Control Of Nonlinear Systems Research Articles

Adaptive backstepping fast terminal sliding mode control for nonlinear uncertain active suspension system with mechanical elastic wheel

Adaptive backstepping fast terminal sliding mode control for nonlinear uncertain active suspension system with mechanical elastic wheel

A reinforcement learning methodology to hierarchical sliding‐mode surface H∞$$ {H}_{\infty } $$ control of nonlinear systems via a dynamic event‐triggered mechanism

SummaryThis paper addresses the problem of a hierarchical sliding mode surface (HSMS) control design for nonlinear systems via a dynamic event‐triggered mechanism. Initially, the HSMS containing the system states is constructed to enhance the system's response rate and robustness. By assigning a cost function associated with the HSMS, such an control problem is equivalently transformed into a zero‐sum game problem, where the control policy and the exogenous disturbance are treated as two players with opposite interests. Afterwards, a novel dynamic event‐triggered mechanism is designed, where the triggering condition depends on HSMS variables. To solve the corresponding event‐triggered Hamilton–Jacobi–Isaacs equation, a single‐critic reinforcement learning algorithm is developed, which removes the error generated by approximating the actor network in the actor‐critic network. According to the Lyapunov stability theory, all signals of the considered system are strictly proved to be bounded. Finally, the validity of the proposed control method is demonstrated through simulations of a tunnel diode circuit system and a mass‐spring‐damper system.

Event-triggered adaptive compensation control for stochastic nonlinear systems with multiple failures: An improved switching threshold strategy.

This paper considers the event-triggered adaptive fault-tolerant control (FTC) problem for a class of stochastic nonlinear systems suffering from finite number of actuator failures and abrupt system external failure. Unlike existing event-triggered mechanisms (ETMs), this paper proposes an improved switching threshold mechanism (STM) that effectively addresses the potential system security hazards caused by large signal impulses when both the magnitude size of the controller and its rate of change are too large, while also saving energy consumption. Especially, when the occurrence of both actuator failure and system external failure may lead to over-change rate of the controller, by using the multi-dimensional Taylor network (MTN) approximation technique, the adaptive fault-tolerant control scheme designed based on the improved STM not only has lower resource consumption, but also indirectly improves the control performance of the system by ensuring the system security operation. Not only does it ensure that all signals of the closed-loop system are bounded in probability and the tracking error converges through the proposed control scheme. The feasibility and superiority of the developed scheme is well shown by dynamic model simulations.

Robust controller design based on double-layer nonlinear disturbance observers and sliding mode control for nonlinear systems with mismatched disturbances and uncertainties

Robust controller design based on double-layer nonlinear disturbance observers and sliding mode control for nonlinear systems with mismatched disturbances and uncertainties

Novel adaptive predefined-time complete tracking control of nonlinear systems via ELM

Novel adaptive predefined-time complete tracking control of nonlinear systems via ELM

Event-triggered resilient control of nonlinear multi-agent systems with disturbances under DoS attacks and directed switching topologies

This paper addresses the issue of resilient event-triggered consensus control for nonlinear multi-agent systems (MASs) operating in non-ideal communication networks. Specifically, it focuses on the challenges posed by directed switching topologies, disturbances and denial-of-service (DoS) attacks. A new approach is introduced to study the consensus tracking problem with a dynamic leader. The proposed strategy utilises a resilient event-triggered consensus tracking control framework, which comprises an event-triggered controller (ETC) and an event-triggering mechanism (ETM). By establishing matrix inequalities and considering parameters associated with DoS attacks and average dwell time, sufficient conditions for solving H ∞ consensus tracking problem are derived. One characteristic of the designed ETM is that events only occur at the triggering or switching time, preventing Zeno behaviour. Furthermore, a resilient event-triggered control strategy is suggested to tackle the containment problem with multiple dynamic leaders. Finally, the effectiveness of the designed control strategies is demonstrated through numerical simulations.

Event‐Triggered Integral Sliding Mode Control for Nonlinear Networked Cascade Control Systems With Uncertain Delay

ABSTRACTThis article investigates the problem of event‐triggered integral sliding mode control (ISMC) for a class of nonlinear networked cascade control systems (NCCSs). Due to the limited communication bandwidth between network nodes, uncertain delay can occur during transmission. This may result in a deterioration of the capabilities of NCCS and system instability in certain instances. Thus, an event‐triggered mechanism (ETM) and ISMC are introduced into the system, and a new model is constructed to increase the network bandwidth and reduce the effect of nonlinear perturbation. After that, the design of two controllers and ETM is accomplished through the use of the Lyapunov generalized method and linear matrix inequality (LMI) techniques. Finally, a simulation example of a power plant model is given to verify the effectiveness of the method. The results show that, for the first time, the event‐triggered ISMC is applied to NCCSs, which greatly saves the network bandwidth resources. The states of the system can reach the sliding mode surface (SMS) in finite time and remain stabilized there afterward, which ensures the stable operation of the system.

Fuzzy Control of Multivariable Nonlinear Systems Using T–S Fuzzy Model and Principal Component Analysis Technique

In this work, a new nonlinear control method is proposed, which integrates the Takagi–Sugeno (T–S) fuzzy model with the Principal Component Analysis (PCA) technique. The approach uses PCA to reduce the system’s dimensionality, minimizing the number of fuzzy rules required in the T–S fuzzy model. This reduction not only simplifies the system variables but also decreases the computational complexity, resulting in a more efficient control with smooth transient responses and zero steady-state error. To validate the performance of this PCA-based approach for both system identification and control, an interconnected double-tank system was employed. The results demonstrate the method’s capacity to maintain control accuracy while reducing computational load, making it a promising solution for applications in industrial and engineering systems that require robust, efficient control mechanisms.

