Networked Predictive Control Systems Research Articles

Dynamic event-triggered delay compensation control for networked predictive control systems with random delay

This paper aims to investigate the dynamic event-triggered control problem for networked predictive control systems with random delays and disturbance. First, a discrete-time dynamic event-triggered control scheme, in which sensor information is only updated when it is necessary, is presented. Next, the systems are modeled as a time-delay singular Markovian jump systems with time-varying switching. Then, a dynamic event-triggered delay compensation control strategy is proposed. Sufficient conditions guaranteeing the asymptotically stable are derived based on the Lyapunov–Krasovskii functional method together with the linear matrix inequality (LMI) technique. Finally, simulation results verify the effectiveness of the proposed strategy.

A fuzzy based intelligent scheme for enhancing the performance of the optimal controllers by online weighting matrix selection in seismically excited nonlinear buildings

In the present work, an effective strategy is proposed to improve the efficiency of the optimal controllers that are based on minimizing a performance index by using a fuzzy controller. In the used fuzzy system, the weighting matrices of the optimal controller are tuned online according to the response of the structural system. In order to assess the effectiveness of the proposed control scheme, its function is investigated in two phases on a 3-story nonlinear benchmark building equipped with MR dampers. In the first phase, a linear quadratic Gaussian (LQG) controller is combined with a fuzzy logic system (Fuzzy-LQG) and the efficiency of the used technique is compared with three different cases of the conventional LQG controllers. The results show that the Fuzzy-LQG, in addition to using an optimal level of demand energy, has a better performance than the conventional LQG methods in controlling the maximum responses of the structure. In the second phase, the performance of a polynomial optimal controller that is a summation of polynomials in nonlinear states in combination with the fuzzy system is investigated. At this stage, the effectiveness of the designed control system (Fuzzy Polynomial) in terms of seismic performance criteria with the results of a conventional LQG active control system, an optimal adaptive neuro-fuzzy inference system (ANFIS) and a neural network predictive control (NNPC) system is evaluated. The results show that this method is successful in improving the structural performance of a nonlinear building subjected to earthquakes.

Neural network predictive control applied to automotive paint drying and curing processes

In the automotive painting process, maintaining optimal operating conditions in drying and curing ovens is crucial to ensure high-quality paint finishes, particularly for specific car body-in-white (BIW) parts' temperature profiles. This paper presents a methodology for developing and implementing a Neural Network Predictive Control (NNPC) system for painting drying and curing processes in an automotive oven used in the electrodeposition stage (Elpo oven). The objective is to enhance temperature control for BIW parts. To train the Artificial Neural Networks (ANN) in the various zones of the Elpo oven, a dataset was generated using a phenomenological model based on first principles. Random disturbances were applied to the input variables (conveyor speed and zone temperatures) to capture the dynamic response of the output variables (temperature in specific BIW parts). The dataset was split into training (80 %), validation (10 %), and test (10 %) sets, and the ANN training was performed using the backpropagation algorithm. The implemented NNPC utilizes the trained ANNs to predict future temperature values in specific BIW parts (controlled variables). To determine the optimal control signals (manipulated variables), an optimizer based on the Generalized Predictive Control (GPC) model was employed, and the objective function was minimized using the Ant Colony Optimization Algorithm (ACO). Through simulations of four operational scenarios with applied disturbances, the results demonstrate satisfactory performance of the NNPC, effectively maintaining controlled temperatures of BIW parts close to predefined setpoints. The utilization of NNPC offers improved temperature control for BIW parts, mitigating painting issues, reducing rework and operating costs, while ensuring painting quality is uncompromised.

