate for the communication delays actively. Furthermore, based on the output feedback, observer and the networked predictive control scheme, a novel protocol is proposed in this paper. By using the tools of graph, algebra and singular system theory, the necessary and sufficient conditions are given for the existence of the proposed protocol to solve the considered consensus problem. The given conditions depend on not only the topologies of singular multi-agent systems but also the structure properties of each agent dynamics. Moreover, a consensus algorithm is proposed to design the novel observer-based predictive protocol. A numerical example demonstrates the effectiveness of compensation for networked delays using the provided consensus algorithm.

A current trend in the agricultural area is the development of mobile robots and autonomous vehicles for precision agriculture. One of the major challenges in the design of these robots is the development of the electromechanical components. This platform has as main characteristics four-wheel propulsion and independent steering, adjustable gauge, ground clearance of 1.80 m, diesel engine, hydraulic system, and a CAN-based networked control system. The aim of this work is to describe the project of an experimental platform for data acquisition in field for the study of the spatial variability and development of agricultural robotics technologies to operate in agricultural environments. This proposal is based on a description of the design process and definition of the design parameters utilized.

Control and/or scheduling design for systems characterized by limited resources, e.g. networked embedded control systems (NECSs) with limited communication and computation resources, entails an efficient utilization of such scarce resources. Efficient utilization is in the sense of fairly distributing and occupying the resources only if a necessity exists from stability or performance perspective. For this purpose, a robust event-based control and scheduling (RoEBCS) codesign strategy is proposed for NECSs with uncertain but interval-bounded time-varying communication and computation delays. The considered system is modeled as a discrete-time switched polytopic system with an additive norm-bounded uncertainty. The RoEBCS problem is then formulated as a linear matrix inequality feasibility problem for which many efficient solvers are available. The effectiveness of the RoEBCS strategy is illustrated for networked embedded control of a set of mechanically coupled inverted pendulums. Notably, the proposed codesign strategy is generally applicable to discrete-time switched polytopic systems with additive norm-bounded uncertainty.

When considering networked control systems, the controller is often designed for given loss and delay. However, when the packets are transmitted over a communication network, then the loss and delay between sensor, controller, and actuator depend on the position of the controller and the routes between sensor, controller, and actuator. Since many positions and routes might be possible, the interesting question is: Which controller position and which route gives the best control performance? To answer this question, we first present a model of the communication network and then formulate the controller design, its placement, and the routing as one optimization problem with integer constraints. With the help of two examples, we observe that the optimal route is in general not the one with the shortest delay, highest arrival probability, or minimal number of hops. Moreover, these examples also show that the optimal position of the controller is not always the actuator node.

The efficient usage of scarce communication resources is a vital necessity in networked control systems. This paper introduces a novel stochastic scheduling scheme for networked control systems with shared access through dynamic priority assignment. The overall system is assumed to consist of multiple control loops closed over a common communication network. A p -powered prioritized error-based (PEB) scheduler decides which transmission requests have the priority for channel access, and subsequently which controllers are updated with the actual states. According to the protocol, the likelihood to allocate the resource to a subsystem increases with a growing p -powered norm of its network-induced error. We show, under very mild assumptions, that the described system scheduled by the PEB protocol is stochastically stable. Moreover, numerical simulations demonstrate a significant reduction of the network-induced error variance in comparison to the other scheduling protocols such as CSMA or round robin.

In this paper, deterministic learning control using neural networks (NNs) is presented for a robotic system with unknown system dynamics. The dynamics of the robotic system are represented by an n-link strict robotic manipulator. The adaptive NNs is employed as the first control strategy to approximate the unknown model of the system and adapt interactions between the robot and a patient. Deterministic learning control using learned knowledge from direct NNs with Radial Basis Functions (RBFs) is employed as the second control strategy to improve the system intelligence for energy conservation and reduce control tasks. Uniform ultimate boundedness (UUB) of the closed loop system is achieved under the condition of the Lyapunov's stability with full state feedback control. Extensive simulations are carried out to expound the efficacy of the proposed control strategies and the advancement of learning control.

