either the sensor-to-controller or the controller-to-actuator channel, meaning that the majority of the existing models of NCSs simply assume those two delays affect the system performance in the same way. Although this assumption seems naturally true, further clarifications are necessary before regarding it as a general principle: Is it universally true that the delays in both channels are identical with respect to their effects on the system performance? This question is important since, although the answer of “yes” could confirm the correctness of existing results, the possible answer of “no” will put all these existing results in an awkward position and open the gate for a more appropriate modeling approach to NCSs.
High accuracy, fast data transfer, and proper performance of controlsystems are among the features which are very critical in recent decades. The closed-loop controlsystems generally include components as controller, sensor and actuator, and traditionally point-to-point wired system was used to connect components. The rapid progress of the digital systems, the widespread presence of networks such as the internet, and the advancement of control techniques, have led to the emergence of many trends in the field of networkedcontrolsystems [1]. The networkedcontrol system consists of sensors, actuators and controllers that are located in different places and are interconnected through the exchange of information by the network. Networkedcontrolsystems have many applications, among which remote control of robots, remote surgery, unmanned vehicles, automation of factories, remote diagnosis and advanced missiles can be mentioned [2]. According to their applications, the networkedcontrolsystems have different structures, the most important of which are direct structures. The direct structure has several types and is one of the most commonly used direct structure modes in which the controller and the plant are interconnected through a network. In the direct structure, the plant and the controller are located on the both sides of the network [3].
Considering the random delay and packet loss prob- lem caused by network bandwidth constraint, the clas- sical Smith predictor has strong advantages in compensating delay and has been successfully applied in SISO NCS [5]. In [6], an adaptive Smith predictor has been studied, where the predictive delay is adapted based on the delay information. In [7], a digital Smith pre- dictor based on delay compensator is proposed. Based on linear matrix inequalities, the uncertain system model is remodeled as Lyapunov equation for the stability ana- lysis. In [8], a controller is designed in terms of Smith predictor and incomplete differential proportion integra- tion differentiation (PID) method. The output has super- ior performance when there are large delays in network channels. In addition, a modified Smith predictor is proposed in [9] to reduce the negative effects of net- work-induced delays and disturbance. In [10], a Smith predictor is designed to compensate the delay and combined with proportion differentiation (PD) control
A feedback control system where in the control loop is barren through a real-time network is known as a networkedcontrol system (NCS), which includes fieldbus con-trol systems constructed on the origin of bus technology (e.g., DeviceNet, ControlNet, and LonWorks) and Internet- basedcontrolsystems using general computer networks. The NCS is a totally conveyed continuous input control system that is a combination of sensors, controllers, actuators, and correspondence systems .The insertion of the communication network in the feedback control loop makes the analysis and the design of an NCS complex. In particular, the accompanying issues should be addressed [1], [2]. 1) The network-induced delay (sensor-to-controller delay and controller-to-actuator delay) that happens while trading information among gadgets associated with the common system, which will be either steady or time differing, can corrupt the execution of control frameworks composed immediately and can even destabilize the systems.
In order to evaluate the effectiveness of the proposed delay prediction algorithm, an internetdelay history is captured and used. In this simulation, the ping command was used to obtain the Round Trip Time (RTT) between a PC in our laboratory (at the Iran University of Science and Technology) as the controlled plant and the server of www.google.com as the central controller. RTT is the total time needed to transmit a signal over a network up to a destination and back and can be considered as the total delay time for transmit, receive and service times. Therefore, it is a measure of the total delay of the network in both forward and backward channels, i.e., total delay of the control loop for the illustrated case study. The histogram and the plot of this data are shown in Figure 3 and Figure 4. Using this history, the proposed approach in Section 2 is employed to estimate the order of the network delay as a Markov chain, namely L. As shown in Figure 5, it is evident that the order of the process turns out to be 4. Then, the expected upper and lower bounds on the
Introduction: With the development of network technology, network control system (NCS) has been an active research hot spot in recent years. Network control system (NCS) are the systems in which the communication sensors, actuators and controllers occur through a shared band –limited digital communication network [1].The control system constructed by network has many excellent character, such as high expandability ,flexible structure , low cost, reliability etc. But at the same time it has some drawbacks like time delay and packet loss which cannot be avoided [2]. Fig 1 shows the basic block diagram of network control system. This paper mainly discuss about DC servomotor (which plays the role of plant), PID controller, Fuzzy controller and smith controller (which plays the role of controller).