Development and Simulation of a Fast Delay Cancellation Algorithm for Digital Multimedia Network Communication

The digital multimedia network data traffic is huge, and many end devices are applied to the digital multimedia network; therefore, the digital media network communication delay transmission must be analysed in order to improve the performance of the digital multimedia network communication transmission. Due to the presence of a large number of data packets in the communication network used, some random delays in data can occur during use. Conventional data transmission methods rely solely on extended data storage capacity to achieve this, making it difficult to achieve intelligent scheduling, resulting in delays and optimized performance degradation during communication data transmission. The aim of this paper is to enhance the communication quality of digital multimedia networks by developing an algorithmic model that rapidly mitigates communication delays. The proposed approach employs a delay cancellation algorithm in a network predictive control system. Simulation techniques are utilized to simulate and compensate for delays in multimedia communication networks. The efficacy of the network control strategy is demonstrated through simulation tests on a large-scale multimedia network communication system. The research findings indicate that the network control prediction model and elimination algorithm can substantially reduce the packet loss rate and quickly eliminate communication delays in digital multimedia networks.

Output Tracking Predictive Control of Networked Systems with Two-channel Random Communication Constraints

Output Tracking Predictive Control of Networked Systems with Two-channel Random Communication Constraints

Predictive output feedback control of networked control system with Markov DoS attack and time delay

SummaryThis paper studies the networked control systems (NCS) predictive output feedback control issue with random time delay and denial of service (DoS) attacks. The mathematical description of the random DoS attacks and time delays in controller‐to‐actuator (C‐A) and sensor‐to‐controller (S‐C) communication channels is researched based on Markov stochastic theory. Considering the random characteristics of network‐induced attacks and delays, the closed‐loop NCS is modeled as a discrete‐time linear system with Markov DoS attack and time delay. The stochastic stability criterion of a class of networked predictive control systems with random attack and delay is proposed based on Lyapunov stability theory and the predictive compensation control method. The networked model predictive output feedback controller is designed by applying the parameter compensation method and linear matrix inequality (LMI) technique. Numerical simulation results are proposed to verify the effectiveness of the proposed control strategy.

Event-based Networked Predictive Control Systems with Secure Transmission Protocol

Event-based Networked Predictive Control Systems with Secure Transmission Protocol

Finite-time analysis for networked predictive control systems with induced time delays and data packet dropouts

This paper is concerned with the problem of finite-time stability (FTS) and finite-time boundedness (FTB) issues of networked predictive control systems (NPCs) with induced time delays and data packet dropouts. First, a new networked predictive control (NPC) scheme is established based on the designed observer and prediction strategy, and a model of NPCs is obtained. Second, in order to handle different time delays and data packet dropouts, an improved method of combining matrix singular value decomposition (SVD) with multivariate presupposition is proposed. Third, an observer-based predictive controller is designed to make the closed loop system FTS and FTB via finite-time theory, then the induced time delays and data packet dropouts are disposed actively. In addition, new sufficient conditions are obtained, which can guarantee the desired controller and observer gain matrices be subsequently acquired by solving some linear matrix inequalities (LMIs). Finally, two numerical examples are illustrated to validate the effectiveness of the theoretical findings.

Event-Triggered Predictive Control for Networked Systems Based on a Dual-Rate Sampling Switched Observer

This paper is concerned with predictive control of discrete-time networked control systems (NCSs) based on a dual-rate sampling switched observer, where the multiple output signals of the controlled plant are sampled using a dual-rate sampling mechanism. To reduce the effect of the bilateral network-induced uncertainties on NCSs, a novel predictive control protocol is proposed to compensate for the bilateral network-induced delays and packet dropouts. First, a dual-rate sampled-data-based switched observer with a fixed switching law is designed to estimate the state of the controlled plant, where the switching law is determined by the dual-rate sampled instants. Second, an estimated-state-based predictive controller is designed by synchronizing the control inputs used by the state predictor and the controlled plant, and the networked predictive control system is formulated as a threshold-error-dependent delay system model, where two associated but different event-triggered transmission schemes are introduced to determine whether the estimated states or the control packet should be transmitted or not. Sufficient conditions for co-designing the event-triggered matrix and the predictive controller gain are derived by using the Lyapunov-Krasovskii functional method. Finally, it is shown through a numerical example that the proposed method in this paper is effective for ensuring better system performance.