This paper investigates a new alternative approach to compensate for the effects of packet dropouts (data do not arrive at destination) on a dynamic networked control system (NCS). The approach uses an observer-based control computation using time-delay predicted values as the sampling times of the NCS. To obtain the predicted values of time delays we propose the use a one-step-ahead and multi-step prediction algorithms based on an adaptive time delay neural network (TDNN). The control gains are calculated using optimal control.

This paper proposes a new approach to distributed control of physically interconnected subsystems which combines continuous and event-based state feedback. The main aim of the controller is to suppress the propagation of a disturbance within an interconnection of subsystems. The novelty of this approach is that the controllers request current state information from the neighboring systems at the event times. Events are generated based on a condition for which only local information are applied. The disturbance rejection behavior of the control approach with event-based information requests is demonstrated in an illustrative example which shows that the disturbance propagation is considerably reduced compared to a continuous decentralized state-feedback controller.

Efficient control of Networked Control System (NCS) is a challenge, as the control methods need to deal with non-deterministic variable delays and data loss. This paper presents a novel hybrid approach to NCS where Model Predictive Control (MPC) is applied as a main controller and implicit switching MPC is used for data transmission control in event-driven shared communication medium, leading to complex control system with active data transmission mechanisms.

The paper considers the problem of synchronising linear discrete-time agents within a finite time interval. The agents can measure only the outputs to be synchronised. The main result is a controller structure for which the agents synchronise within at most 2 n time steps, where n is the dynamical order of the agents. The local controllers are composed of a dead-beat observer for the local states and a dead-beat state feedback. The result shows that for the standard synchronisation problem without disturbances, which is the subject of the majority of papers about synchronisation and consensus, a feedback controller can be found that does not only reach asymptotic synchronisation but moves all agents in finite time onto the synchronous trajectory. The result is illustrated by its application to distance control of a vehicle platoon.

In this paper, we consider designing an adaptive output feedback control system with a simple structure for the slip control of a lock-up clutch system with a time delay. A design scheme of a parallel feedforward compensator (PFC) for systems with a time-delay for rendering ASPR augmented system will be proposed in order to realize a stable adaptive output feedback control to a slip control system of lock-up clutch with a time-delay. The effectiveness of the proposed method is confirmed through numerical simulations for a slip control of lock-up clutch system.

International audience; This paper investigates H∞ predictive control problem for a class of networked control system, and presents a method for quantitatively selecting the control weighting parameter. Based on Lyapunov function approach and H∞ control theory, a generalized predictive state feedback controller is derived to guarantee the closed-loop networked system stable. Then, an algebraic method for selecting a feasible GPC control-weighting parameter is proposed. The contribution realizes the adaptive parameter tuning for GPC with respect to response demands and performance. A simulation example demonstrates the feasibility and practicability of the proposed method.

The domain of networked control systems (NCS) has traditionally been concerned with modeling and designing distributed controllers in the presence of control message loss, varying delay and jitter. Here, the characteristics of the network are assumed to be given and the focus has largely been on the controller. While this is meaningful in the context of control over wireless networks, where the network is assumed to be predesigned, in many other domains there is considerable flexibility in designing the network itself. For example, in an automotive architecture, distributed controllers are implemented on multiple electronic control units (ECUs) that communicate over CAN or FlexRay buses. The network design problem in this case consists of mapping control tasks to ECUs, and determining both the scheduling parameters for the ECUs as well as those for the communication buses. Given a choice of these parameters, the controller design problem is similar to that studied in NCS, i.e., that of designing/analyzing the controller. However, the joint design and optimization of the network and the controller gives rise to new research questions, that have neither been studied within the NCS, nor within the embedded systems domain. We refer to such systems as arbitrated networked control systems (ANCS) to emphasize the fact that the arbitration policy in the network also needs to be determined, and discuss recent results that have been obtained in the literature in this context.

