Networked Predictive Control Systems Research Articles

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.

Asynchronous update based networked predictive control system using a novel proactive compensation strategy

Networked predictive control system (NPCS) has been proposed to address random delays and data dropouts in networked control systems (NCSs). A remaining challenge of this approach is that the controller has uncertain information about the actual control inputs, which leads to the predicted control input errors. The main contribution of this paper is to develop an explicit mechanism running in the distributed network nodes asynchronously, which enables the controller node to keep informed of the states of the actuator node without a priori knowledge about the network. Based on this mechanism, a novel proactive compensation strategy is proposed to develop asynchronous update based networked predictive control system (AUBNPCS). The stability criterion of AUBNPCS is derived analytically. A simulation experiment based on Truetime demonstrates the effectiveness of the scheme.

Design and Performance Analysis of Incremental Networked Predictive Control Systems.

This paper is concerned with the design and performance analysis of networked control systems with network-induced delay, packet disorder, and packet dropout. Based on the incremental form of the plant input-output model and an incremental error feedback control strategy, an incremental networked predictive control (INPC) scheme is proposed to actively compensate for the round-trip time delay resulting from the above communication constraints. The output tracking performance and closed-loop stability of the resulting INPC system are considered for two cases: 1) plant-model match case and 2) plant-model mismatch case. For the former case, the INPC system can achieve the same output tracking performance and closed-loop stability as those of the corresponding local control system. For the latter case, a sufficient condition for the stability of the closed-loop INPC system is derived using the switched system theory. Furthermore, for both cases, the INPC system can achieve a zero steady-state output tracking error for step commands. Finally, both numerical simulations and practical experiments on an Internet-based servo motor system illustrate the effectiveness of the proposed method.

Design and analysis of networked non‐linear predictive control systems

This study is concerned with the control problem of networked non-linear systems with communication delay and data dropout. A networked non-linear predictive control scheme is proposed to compensate for communication delay and data dropout actively rather than passively. The stability analysis shows that the stability problem of the closed-loop networked non-linear predictive control system is converted to the one of a conventional non-linear control system without network. The simulations illustrate that the proposed scheme can compensate for communication delay and data dropout, and achieve the same control performance of the local closed-loop control system without network.

Hot Blast Stove Temperature Control System Based on Neural Network Predictive Control

Chain type coal-fired hot blast furnace boiler has a strong coupling, large delay, large inertia characteristics. Control effect of control method of mathematic modeling method and the classical routine of it is very difficult to produce the ideal. The predictive control theory combined with neural network theory. Through the model correction and rolling optimization control method of the system is good to overcome the effects of model error and time-varying process. The experimental results showed that neural network predictive control system is improved effectively the static precision and dynamic characteristic. It has better practicability of boiler temperature of this kind of large time delay system.

Model-based predictive controller design for a class of nonlinear networked systems with communication delays and data loss

This paper discusses the model-based predictive controller design of networked nonlinear systems with communication delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation results demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.

Design and Stability Analysis of Uncertain Networked Predictive Control Systems with Multiple Forward Channels

This paper is concerned with the design and stability of networked predictive control for uncertain systems with multiple forward channels. The delays and packet dropouts are distributed such that the classic networked predictive control (NPC) needs modifications to be implemented. An improved control signal selection scheme with distributed prediction length is proposed to increase the prediction accuracy and hence achieve better control performance. Moreover, stability analysis results are obtained for both constant and random cases. Interestingly, it is shown that the stability of the closed-loop NPC system is not related to the distributed delays when they are constant and the system model is accurate. Finally, a two-axis milling machine example is given to illustrate the effectiveness of the proposed method.

Brief Study of Identification System Using Regression Neural Network Based on Delay Tap Structures

Abstractthis investigation aims to provide an overview and investigations of non-linear identification system using neural network approach. Nowadays, a lot of neural network approach was done to provide a satisfying identification system. Backpropagation scheme as the mainstream approach for developing identification system has several limitations such as training data, computation time, architecture, optimization technique for weight value update, and many others. Regression Neural Network which is found by specht on 1990 contains more advantages compare with backpropagation scheme. With the improvement of computation time, architecture, and robustness of this model and provided 90% of effectiveness, promising a good prospective to develop non-linear identification system. For future works, it can be implemented in neural network predictive model control system and another control scheme based on identification system approach.