Model predictive position tracking control for motion system with random communication delay

This study focuses on position tracking control for the networked predictive motion control system with random communication delay. First, the output feedback controller is designed by networked predictive control law to actively compensate the time delay induced by the random channels of the motion control system. A closed-loop model is established for the networked predictive motion control system with random bounded communication delay, modelled by a Markov chain. Then, the sufficient conditions of stability for the networked predictive motion control system are provided, by constructing the Lyapunov–Krasovskii functional, followed by the theoretical proof. Furthermore, the output feedback controller is constructed and the linear matrix inequality method is applied to obtain the designed controller gain. Last, the simulation and experimental results are presented to prove the effectiveness of the proposed method.

Model-based event-triggered dynamic output predictive control of networked uncertain systems with random delay

ABSTRACTThis paper explores a dynamic output predictive control issue for networked systems with random delays and uncertainties. Based on the networked predictive control systems, an adaptive event-triggered dynamic output predictive control strategy is presented. And a networked delay compensator method is employed to compensate the communication delay actively. Furthermore, the problem we addressed can be transformed into the delay-dependent stability and stabilisation problem of networked control systems. Finally, the Lyapunov–Krasovskii functional approach and linear matrix inequality method are introduced to solve the expected solution. Both the results of numerical simulations and experiment are used to verify the effectiveness of the proposed method.

Networked Multi Pipeline Pressure Synchronous Coordinated Predictive Control based on the Model Free Adaptive Method

In order to achieve the rapid and accurate synchronous coordination of multi pipeline pressure systems using networked control system theory, an adjacent cross-coupling structure is adopted to improve the synchronisation performance. Considering the possible limitations of network communication, a networked predictive control algorithm based on model free adaptive control is proposed to compensate the delay caused by network. The above two strategies are combined to form the networked multi pipeline pressure synchronous coordination predictive control systems. The simulation experiments of synchronous coordinated control of three pipeline pressure systems are carried out on Simulink platform. The simulation results show that the proposed control scheme can compensate the network delay, and improve the dynamic and synchronous performance. The effectiveness of the control scheme is verified.

A Communication Delay Compensation Method Applied to ACLs for Distributed Energy Consumption

With the development of smart power systems, it has become possible to adjust the operating status of aggregated air conditioning loads (ACLs) to make them trace the commands for distributed energy sources consumption. However, inevitable communication delay causes conspicuous control error. On the basis of analyzing control error of ACLs caused by communication delay, this paper proposes a communication delay compensation method based on network predictive control system. Then, aggregated ACLs are applied to consume the distributed energy sources in a microgrid, and several simulations demonstrate the compensation method proposed in this paper. The simulation results show that this method can diminish the control error caused by random communication delay to a large extent.

Modeling and Control of Air Conditioning Loads for Consuming Distributed Energy Sources

This paper aims to tap the potential of air conditioning loads (ACLs) for consuming photovoltaic power (PV) and wind power (WP). By fully considering different thermal comfort of different users, an ACL twice-clustered model based on different ACL parameters and users tolerance values (UTV) is built. Then, a two-stage ACL control method based on both temperature control (TC) and switch control (SC) is proposed, which achieves rapid control of ACLs as well as diminishing users’ discomfort. Widely existent communication time delay in ACL control network causes obvious control error, which leads to ACL consumption deviation from the target. Therefore, on the basis of analyzing errors and impacts of ACLs caused by communication time delay, this paper proposes a time delay compensation method based on a network predictive control system. Applying the ACLs clustered model and the control method into consuming PV and WP, a dual-stage consumption model considering communication time delay is established. Simulations of the PV and WP consumption effects based on ACLs clusters are conducted, and the influence of SC cycle and outdoor temperature on the simulation results are analyzed. The simulation results demonstrate the validity of the model and the methods proposed in this paper, showing a strong adaptability in different circumstances.