There are many benefits to using a multi-agent system for power plant control, such as the ability to perform multiple computationally intensive tasks in parallel such that effective optimized real-time control can be achieved. These parallel tasks include neural network training, parameter optimization, and system monitoring. The multi-agent system approach also allows for intelligent control that is robust and flexible in that it can autonomously make decisions and adjust to partial control system failure to maintain control with minimal performance degradation, to name a few of the potential benefits. It is the goal of this paper to detail the implementation of neural networks as models of the power plant system to allow effective online performance evaluation of the power plant in the optimization processes, where the optimization processes provides optimized control setpoints and feedback gain values. Though equations are available for the power plant model used here, the larger, more complex power plant systems for which this MAS control system is ultimately being designed will either not have equations available or the model is too computationally complex for use in real-time operation, making it necessary to have a simpler model such as an artificial neural network.

This paper studies the problem of active fault-tolerant control for networked control systems with imperfect measurements. A unified measurement model is utilized to take time-varying delay and random packet dropout into consideration simultaneously. The fault is asymptotically estimated via a fault estimation filter. Based on the fault estimation information, an output feedback fault-tolerant controller is constructed to guarantee the stability and a certain performance constraint of the closed-loop systems. In terms of linear matrix inequality, a sufficient condition is derived for the existence of fault estimation filter and fault-tolerant controller. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.

The paper addresses problem of designing a robust output feedback model predictive control for uncertain linear systems over networks with packet-loss. The packet-loss process is arbitrary and bounded by the control horizon of model predictive control. Networked predictive control systems with packet loss are modeled as switched linear systems. This enables us to apply the theory of switched systems to establish the stability condition. The stabilizing controller design is based on sufficient robust stability conditions formulated as a solution of bilinear matrix inequality. Finally, a benchmark numerical example-double integrator is given to illustrate the effectiveness of the proposed method.

This paper gives a survey on recent reserch activities and published results in the area of fault detection and fault-tolerant control in networked control systems.

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 investigates the stability as well as communication properties of sampleddata control and two event-triggered control schemes: deadband control and model-based eventtriggered control. Af ...

This paper proposes a supervisory control structure for networked systems with time-varying delays. The control structure, in which a supervisor triggers the most appropriate controller from a multi-controller unit, aims at improving the closed-loop performance relative to what can be obtained using a single robust controller. Our analysis considers average dwell-time switching and is based on a novel multiple Lyapunov-Krasovskii functional. We develop analysis conditions that can be verified by semi-definite programming, and show that associated state feedback synthesis problem also can be solved using convex optimization. Small and large scale networked control systems are used to illustrate the effectiveness of our approach.

Herein we present an algorithm for controlling LTI processes using an adaptive sampling interval where the controller at every sampling instant not only computes the new control command but also decides the time interval to the next sample. The approach relies on MPC where the cost function depends on the control performance as well as the cost for sampling. The paper presents a method for synthesizing such a predictive controller and gives explicit conditions for when it is stabilizing. Further it is shown that the optimization problem may be solved off-line and that the controller may be implemented as a lookup table of state feedback gains. The paper is concluded with a numerical example.

The multi-agent system approach to power plant control provides many benefits in that it allows multiple computationally intensive tasks to be performed in parallel to provide effective real-time control, such as training and adapting artificial neural network models, parameter optimization, and system monitoring. The multi-agent system approach also provides intelligent control that is robust and flexible in that it can autonomously make decisions depending on the situation and adjust to partial control system failure to maintain control, to name a few of the potential benefits. It is the goal of this paper to detail the implementation of real-time feedback gain optimization as a means to provide more efficient control and demonstrate an added advantage of the parallel nature of a multi-agent system designed for intelligent optimized multiobjective control.

