Nonlinear Networked Control Systems Research Articles

Dynamic event‐triggered dissipative control of nonlinear networked control systems with deception attacks

AbstractFor nonlinear networked control systems with deception attacks, the problem of dynamic event‐triggered dissipative control is researched in this article. First, on the basis of nonlinear networked control systems model, a dynamic event triggered scheme is proposed to save impact of network bandwidth. Second, considering the impact of time delay, a networked time‐delay system model under deception attacks is established. Third, by constructing a new and less conservative Lyapunov function, using linear matrix inequality and unilateral Lipschitz condition, we derive some new sufficient conditions to guarantee the asymptotically stability of nonlinear networked control systems and strictly ‐dissipative performance. Besides, a cooperative design strategy of dynamic event triggered scheme parameters and controller gain is put forward. Finally, a simulation example is given to prove feasibility and rationality of the proposed strategy.

Event‐triggered model‐free adaptive predictive control for multiple‐input and multiple‐output networked control systems under deception attacks

AbstractThe event‐triggered model‐free adaptive predictive control (ET‐MFAPC) problem for a class of multiple‐input and multiple‐output nonlinear networked control systems (NCSs) with network delay under deception attacks is addressed in this article. First, a data driven MFAPC strategy with adaptive predictive gain is proposed by transforming the NCSs into an equivalent data model via dynamic linearization technology and designing controller only with I/O information rather than the system model. Meanwhile, an ET mechanism is applied to reduce the amount of data transmission under the limited network bandwidth. Further, the boundedness and convergence of tracking error is rigorous proved through the contraction mapping principle. Finally, the theoretical results are demonstrated through detailed simulations.

An improved event-triggered control method for fuzzy systems under membership functions online learning

This paper is concerned with the problem of adaptive event-triggered (AET) based optimal fuzzy controller design for nonlinear networked control systems (NCSs) characterized by Takagi–Sugeno (T–S) fuzzy models. An improved AET communication scheme with a memory adaptive rule is proposed to enhance the utilization of the state response vertex data. Different from the existing ET based results, the improved AET scheme can save more communication resources and acquire better system performance. The sufficient criteria of performance analysis and controller design are presented for the closed-loop control system subject to mismatched membership functions (MFs) and AET scheme. And then, a new MFs online learning algorithm on the basis of the gradient descent approach is employed to optimize the MFs of fuzzy controller and obtain optimal fuzzy controller for further improving system performance. Finally, two simulation examples are presented to verify the advantage and effectiveness of the provided controller design technique.

Observer-based dynamic event-triggered secure control for nonlinear networked control systems with false data injection attacks

This paper considers the problem of observer-based dynamic event-triggered secure control for nonlinear networked control systems (NCSs) under false data injection attacks (FDIA). A novel output feedback controller is developed by utilizing a sampled-data observer to relieve the impacts of unmeasured states and FDIA. To make full use of the limited network resources, a dynamic periodic event-triggered mechanism (PETM) with an auxiliary variable is designed, which can reduce the transmission of information. Then, an approximation of injected false data is implemented based on an adaptive neural network (NN) in the presence of FDIA. Based on the proposed control strategy, the boundness of the nonlinear NCSs can be ensured by selecting some proper parameters. Finally, the effectiveness and validity of the proposed control strategy are illustrated via simulation of a single link robot arm system.

Transmission Power Policies for Energy-Efficient Wireless Control of Nonlinear Systems

We present an emulation-based controller and transmission policy design procedure for nonlinear wireless networked control systems. The objective is to ensure the stability of the closed-loop system, in a stochastic sense, together with given control performance, while minimizing the average power used for communications. The controller is designed by emulation, i.e., ignoring the network, and the transmission power is given by threshold policies. These policies involve waiting a given amount of time since the last successful transmission instant, as well as requiring that the measured wireless channel gain is above a given threshold, before attempting a new transmission. Two power control laws are investigated: i) a constant power and ii) a power level inversely proportional to the channel gain. We explain how to select the waiting time, the channel threshold and the power level to minimize the induced average communication power, while ensuring the desired control objectives.

Extended Disturbance-Observer-Based Data-Driven Control of Networked Nonlinear Systems With Event-Triggered Output

This article is dedicated to data-driven control of networked nonlinear systems with event-triggered output. An improved extended state observer is constructed to estimate unknown disturbances. An output estimator is built on the triggered output and the estimated output. Consequently, triggering conditions for single-input single-output and multiple-inputs and multiple-outputs systems are individually proposed by integrating the estimated disturbances, the true and the estimated tracking errors. Sufficient conditions are established to guarantee that the resultant tracking error systems are uniformly ultimately bounded. The proposed strategies are verified by illustrative numerical examples.

