Adopting Setpoint Weighting Strategy for WirelessHART Networked Control Systems Characterised by Stochastic Delay

This paper presents an Internal model controller (IMC) for temperature control of heating, ventilation and air conditioning (HVAC) system. Firstly, the temperature of an air-conditioning system of the test room is controlled by using PID controller. The performance and stability of the control process are degraded due to the significant time delay of air-conditioning process. So, smith-predictor scheme is used to compensate the time delay. But when the process has larger system parameters and longer time delay, smith-predictor does not provide better results. To overcome this problem, an internal model (IMC) controller is developed. Simulation results compare the performance of internal model (IMC) controller with conventional PID controller and smith predictor controller. Simulation result shows that an internal model controller gives satisfactory performance with less overshoot and less settling time.

Aiming at the problem of stochastic network-induced delay, we use the Dynamic Matrix Control (DMC) prediction technique to design the controller in order to ensure the validity and stability of the control in the Network Control Systems. Based on an overall consideration of the stochastic network-induced delay, a unified modeling approach of stochastic network-induced delay is proposed. We adopt a DMC strategy to solve the problem of stochastic network-induced delay and constitute predictive control algorithm based on the DMC and feedback compensation. The simulating results shows a favorable control performance and stability of Closed Loop Network Control Systems by using this design.

Aiming at the problem of network-induced delays for multi-input multi-output networked control systems (MIMO NCS), a new Smith predictive fuzzy immune PID algorithm is introduced to effectively reduce the adverse effect of network-induced delays about stability in MIMO NCS, firstly, by using the v-norm type to decouple the coupling plant. Based on the generalized plant after decoupling, the new Smith predictor is designed. Then, an modified fuzzy immune feedback control algorithm is used to tune PID controller parameters online. When there is a model mismatch or parameter perturbation, the set-point tracking performance and robustness of output response can be significantly optimized. The proposed algorithm does not contain the network delay prediction model and does not need to measure, estimate, or identify the network delay. It can be used in MIMO NCS where the network bandwidth is constrained and the network-induced delays are large. Numerical examples with Ethernet protocol are built to illustrate the correctness and effectiveness of this new algorithm.

Networked control systems (NCSs) are significant and foremost multidisciplinary research areas for many decades. This paper is mainly oriented toward recent developments and challenges of network-induced delays due to inclusion of data network in NCSs. Network delays deteriorate the control performance and stability of the NCSs. The time-varying delays can be measured in real time by calculating the time difference of sending and receiving control packets. Various compensation techniques are reviewed to mitigate the effect of constant, time-varying, and stochastic delay. Lastly, some conclusions are drawn and the future research scope is directed.

This paper presents a novel predictive control scheme with an enhanced Smith predictor for a networked control system with random time delays and system uncertainties. In the scheme, time axis is partitioned into equidistant small intervals to limit the continuous time varying delays into several discrete values. The stability of the networked control system is achieved by establishing an offline database and an online update strategy for Smith predictor, which reduces the reliance on the determined model of delay and system uncertainties in comparison with conventional Smith predictor. In this way, a sequence of finite forward predictive control signals of all possible time delays can be generated in advance and the actual delays will be compensated in real time when achieving the real delay information. Illustrative examples are given to demonstrate the effectiveness and robustness of the proposed predictive methods towards the random transmission delays and system uncertainties integrated in the networked control system.

An online delay estimation algorithm is adopted for the adaptive Smith predictor in the networked control systems (NCS) design under the significant delay time. However, the Smith predictor is sensitive to parameter uncertainties within the system model and time delay. The quantitative feedback theory (QFT) controller is suitable to cope with parameter uncertainties of the Smith predictor in this paper; moreover, a template expressed in phase and gain for the QFT design has been constructed by considering the bounded variations with the time delay and the external loading. Therefore, the adaptive Smith predictor control scheme with QFT controller provides better tolerance to parameter uncertainties. Both simulation and experimental results on AC servo motor with both the hybrid controller area network (CAN) bus and the Ethernet network have proven the feasibility and improved performance of the proposed NCS.

The distillation columns are widely used in various chemical industries. It is basically used for separating the components of a liquid mixture. The More volatile component is vaporized, and the less volatile one is left back in the liquid. The plant of distillation column that is considered in the present work is a SOPDT transfer function model imported from an online source. Due to the impact of present dead time, the stability in the output variable is a challenging task. So, the improved control performance is achieved by the dead time compensation using the Smith predictor. The PI control systems are designed, using MATLAB, for this plant with and without Smith predictor, and their performance is compared.