Predictive Control of Networked Multiagent Systems via Cloud Computing.

This paper studies the design and analysis of networked multiagent predictive control systems via cloud computing. A cloud predictive control scheme for networked multiagent systems (NMASs) is proposed to achieve consensus and stability simultaneously and to compensate for network delays actively. The design of the cloud predictive controller for NMASs is detailed. The analysis of the cloud predictive control scheme gives the necessary and sufficient conditions of stability and consensus of closed-loop networked multiagent control systems. The proposed scheme is verified to characterize the dynamical behavior and control performance of NMASs through simulations. The outcome provides a foundation for the development of cooperative and coordinative control of NMASs and its applications.

Design and performance analysis of networked predictive control systems based on input-output difference equation model

This paper is concerned with the design and performance analysis of networked control systems, where random network-induced delay, packet disorder, and packet dropout in the feedback and forward channels are considered simultaneously and further treated as the round-trip time (RTT) delay. To actively compensate for the RTT delay, a networked predictive control scheme is designed based on the input-output difference equation model. For time-varying reference signals, the resulting closed-loop system can achieve the same output tracking performance and closed-loop stability as the corresponding local control system. Specifically, for the step reference input, it can provide a zero steady-state output tracking error. The controller design problem is solved by using the augmented state-space model as well as the static output feedback strategy. In addition, the stability of the closed-loop system is also discussed for the plant subject to bounded disturbances and modelling errors. Finally, simulation and experimental results are given to demonstrate the effectiveness of the proposed method.

Consensus and Stability Analysis of Networked Multiagent Predictive Control Systems

This paper is concerned with the consensus and stability problem of multiagent control systems via networks with communication delays and data loss. A networked multiagent predictive control scheme is proposed to achieve output consensus and also compensate for the communication delays and data loss actively. The necessary and sufficient conditions of achieving both consensus and stability of the closed-loop networked multiagent control systems are derived. An important result that is obtained is that the consensus and stability of closed-loop networked multiagent predictive control systems are not related to the communication delays and data loss. An example illustrates the performance of the networked multiagent predictive control scheme.

Event-triggered networked predictive control of system with data loss

This paper investigates the problem of event-triggered networked predictive control for systems with data loss. An event-triggered networked predictive control system is proposed. Based on predictive control model, a data loss compensation strategy is presented and an extended event-triggered transmission mechanism is developed. The closed-loop event-triggered predictive control system is described as a switched system and sufficient closed-loop stability conditions related to event-triggered mechanism are established. Under the event-triggered networked predictive control scheme, the consumption of the communication resources is reduced. Finally, an example is provided to illustrate the effectiveness of the proposed method.

Model-Based Event-Triggered Predictive Control for Networked Systems with Data Dropout

This paper proposes a novel structure of networked control systems (NCSs) with communication logic, which incorporates model-based networked control systems (MB-NCSs), predictive control, and an event-triggered communication scheme into a unified framework to consider the bandwidth reduction of the network communications. Within this framework, first an event-triggered communication scheme at the sensor side is introduced to determine whether or not the sensor measurement should be transmitted to the controller through the imperfect forward paths. Second, at the controller, a model of the plant is used to predict future state behavior of the plant, and based on the predicted state between successful transmission instants, a novel predictive event-triggering scheme is proposed to compress the size of the packetized control signals transmitted from the controller side to the actuator side through feedback paths. Finally, a unified model of NCSs is established. Based on this model, a codesign condition of th...

A necessary and sufficient stability criterion for networked predictive control systems

Stability of a networked predictive control system subject to network-induced delay and data dropout is investigated in this study. By modeling the closed-loop system as a switched system with an upper-triangular structure, a necessary and sufficient stability criterion is developed. From the criterion, it also can be seen that separation principle holds for networked predictive control systems. A numerical example is provided to confirm the validity and effectiveness of the obtained results.