Learning-Based Event-Triggered Tracking Control for Nonlinear Networked Control Systems With Unmatched Disturbance

This article concentrates on optimal tracking control for a class of nonlinear networked systems subjecting to limited network bandwidth and unmatched disturbance. Given the models of the control and reference systems, the considered optimal tracking control issue is initially formulated as a minimax optimization problem. Then, with the introduction of an event-triggered mechanism used for saving bandwidth, the formulated problem is transformed into solving an event-based Hamilton-Jacobi-Isaacs (HJI) equation by recurring to the Bellman optimality theory. Based on the HJI equation, we demonstrate that the stability of the concerned system in the sense of uniformly ultimately bounded (UUB) can be guaranteed with the envisioned optimal control and worst disturbance policies. Here, the disturbance policy can be varied periodically while the control policy can only be updated at event-triggering instants, which differs from the existed researches. Furthermore, we propose a reinforcement learning (RL)-based algorithm to handle the constructed HJI equation and thus settle the studied tracking control problem. The effectiveness of the algorithm is finally validated by both theoretical analysis and simulations.

Fixed-Time Adaptive Neural Network Control for Nonlinear Systems With Input Saturation.

This study concentrates on the tracking control problem for nonlinear systems subject to actuator saturation. To improve the performance of the controller, we propose a fixed-time tracking control scheme, in which the upper bound of the convergence time is independent of the initial conditions. In the control scheme, first, a smooth nonlinear function is employed to approximate the saturation function so that the controller can be designed under the framework of backstepping. Then, the effect of input saturation is compensated by introducing an auxiliary system. Furthermore, a fixed-time adaptive neural network control method is given with the help of fixed-time control theory, in which the dynamic order of controllers is reduced to a certain extent since there is only one updating law in the entire control design. Through rigorous theoretical analysis, it is concluded that the proposed control scheme can guarantee that: 1) the output tracking error can converge to a small neighborhood near the origin in a fixed time and 2) all signals in the closed-loop system are bounded. Finally, a numerical example and a practical example based on the single-link manipulator are provided to verify the effectiveness of the proposed method.

Observer-based model predictive control for uncertain NCS subject to hybrid attacks via interval type-2 T–S fuzzy model

In this study, an observer-based model predictive control (MPC) algorithm is addressed for an uncertain discrete-time nonlinear networked control system (NCS) subject to hybrid malicious attacks by using interval type-2 Takagi–Sugeno (IT2 T–S) fuzzy theory. Hybrid malicious attacks, including two typical attacks, i.e., denial-of-service (DoS) attacks and false data injection (FDI) attacks, are considered in the communication networks. Under DoS attacks, the control signals will be interfered, which cause the degradation of signal-to-interference-plus-noise ratio, then lead to packets loss. Under FDI attacks, the false signals are injected and output signals are modified so that the system performance is deteriorated. For the NCS subject to hybrid attacks, a secure observer that can resist FDI attacks is devised and a fuzzy MPC algorithm that can solve the controller gains is proposed. Besides, by updating the bound of augmented estimation error, the recursive feasibility can be guaranteed. Finally, illustrative examples are given to show the effectiveness of proposed scheme.

Dual-Thresholds Event-Triggered Based H∞ Control for Nonlinear Networked Control Systems with Imperfect Communication Channels

Dual-Thresholds Event-Triggered Based H∞ Control for Nonlinear Networked Control Systems with Imperfect Communication Channels

Data‐based learning control for optimization of nonlinear systems

Data‐based learning control for optimization of nonlinear systems

Event-Based Fuzzy Tracking Control for Nonlinear Networked Systems Subject to Dynamic Quantization

This paper investigates the problem of event-based output feedback tracking control for discrete-time nonlinear networked systems with dynamic quantization. The Takagi-Sugeno (T-S) fuzzy systems theory is utilized to approximate the investigated nonlinear systems. Three general dynamic quantizers and an improved asynchronous event-triggering communication scheme are carried out to comprehensively decrease the amount of data in the communication of network and realize the rational utilization of limited communication resources. The objective is to design an event-based static output feedback tracking controller such that, in the presence of dynamic quantization, the closed-loop system can be asymptotically stabilized, and the tracking error achieves the predefined tracking performance. Moreover, the parameters for the desired dynamic quantizers and tracking controller can be obtained simultaneously by solving a set of linear matrix inequalities. Finally, the simulation responses are provided to demonstrate the validity of the proposed tracking control strategy.

Non-Fragile Fuzzy Tracking Control for Nonlinear Networked Systems with Dynamic Quantization and Randomly Occurring Gain Variations

This paper investigates the observer-based non-fragile output feedback tracking control problem for nonlinear networked systems with randomly occurring gain variations. The considered nonlinear networked systems are represented by a Takagi–Sugeno (T–S) fuzzy model. The dynamical quantization methodology is employed to achieve the reasonable and efficacious utilization of the limited communication resources. The objective is to design the observer-based non-fragile output feedback tracking controller, such that the resulting system is mean-square asymptotically stable with the given H∞ tracking performance. Based on the descriptor representation strategy combined with the S-procedure, sufficient conditions for the existence of the desired dynamic quantizers and observer-based non-fragile tracking controller are proposed in the form of linear matrix inequalities. Finally, simulation results are provided to show the effectiveness of the proposed design method

Model compression optimized neural network controller for nonlinear systems

Neural network-based controllers are widely used within the domain of robotic control systems. A network controller with more neurons typically achieves better performance, but an excessive number of neurons may make the model computationally intensive, resulting in slow dynamic responses in real-world environments. This paper reports a network compression method that integrates knowledge distillation technology for the development of concise neural network-based controllers to achieve a balance between the control performance and computational costs. The method first trains a full-size teacher model, which is then pruned, leading to a concise network with a minimum compromise of performance. From in this study, the resulting concise network is considered to be the prototype of a student model, which is further trained by a knowledge distillation process. The proposed compression method was applied to three classical networks, and the resultant compact controllers were tested on a robot manipulator for efficacy and potential demonstration. The experimental results from a comparative study confirm that the student models with fewer neurons resulting from the proposed model compression approach can achieve similar performance to that of the teacher models for intelligent dynamic control but with faster convergence speed.