On the use of information quality in stochastic networked control systems

Markovian jumping systems have arisen naturally in the mathematical modeling of phenomena spanning disciplines in the social sciences, natural sciences, and engineering. This kind of stochastic dynamical systems can be employed to model the dynamics when parameters are subject to random abrupt changes due to sudden environment changes, subsystem switching, system noises, executor faults, and so forth. Much attention has been given to modeling, optimization, and real applications of such stochastic dynamical systems in the literature in recent years. As the advanced control and optimization will provide a basis for the design and application of such stochastic systems, these advanced techniques would result in substantial and sustainable benefits. The accepted papers in this special issue include stochastic stability, stabilization, stochastic control optimization, system modeling and identification methods, predictive control, signal processing, robust filtering, multiagent systems, networked control systems, time-delayed systems, neural networks, the Takagi-Sugeno fuzzy systems, simulated annealing, and fault detection methods.

To aim at the network delays in the networked control systems, an internal model control algorithm is proposed. This method makes that the network delays don't need to be measured, identified or estimated on line, and the nodes don't need the clock signal synchronization. Moreover, it adapts to the environment which has random, variant and uncertain delays, more than dozens of sampling periods, some data packets loss, the change of controlled plant in the networked control system. The results of the simulation show the validity of the control scheme, and indicate the system has better dynamic performance and robustness.

Networked Control Systems (NCS) form a key element of Cyber-Physical Systems (CPS) where sensors, actuators (plants) and controller functions are spatially distributed and interconnected by communication networks. In this contribution models are developed where significant properties of communication networks such as stochastic delays, error control protocols and shared-network influences are embedded in a closed-loop control system. The resulting integrated systems are analyzed by methods of classical system-theoretic and discrete-time state analysis methods as well as by computer simulations. Fundamental insights in the properties of NCSs can already be gained through system-theoretic studies on basic architectures which provide closed-form expressions which are verified and extended by more detailed studies based on tool-supported discrete-time system state analysis and simulation-tool results. The approach allows for a more detailed detection of network protocol impacts on, e.g., the real-time behavior of NCSs when certain Service Level Agreements (SLA) have to be guaranteed as prescribed percentiles of control reaction times.

Networked Control Systems (NCS) have revolutionized modern control engineering by facilitating distributed monitoring and operation across critical infrastructure domains. However, the integration of communication networks introduces a dual challenge: network-induced phenomena, such as time delays and packet losses, degrade operational performance, while exposure to sophisticated cyberattacks compromises system integrity and safety. This paper addresses these fundamental challenges through a unified Internal Model Control (IMC)-based framework that simultaneously achieves computational efficiency, systematic realizability, and intrinsic cyber-resilience. Three novel controller architectures are developed: (i) an optimization-free IMC-PID controller for Second-Order Plus Dead Time (SOPDT) systems utilizing maximum sensitivity analysis, delivering closed-form gain expressions with deterministic O ( 1 ) computational complexity; (ii) a physically realizable IMC-based Proportional-Integral-Derivative with Acceleration (PIDA) controller for Third-Order Plus Time Delay (TOPTD) systems, which systematically resolves the persistent realizability challenge through filtered derivative incorporation while preserving single-parameter tuning; and (iii) a cyber-resilient IMC-PID-II controller explicitly designed to mitigate Man-in-the-Middle (MitM) attacks in NCS through double integrator architecture, providing intrinsic resilience against malicious data injection attack. Comprehensive validation through high-fidelity simulations on benchmark processes, microgrid frequency control systems, and DC–DC converters, coupled with experimental verification using WAVECT WCU300 real-time controller, confirms superior transient response, disturbance rejection, parametric robustness, and cyber-attack resilience.

In this work, control structures for regulating mass flow in feeders in conveyor belt circuits are discussed, in mining processes, when the dead time is high. In this case, dead time consists of the difference between the instant of the event of change of feeder speed and the instant of the detection event of the effect of this change in the flow measurement scale of this equipment. The motivation of this work is the search to obtain an increase in operational safety, avoiding flow values above the nominal reference values of the equipment, and to achieve the optimal use of the capacity of the mass transport circuits. The following control strategies are evaluated: Proportional-Integral-Derivative (PID) controller, Internal Model Controller (IMC) and Smith predictor. It is concluded that the use of the structure with Smith predictor, with feed mass adherence of 99,26%, had the best performance.

A stable Kalman filter predictor (KFP) is developed which generates minimum variance estimates of the future outputs {y(t + i | t), i = 1, … d} of stochastic, single‐input/single‐output processes with time delay, d. The predicted outputs are used for time delay compensation and in the design of a predictive feedback controller. An innovation model analysis is used to convert the state space formulation to transfer function form and to show the relationship between the KFP, the Smith predictor, and the internal model controller. A modified KFP includes a disturbance model, and eliminates offset due to deterministic disturbances (e.g., steps) and modeling errors. Simulation results show that the modified KFP also predicts the disturbances and gives significantly better performance than the Smith predictor, particularly in the presence of process and measurement noise.

Iterative feedback tuning for internal model controllers