A networked control system (NCS) is a control system in which plants, sensors, controllers, and actuators are connected through communication networks. In this paper, as one of the design problems for NCSs, we consider optimal sampled-data control of linear systems with uncertain input delay and uncertain sampling period. First, an input delay system is transformed into a discrete-time system with parameter uncertainty. Furthermore, the obtained system is expressed as a mixed logical dynamical model by using our previously proposed modeling method. Next, a given continuous-time cost function is transformed into a discrete-time cost function with parameter uncertainty, and the optimal control problem is approximately expressed as a mixed integer programming problem.

In an event-triggered networked control system (NCS), an agent broadcasts its state information to its neighbors when some local event occurs. This paper studies the design of quantization scheme in such systems. We derived conditions on quantized event-triggered NCS which, when satisfied, can guarantee the asymptotic stability of the system. We also propose an event-triggered dynamic logarithmic quantizer, which fits well into the current system framework, and incurs little communication overhead.

In this paper we analyze the scenario where a set of wireless sensors are time-multiplexed in order to reduce the traffic load and energy consumption in a Networked Control System. The possible choices for scheduling the transmission of measures are explored under the concept of periodic feedback patterns, i.e. repeated sequences of different measures. The length and structure of such patterns are chosen to guarantee stability of the closed loop and to minimize a predefined performance index. The control scheme is enhanced with a controller-side observer running at a rate higher than the sampling rate, for which a novel stability problem is stated and solved.

In this paper, the virtual actuator (VA) technique for Fault Tolerant Networked Control Systems (FTNCS) subject to actuator failure is addressed. This is a powerful technique for reconfiguration-based active Fault Tolerant Control (FTC) which enables a design process independent from estimated model of the fault. This technique is generalized here to derive the sufficient conditions for a dynamical virtual actuator in order to preserve the stability and performance of faulty control loop. These conditions as well as the design procedure of the VA are presented. Furthermore, the virtual actuator is extended toward FTNCS subject to actuator failure. The simulation results indicate that the technique can completely reconfigure the NCS in presence of actuator failure.

This article deals with the control of a two-input two-output (TITO) discrete time LTI plant, where one control signal is to be sent through a transparent channel, and the remaining control signal has to be sent through a channel that presents signal-to-noise ratio (SNR) constraints. For this situation, we study three different control architectures: state feedback, output feedback, and output feedback enriched with feedback around the constrained channel. We explore how the stability, and the best achievable performance, depend on the location of the SNR constrained channel for each architecture. An interesting conclusion of this work is that the best channel choice, when stability is the only concern, may be inadequate when the focus is on performance. A numerical example is included to illustrate our results.

This paper proposes a new event-based control method for nonlinear systems that are input-output linearizable. The control input generator uses a copy of a continuous reference system to generate an exponential control input that keeps the state of the disturbed plant in a bounded surrounding of the setpoint. An upper bound for the deviation of the event-based control loop to the reference system is derived, which depends on the event threshold of the event generator. Hence, by appropriately choosing the event threshold, the event-based control can be made to mimic the continuous control with arbitrary accuracy. The proposed event-based control method is applied to a cooling process.

The use of communication networks for the transmission of control system signals (measurements and actuation) is becoming more widespread. If the communication (or the sensors and actuators themselves) can be modified by a malicious agent then the control of the physical plant can be covertly appropriated. A parameterised decoupling structure is introduced which allows the covert agent a wide range of control actions on the physical plant while remaining undetectable from the point of view of the original networked controller. The designer of the covert agent need only have a model of the physical plant; knowledge of the networked controller is not required. A MIMO process control example (based on the control of irrigation canals) is used to illustrate the concepts.

The performance of Networked Control Systems (NCS) is directly affected mainly by time-varying network delays, so a specific approach to compensate their effects is usually required during the controller design process. This paper deals with the control of an experimental platform over the Internet by applying a novel fractional gain scheduling strategy which enables an existing fractional order PI controller for efficient control over the network. The proposed controller introduces delay-adaptive gains, which are optimal with respect to the current network condition. Experimental results show the effectiveness of the proposed controller, providing significantly better system performance, especially when the network-induced delay is increased in certain circumstances.