In biology, psychology, sociology as well as in philosophy, the notion of ‘learning’ has been acknowledged as one of intelligent capabilities for an individual to earn food and fit the environment for surviving and evolving persistently. It is noted as a process for an in- telligent agent to acquire knowledge or experience from its perception and cognition of the environment and then to act on the environment so as to improve its behavior per- formance at the next time. Benefited from the advancing computer technology, learning algorithm has been algorithmically embedded into the control programming of a robotic manipulator to track a desired trajectory. The pioneer contribution is the iterative learn- ing control (ILC) invented in the s whose scheme is to utilize the historical tracking discrepancy to modify its control command so that the upgraded control command may drive the repetitive system to track a predetermined desired trajectory []. Overviewing the existing ILC investigations, the ILC has been acknowledged as one of the most effec- tive intelligent control strategies for a repetitive system operated over a fixed time interval owing to its less system information requirement and precise tracking insurance [–].
Based on our previous work [20]-[22], we present an active FTC method for NCSs with large transfer delays. We employ the multirate sampling technique to model the large random delay NCSs as Markovian jump systems with input delays. Under this model, an observer-based fault diagnosis method is proposed, which can provide accurate estimations of states and faults after faults occur. Based on the fault estimation and delayed state information, an active fault-tolerant control is designed to achieve the system stability. Finally, an inverted pendulum example is used to demonstrate the effectiveness of the theoretic results obtained.
The present paper addresses problem of stabilization for linear time-varying delaysystems under observer controller. A delay dependent robust stabilization condition is derived based on linear matrix inequalities (LMIs). The stabilization analysis is derived in three steps: (i) applying a state-observer controller to reconstruct an approximation of the unavailable state from the available input and output of the systems, (ii) the choosing of Lyapunov–Krasovskii functional with linear matrix inequality (LMI) techniques and integral inequality approach (IIA) in the designed observer controller and (iii) finally, a maximum allowable delay bound which ensures that the class of time-varying delay system with observer controller considered in this paper is stabilizable for any h is determined by solving an optimization problem.
49 Networked health in- formation providers like AHCN and Optum Online are exclusively in the business of information exchange and whatever relationship they have wi[r]
Although this work is a significant advance in the field in many respects, more work is needed to fully understand and solve the problem of optimal control of a network of dynamic agents. The optimal control law we have presented serves as a good benchmark to evaluate any other control strategy; however, it is computationally very expensive and good approximations that are more tractable will be useful in many situations. We have presented one such sub-optimal algorithm. From numerical examples it seems that the loss in performance is not huge. However, we have not been able to obtain an analytic expression for the loss in performance or a bound on it. Similarly, an analytic method for obtaining a relation between topology and optimal cost is needed. Our experience has been that not all topologies with same number of edges are equally good. The best topologies given the same number of edges tend to be the ones which are of the leader-follower type, in which there is one node in contact with all others. More work is needed to obtain analytic statements along these lines. Finally, work characterizing the effect of topology on the cost will also help in understanding the robustness of the algorithms to knowledge of the topology. Currently, we assume that either the control law is calculated off-line by a central processor, or each node knows the topology and calculates the control law for the entire system. To make this implementation more scalable, it will be useful to understand the effects of topology changes far from the individual agent, or equivalently, to imperfect knowledge of the topology far away.