Quantized Synchronization Control of Networked Nonlinear Systems: Dynamic Quantizer Design With Event-Triggered Mechanism.

This article investigates the quantized control issue for synchronizing a networked nonlinear system. Due to limited energy and channel resources, the event-triggered control (ETC) method and input quantization are simultaneously taken into account in this article. First, a dynamic quantizer, which discretely adjusts its parameters online and possesses a finite quantization range, is introduced to achieve exact synchronization, rather than quasisynchronization. Next, a new distributed Zeno-free ETC strategy is proposed based on the dynamic quantizer. Then, two different situations, that is, the quantizer is designed with/without the network topology information, are, respectively, discussed. Synchronization criteria are, respectively, derived under such two circumstances by using the Lyapunov method. Finally, numerical examples are provided to show the effectiveness of the theoretical results.

Output Feedback Stabilization of Nonlinear Strict-feedback Networked Control Systems

Design of output feedback semiglobally practically input-to-state (SPIS) stabilizing controllers for nonlinear strict-feedback networked systems is considered. We first design continuous-time state feedback globally asymptotically (GA) stabilizing controllers and discrete-time reduced-order observers and combine the emulation of state feedback controllers and the designed observers to construct discrete-time output feedback controllers. Then we show that the designed controllers SPIS stabilize nonlinear strict-feedback networked control systems when transmission intervals are sufficiently small and communication networks satisfy suitable assumptions. A numerical example is also given to illustrate the efficiency of proposed design of controllers.

Self-triggered output feedback control for nonlinear networked control systems based on hybrid Lyapunov functions*

Most approaches for self-triggered control (STC) of nonlinear networked control systems (NCS) require measurements of the full system state to determine transmission times. However, for most control systems only a lower dimensional output is available. To bridge this gap, we present in this paper an output-feedback STC approach for nonlinear NCS. An asymptotically stable observer is used to reconstruct the plant state and transmission times are determined based on the observer state. The approach employs hybrid Lyapunov functions and a dynamic variable to encode past state information and to maximize the time between transmissions. It is non-conservative in the sense that the assumptions on plant and controller are the same as for dynamic STC based on hybrid Lyapunov functions with full state measurements and any asymptotically stabilizing observer can be used. We conclude that the proposed STC approach guarantees asymptotic stability of the origin for the closed-loop system.

A Multi-Processor Implementation for Networked Control Systems

We study nonlinear networked control systems (NCS), where the controller is implemented over multiple processors via an emulation-based approach. We start with a stable and centralised NCS commonly considered in the literature. Then, we show how to implement the centralised controller over multiple processors inspired by parallel computing techniques, so that stability is preserved (semi-globally and practically) under sufficiently fast computations. An example is given to illustrate the main results.

H∞ Control for the Nonlinear Markov Networked Control System in the Presence of Data Packet Loss and DoS Attacks via Observer

The H∞ control issue for nonlinear Markov networked control systems in the presence of data packet loss and periodic denial‐of‐service (DoS) attacks is researched in this paper. First, two Bernoulli random variables are used to describe the packet loss between sensor to controller and controller to actuator. Considering the impact of DoS attacks, the probability of packet loss is set to be different during the attack sleeping interval and the attack active interval. Secondly, an observer is constructed on the controller side, and a comprehensive mathematical model including packet loss and DoS attacks is established. The sufficient conditions for the stochastic stability of the system are derived by the Lyapunov theory, and the design method of the controller and the minimum disturbance suppression performance index are also provided. Finally, a numerical example is utilized to reveal the applicability of the approach.

Dynamically Triggering Resilient Control for Networked Nonlinear Systems under Malicious Aperiodic DoS Attacks

Networked nonlinear systems (NNSs) have great potential security threats because of malicious attacks. These attacks will destabilize the networked systems and disrupt the communication to the networked systems, which will affect the stability and performance of the networked control systems. Therefore, this paper aims to deal with the resilient control problem for NNSs with dynamically triggering mechanisms (DTMs) and malicious aperiodic denial-of-service (DoS) attacks. To mitigate the impact from DoS attacks and economize communication resources, a resilient dynamically triggering controller (RDTC) is designed with DTMs evolving an adaptive adjustment auxiliary variable. Thus, the resulting closed-loop system is exponentially stable by employing the piecewise Lyapunov function technique. In addition, according to the minimum inter-event time, the Zeno behavior can be excluded. Finally, the merits of the proposed controllers and theory are corroborated using the well-known nonlinear Chua circuit.