Event-triggered Decentralized H∞ Control for Nonlinear Interconnected System Based on Adaptive Dynamic Programming: Application to Roller Kiln

Event-triggered Decentralized H∞ Control for Nonlinear Interconnected System Based on Adaptive Dynamic Programming: Application to Roller Kiln

Observer-based adaptive control for fractional-order strict-feedback nonlinear systems with state quantisation and input quantisation

This paper investigates the adaptive control problems of fractional-order strict-feedback nonlinear systems with state quantisation and input quantisation. Both Caputo and Riemann–Liouville derivative definitions are considered. Based on these two definitions, two fractional-order state observers are constructed to smooth the discontinuous quantised states, respectively. Then, new error variables and the dynamic filters are introduced to facilitate the design of the adaptive quantised controller. It is shown that the designed controller is valid for fractional-order strict-feedback nonlinear systems under both Caputo derivative definition and Riemann–Liouville derivative definition. Finally, three examples are given to demonstrate the effectiveness of the proposed method.

Event-triggered adaptive asymptotic tracking control for uncertain nonlinear system with time-varying parameters and full state constraints

Event-triggered adaptive asymptotic tracking control for uncertain nonlinear system with time-varying parameters and full state constraints

Leader-following privacy-preserving consensus control of nonlinear multi-agent systems: a state decomposition approach

This paper focuses on a nonlinear multi-agent system (MAS) in the presence of unknown dynamics and eavesdroppers. An output consensus control strategy is proposed based on an extended state observer, which achieves leader-following privacy-preserving consensus in a backstepping framework. Different from existing distributed average consensus approaches that require each follower to exchange and expose state information to its neighbours, our strategy avoids the requirement for communication during information interaction by decomposing each follower's state information into two sub-states. The status information is leaked, thereby achieving privacy preservation. Furthermore, for this class of MAS with unknown dynamics, we use an extended state observer to estimate the unknown dynamics and design an auxiliary system. The auxiliary variables generated by this auxiliary system combined with the back-stepping method can effectively compensate for dynamics. By combining graph theory knowledge and techniques such as Lyapunov functions, it is proven that the output-consensus tracking error of an MAS is ultimately bounded. Finally, the effectiveness of the control strategy is verified via an example.

Monitoring-Based Adaptive Fault-Tolerant Control for Uncertain Nonlinear Systems With Gradual Actuator Deactivation

Monitoring-Based Adaptive Fault-Tolerant Control for Uncertain Nonlinear Systems With Gradual Actuator Deactivation

Secured Bipartite Consensus Control for Nonlinear Multi-Agent Systems Against Hybrid Attacks: A Component-Based WTOD Protocol

Secured Bipartite Consensus Control for Nonlinear Multi-Agent Systems Against Hybrid Attacks: A Component-Based WTOD Protocol

Self-Triggered Impulsive Control for Nonlinear Stochastic Systems

Self-Triggered Impulsive Control for Nonlinear Stochastic Systems

Cooperative-Critic Learning-Based Secure Tracking Control for Unknown Nonlinear Systems With Multisensor Faults.

This article develops a cooperative-critic learning-based secure tracking control (CLSTC) method for unknown nonlinear systems in the presence of multisensor faults. By introducing a low-pass filter, the sensor faults are transformed into "pseudo" actuator faults, and an augmented system that integrates the system state and the filter output is constructed. To reduce design costs, a joint neural network Luenberger observer (NNLO) structure is established by using neural network and input/output data of the system to identify unknown system dynamics and sensor faults online. To achieve the optimal secure tracking control, an augmented tracking system is formed by integrating the dynamics of tracking error, reference trajectory, and filter output. Then, a novel cost function is designed for the augmented tracking system, which employs the fault estimation and the discount factor. The Hamilton-Jacobi-Bellman equation is solved to obtain the CLSTC strategy through an adaptive critic structure with cooperative tuning laws. Besides, the Lyapunov stability theorem is utilized to prove that all signals of the closed-loop system converge to a small neighborhood of the equilibrium point. Simulation results demonstrate that the proposed control method has good fault tolerance performance and is suitable for solving secure control problems of nonlinear systems with various sensor faults.

Dynamic Event-Triggered Secure Cooperative Control of Second-Order Nonlinear Multi-Agent Systems Under DoS Attacks

Dynamic Event-Triggered Secure Cooperative Control of Second-Order Nonlinear Multi-Agent Systems Under DoS Attacks

Learning-Based Modeling and Predictive Control for Unknown Nonlinear System With Stability Guarantees

Learning-Based Modeling and Predictive Control for Unknown Nonlinear System With Stability Guarantees

Adaptive Tracking Control of Constrained Nonlinear Systems and Its Application to Circuit Systems

Adaptive Tracking Control of Constrained Nonlinear Systems and Its Application to Circuit Systems

Dynamic Self-Triggered Robust Distributed Model Predictive Control for Coupled Nonlinear Systems

Dynamic Self-Triggered Robust Distributed Model Predictive Control for Coupled Nonlinear Systems