In Control system any quantity of interest in a machine, instrument is maintained or altered with a desired manner [3]. Control system implementation uses point-to-point communication architecture for long time. But there is a limitation in point-to-point architecture due to expanding physical setups and functionality [1]. Hence, these system are no longer suitable to meet new requirements such as modularity, decentralization of control, integrated diagnostics, quick and easy maintenance, and low cost. But new system called networkedcontrolsystems (NCSs) are evolved due to advance technology network availability results in giving network facilities to Control system [12]. The major advantages of these systems are modular and flexible system design, simple and fast implementation, and powerful system diagnosis and maintenance utilities [3].
of monitoring as well as appraisal support to reinforce patient motivation. Internet use, especially in the initial stage, boosts older adults’ self-esteem. They realize their ability to learn something new and feel they are up to date with their children and their grandchildren. The Internet is so versatile; it provides not only opportunities for education, but also for frequent communication with families and friends, leisure through playing games to stimulate and challenge the mind, and can improve instrumental activity of daily living (IADL) by participating in online shopping on drugs, books, and even groceries. Mastering Internet technology, therefore, improves the quality of life of older adults. Throughout this process, emotional support from health care professionals is important to keep the participants motivated until the time that their illness prevents them from using the computer. Its application to Internet use is described in the intervention section of this study, and is illustrated in Figure 1.
Wired controlsystems are replaced by Wireless NetworkedControlSystems in factory automation due to ro- bustness and flexibility offered by the wireless option. Wireless workcell communication for factory automation was proposed in previous literature. The workcell had 30 sensors and 30 actuators communicating wirelessly through IEEE 802.11 to an Access Point (AP). The AP is connected to a controller through switched Ethernet protocol. Hierarchical fault-tolerance was added to the system in order to add a supervisor to a three-workcell system. The supervisor could either be passive or active. If the supervisor was active, the supervisor would take over functions of any controller once it fails. However, if the supervisor was passive, the supervisor would in- tervene only if all controllers failed.
From the model based controllers, the Smith predictor gave the best performance with faster rising time and lower overshoot. It presents a good robustness too. Therefore, there is a good prediction for slow changes in the network. On the contrary, the adaptive IMC presented the poorest performance and robustness. This is a result of a sluggish control signal which does not yield good control action under the adverse network conditions.
using a standard measuring tape (Gulik Tape, II, Moberly, Missouri). Prior to placing the electrodes, the skin at each electrode site was shaved, abraded, and cleaned with an alcohol wipe. Interelectrode impedance was less than 2000 Ω. The EMG signal was amplified (gain: x1000) using differential amplifiers (EMG 100, Biopac Systems, Inc., Santa Barbara, CA, bandwidth= 10-500 Hz).
The active system proposed in this paper integrates an electro-hydraulic actuator into an anti- roll bar. The system contains a high-level controller, which improves the roll dynamics of the chassis using active torque; thus, the roll motion of the chassis is influenced. The high-level control strategy is realized with a gain-scheduling Linear Quadratic (LQ) controller. The actuator of the anti-roll bar is an oscillating hydromotor with a servo valve on the low level. The actuator control guarantees the generation of the necessary active torque and satisfies the input constraint of the electric circuit. The control design is based on a constrained LQ method [13]. The goal of this paper is to demonstrate a multi-level control design of an anti-roll bar system.
Lyapunov-Krasovskii’s laws [1-4] is the most commonly used method for stability analysis of time delaysystems. Compared with usual Lyapunov-Razumikhin’s laws [5-7], Lyapunov-Krasovskii’s laws use Lyapunov functional to replace traditional Lyapunov function in order to obtain sufficient conditions for stability of system, which can effectively reduce the conservatism of stability margin, that is, the upper bound of the maximum allowable network transmission delay is obtained [8-10].
Considering static priorities, each node in the network has a unique and fixed priority. Hence, the application which has data flows of low priorities could not get the desired QoS and QoC. For example, we consider a control system with two nodes A and B, in which A has higher priority than B. At the instant t, both A and B have frames to transmit, B has a stronger transmission urgency in order to satisfy the QoS requirement (for example the deadline) but B cannot transmit its frames before the end of the frame transmission of A. That will make the system less efficient.