We consider an optimal control problem for networked control systems, where the loop is closed via a lossy, distributed network with an acknowledgment mechanism. The network is distributed in the sense that there are different sets of sensors and actuators that each communicate individually with the controller. We assume that all packets, i.e., the measurement packets, the control packets and the acknowledgment packets are sent over the lossy network and thus are subject to loss. We derive suboptimal controllers with respect to a quadratic cost criterion for the general case and optimal controllers for the case that all states are perfectly measured over a single link. Additionally, we present stability criteria for both cases.

This article addresses the Linear Quadratic Gaussian (LQG) optimal control problem for networked control systems when data is transmitted through a TCP-like network and both measurement and control packets are subject to random transmission delays and packet dropouts. Instead of adopting a “zero-input” or “hold-input” strategy, we propose a “predictive optimal-input” strategy, where at each time instant, K future control inputs are computed and sent in addition to the current one in the same control packet. The stabilizability conditions of the system are then derived. Simulation results are presented to demonstrate the effectiveness of the proposed approach.

The binary control protocols are proposed for synchronization of networked Lagrangian systems with/without tracking. These protocols are inspired by the observed behavior in animal groups, where relative motion is determined not by full time-signal measurements, but by coarse estimates of relative heading differences between neighbors. The signum protocol does not require explicit measurement of time signals from neighbors, and hence it has the potential to significantly reduce the requirements for both computation and sensing. In the case that there exists a time-varying control node in the networked Lagrangian systems, the finite-time tracking control is achieved by using the signum protocol. Numerical examples are given to illustrate the theoretical results.

We investigate a networked control architecture for LTI plant models with a scalar input. Communication from controller to actuator is over an unreliable network which introduces packet dropouts. To achieve robustness against dropouts, we adopt a packetized predictive control paradigm wherein each control packet transmitted contains tentative future plant input values. The novelty of our approach is that we seek that the control packets transmitted be sparse. For that purpose, we adapt tools from the area of compressed sensing and propose to design the control packets via on-line minimization of a suitable L1/L2 cost function. We then show how to choose parameters of the cost function to ensure that the resultant closed loop system be practically stable, provided the maximum number of consecutive packet dropouts is bounded. A numerical example illustrates that sparsity reduces bit-rates, thereby making our proposal suited to control over unreliable and bit-rate limited networks., arXiv admin note: text overlap with arXiv:1307.8242

In recent control literature, it was shown that event-based control requires less samples than time-triggered control. Consequently, it is argued that event-based control is superior to time-triggered control for Networked Control Systems (NCS) because it reduces the network utilization. The aim of this work is to show that it is not that simple. Therefore, we compare event-based and time-triggered control for a NCS consisting of subsystems with integrator dynamics and a shared communication medium, where packets are only lost due to collisions. We show that packet losses due to collisions drastically reduce the performance of event-based control if packets are transmitted whenever the event-based control generates an event. In contrast to previous findings, it turns out that in this setup time-triggered control is superior to event-based control.

In this paper, a Robust Networked Control System (RNCS) subject to data losses constraints is considered. These data losses are modelled as an independent sequence of i.i.d. Bernoulli random variable. This random variable is replaced by an additive noise plus a gain, which is equal to the successful transmission probability in the feedback loop. Also, structural uncertainties in the model of the plant are considered. To cope with this problem, a mixed H 2 / H ∞ control technique is proposed in this work. In this way, the H 2 approach is used to stabilize the NCS taking into account the probability of data dropouts, while the H ∞ approach is in charge of making the closed-loop system robust enough against structural uncertainties of the nominal model.