Design, Analysis and Real-time Implementation of Networked Predictive Control Systems

This paper is concerned with the design, analysis and real-time implementation of networked control systems using the predictive control strategy. The analysis of the characteristics of networked control systems is detailed, which shows that a networked control system is much different from conventional control systems. To achieve the desired performance of closed-loop networked control systems, the networked predictive control scheme is introduced. The design, stability analysis and real-time implementation of networked predictive control systems are studied. It is illustrated by simulations and practical experiments that the networked predictive control scheme can compensate for random network communication delay and data dropout, achieve desired control performance and have good closed-loop stability.

Observer-based tracking controller design for networked predictive control systems with uncertain Markov delays

This paper is concerned with a tracking controller design problem for discrete-time networked predictive control systems. The control law used here is a combined state-feedback control and integral control. Since not all the states are available in practice, a local Luenberger observer is utilised to estimate the state vector. The measured output and estimated state vector are packed together and transmitted to the tracking controller via a communication channel with a limited capacity. Meanwhile, the control signal is also transmitted through a communication network.Network-induced delays on both links are considered for the signal transmission and modelled by Markov chains. Moreover, it is assumed that the elements in Markov transition matrices are subject to uncertainties. In order to fully compensate for network-induced delays, the controller generates a sequence of control signals which are dependent on each possible delay in the feedforward channel. By taking the augmentation twice, we obtain delay-free stochastic closed-loop systems and the controlled output is chosen as the tracking error. Sufficient conditions are provided for the energy-to-peak performance of the closed-loop systems. The feedback gains of the controller can be derived by solving a minimisation problem. Two examples are illustrated to demonstrate the effectiveness of the proposed design method.

Design of neural network-based control systems for active steering system

Nowadays, safety of road vehicles is an important issue due to the increasing road vehicle accidents. Passive safety system of the passenger vehicle is to minimize the damage to the driver and passenger of a road vehicle during an accident. Whereas an active steering system is to improve the response of the vehicle to the driver inputs even in adverse situations and thus avoid accidents. This paper presents a neural network-based robust control system design for the active steering system. Primarily, double-pinion steering system used modeling of the active steering system. Then four control structures are used to control prescribed random trajectories of the active steering system. These control structures are as classical PID Controller, Model-Based Neural Network Controller, Neural Network Predictive Controller and Robust Neural Network Predictive Control System. The results of the simulation showed that the proposed neural network-based robust control system had superior performance in adapting to large random disturbances.

Performance analysis of networked predictive control systems with data dropout

SUMMARYThis paper is concerned with the design of networked control systems with random network data dropout. It presents a new control scheme, which is termed networked predictive control. This scheme mainly consists of the control prediction generator and network data dropout compensator. Besides, the control prediction generator provides a set of future control predictions to make the closed‐loop system achieve the desired control performance, and the network data dropout compensator removes the effects of the network transmission data dropout. Simulation results are presented to illustrate the effectiveness of the control strategy via comparing with other three existing control schemes. Copyright © 2012 John Wiley & Sons, Ltd.

Design and Implementation of Secure Networked Predictive Control Systems Under Deception Attacks

This brief addresses the security issues of data transmitted in networked control systems (NCSs), especially confidentiality, integrity and authenticity. A secure networked predictive control system (SNPCS) architecture is presented, which integrates the Data Encryption Standard (DES) algorithm, Message Digest (MD5) algorithm, timestamp strategy, and recursive networked predictive control (RNPC) method. The former three parts are used to form a secure transmission mechanism between the controller side and the plant side, which is responsible for enforcing the data confidentiality and checking the data integrity and authenticity. To guarantee the control system performance when suffering from deception attacks, the RNPC method based on round-trip time delays is proposed to compensate for the adverse effects introduced by the deception attacks as well as the network communication constraints, such as time-varying network delay, packet disorder and packet dropout. A theoretical result using the switched system theory is obtained for the closed-loop stability of the RNPC system. Practical experiments are performed to demonstrate the effectiveness of the proposed SNPCS.