The tutorial paper presents problems and approaches of control of distributed Gaussian systems. A distributed system consists of the interconnection of two or more subsystems. In distributed control of such systems there is no communication between controllers at all and the control synthesis is mostly via the determination of a person-by-person equilibrium. In distributed control with communication the controllers can exchange communication with other subsystems but this is done only when needed and with a restricted subset of the other subsystems. In coordination control a coordinator sets references signals for two or more subsystems. Finally control of hierarchical Gaussian systems is discussed.

An industrial case study involving a hydraulic network underlying a district heating system is investigated. The flexible structure of the network calls for control structure which is able to handle changes in the network structure. For this purpose a set of decentralized proportional controllers have been proposed. These controllers make use only of locally available information, and in order to make implementation of the control laws possible, the control signals are required to be communicated across the network. To accommodate this a quantized version of the control laws are considered, and the results show that the designed closed loop system maintains its stability properties despite the structural changes introduced in the system.

Event-based control aims at reducing the information exchange over the communication network in control systems. This paper extends an event-based state-feedback approach in order to cope with the situation that the digital communication link between the sensors and the actuators induces a substantial time delay. As the main result, a bound for the maximum tolerable time delay is derived, which guarantees that the event-based control loop is stable, in the sense, that its state remains in a bounded surrounding of the state of a continuous-time state-feedback loop without delays.

This work deals with a networked model predictive coordinated control scheme for discrete-time constrained nonlinear systems characterized by mutual physical interconnections and subject to local state-feedback control policies. A centralized controller, communicating with the distributed agents through a packet-based network, is designed to guarantee the uniform boundedness of the trajectories of the overall closed-loop system. In order to cope with time-varying transmission delays, typically affecting networked control systems, a network delay compensation strategy based on the time-stamping of data packets is adopted. With respect to the existent model-based approaches for nonlinear networked control system, the proposed method eliminates the need for acknowledgment messages, by suitably modifying the finite horizon optimization problem associated to the MPC. The proposed coordinated networked policy guarantees the robust enforcement of state and input constraints even in presence of bounded model uncertainty.

We study a predictive control formulation for discrete-time non-linear plant models where controller output data is transmitted over an unreliable communication channel. The channel is affected by random data-loss and does not provide acknowledgments of receipt. To achieve robustness with respect to dropouts, at every sampling instant the controller transmits packets of data. These contain possible control inputs for a finite number of future time instants, and minimize a finite horizon cost function. At the plant actuator side, received packets are buffered, providing the plant inputs. Within this context, we adopt a stochastic Lyapunov function approach to establish stability results of this networked control system.

A key component of the automotive industry is represented by clutch control technology, a central element in gear shifting, playing an important role in assuring improved safety, drivability, comfort and fuel economy. Yet, in modern vehicles, control signals and sensors measurements are exchanged by control system components using a network based type of communication. In this paper a new Networked Predictive Control Strategy is proposed in order to decrease the influence of the variable time delay induced in the NCS on the control performance of the electro-hydraulic actuated wet clutch. The performances of the proposed method are demonstrated by simulation results and corresponding comparisons prove the significance of this method.

International audience; Energy is a key resource in Networked Control Systems, in particular in applications concerning wireless networks. This paper reviews the multi-layer architecture of those systems in the light of their energy-use, and points out major contributions in the area of energy-management policies, layer per layer. This review of the literature is organized according to the layered communication architecture covering from bottom to top the Physical, Data Link, Network, and Application layers. We specifically focus on advances that concern energy-aware management in wireless communication and control co-design. It is argued that existing work is limited to single layer approaches, with a lack of design methods taking into account several layers.

Event-based control aims at reducing the information exchange over the communication network in control systems. This paper presents an extension of an existing event-based state-feedback approach that reduces the communication to quantized state information, which requires a low number of bits to be transmitted over the network. The main results are a maximum bound for the deviation between the behavior of the quantized event-based control loop and a continuous state-feedback loop and a direct relation between the maximum communication frequency and the number of bits to be sent. The results are evaluated by simulations and experiments.