Design of Robust Networked Predictive Control Systems with Packet Loss

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.

Fault Detection and Compensation for Linear Systems Over Networks With Random Delays and Clock Asynchronism

This paper proposes a fault detection and compensation scheme based on likelihood ratios (LRs) for networked predictive control systems with random network-induced time delays and clock asynchronism. The compensator is applied to compensate for the network-induced time delays. The measured outputs are sent back to the local node with random delays and the observer updates based on the time schedule of a remote node clock to avoid the deficiency of asynchronism. Two schemes are proposed in this paper to update the LRs of fault. One of them is to set up a buffer in the local node to save the measured outputs out of sequence, and the observer processes the measured outputs one by one in their original sequence. The other scheme is to discard the measured outputs that are out of sequence; thus, the observer has to estimate the state and update the LRs with intermittent observations. The convergence analysis of a generalized LR test with intermittent observations is proposed as well. A numerical simulation is also given to validate the proposed method.

H ∞ predictive control of networked control systems

This article is concerned with the problem of H ∞ predictive control of networked control system with random network delay. A new control scheme termed networked predictive control is proposed. This scheme mainly consists of the control prediction generator and network delay compensator. While designing the predictor, the control input to the actuator may be different due to networked induced time-delay and data dropout, and two cases are considered depending on the way that the observer obtains the plant control input u t . The necessary and sufficient conditions are given for the closed-loop networked predictive control system to be stochastically stable for different u t and random network delays in controller to actuator channel (CAC) and sensor to controller channel (SCC). A simulation study shows the effectiveness of the proposed scheme.

Modified Generalized Predictive Control of Networked Systems With Application to a Hydraulic Position Control System

This paper is concerned with the design of networked control systems using the modified generalized predictive control (M-GPC) method. Both sensor-to-controller (S-C) and controller-to-actuator (C-A) network-induced delays are modeled by two Markov chains. M-GPC uses the available output and prediction control information at the controller node to obtain the future control sequences. Different from the conventional generalized predictive control in which only the first element in control sequences is used, M-GPC employs the whole control sequences to compensate for the time delays in S-C and C-A links. The closed-loop system is further formulated as a special jump linear system. The sufficient and necessary condition to guarantee the stochastic stability is derived. Simulation studies and experimental tests for an experimental hydraulic position control system are presented to verify the effectiveness of the proposed method.

Predictive Controller Design of Networked Systems With Communication Delays and Data Loss

This brief studies the predictive controller design of networked systems with communication delay and data loss. A networked predictive control scheme is employed to compensate for communication delay and data loss actively rather than passively. Based on analysis of the closed-loop networked predictive control systems, a design strategy of the predictive controller is proposed. The designed predictive controller can achieve the desired control performance and also guarantee the system stability. A numerical example demonstrates the compensation for communication delay and data loss in networked systems using the proposed predictive controller design strategy.

Predictive control of networked systems with random delay and data dropout

This study is concerned with the stochastic stability analysis of networked control systems with random network delay. A new control scheme termed networked predictive control is proposed. This scheme mainly consists of the control prediction generator and network-delay compensator. The control prediction generator provides a set of future control predictions to make the closed-loop system achieve the desired control performance and the network-delay compensator removes the effects of the network transmission delay and data dropout. The stochastic stability criteria of the closed-loop networked predictive control systems are derived. A numerical example and a practical experiment are given to show the potential of the proposed techniques.

L 2-gain analysis for networked predictive control systems based on switching method

This article deals with the problem of L 2-gain for a class of networked control systems with time-varying network delay in both forward and feedback channels. An improved network predictive control scheme is proposed to compensate the effects of network delay and data dropout using a switching control strategy. Based on the average dwell time method, the restrictions that the original delay-compensation strategy imposes on the subsystems are relaxed and a sufficient condition for weighted L 2-gain is developed for a class of switching signal. An example illustrates the effectiveness of the proposed method.