Fault-tolerant Control For Nonlinear Systems Research Articles

Neural network-based adaptive fault-tolerant control for nonlinear systems with uncertainties

This paper proposes a novel fault-tolerant control (FTC) scheme for real-time uncertainty estimation in nonlinear systems. It addresses the challenges arising from nonlinear dynamics in system inputs, states, and outputs, along with measurement uncertainties, within an output feedback framework. Our approach leverages two key components: 1) A neural network NN descriptor-based observer: this novel observer concurrently estimates both system states and sensor uncertainties. It is particularly capable of handling unbounded sensor uncertainties in specific situations. It utilizes NNs as universal approximators to capture the system's complex nonlinearities. 2) A robust model reference tracking controller: this controller employs the estimated states from the NN descriptor-based observer to achieve the desired system performance despite the existence of uncertainties. It exhibits robustness, guaranteeing system stability and asymptotic state tracking to a given reference model. The efficacy of the proposed FTC scheme is validated through theoretical analysis and its application to two real-world case studies.

Observer-Based Finite-Time Adaptive Fault-Tolerant Control for Nonlinear System with Unknown Time-Varying Delay

Observer-Based Finite-Time Adaptive Fault-Tolerant Control for Nonlinear System with Unknown Time-Varying Delay

Command Filtered Neuroadaptive Fault-Tolerant Control for Nonlinear Systems With Input Saturation and Unknown Control Direction.

This article studies the tracking control of a class of nonlinear systems with input saturation, subject to nonaffine faults and unknown control direction. A fault-tolerant command filtered control (CFC) method based on adaptive neural networks (NNs) is proposed for this kind of nonlinear system. First, the combination of CFC and error compensation overcomes the "explosion of complexity" issue and alleviates the impact of filter errors. Then, a set of radial basis function NNs is constructed to approximate the unknown nonlinear items containing the nonaffine fault function. Additionally, the issue of unknown control direction in the system is effectively resolved by using Nussbaum gain technology. It is proven that the designed controller can ensure that all signals in the closed-loop system are bounded and convergent, and the upper bound of the absolute value of system tracking error is given. Finally, three comparative simulation results are illustrated to show the effectiveness of the proposed method.

Neural network-based fault diagnosis and fault-tolerant control for nonlinear systems with output measurement noise

Neural network-based fault diagnosis and fault-tolerant control for nonlinear systems with output measurement noise

Predefined Time and Accuracy Adaptive Fault-Tolerant Control for Nonlinear Systems with Multiple Faults

This work mainly studies the issue of predefined time and accuracy adaptive fault-tolerant control for strict-feedback nonlinear systems with multiple faults. The faults in the controlled system include actuator faults and external system faults. The unknown functions for nonlinear systems are approximated by fuzzy logic systems (FLSs). And then, according to the backstepping technique and the predefined time stability theory, an adaptive fuzzy control algorithm is presented, which can make sure that all closed-loop system signals remain predefined time bound and the tracking error converges to a predefined accuracy within the predefined time. Ultimately, the effectiveness of the presented control algorithm is proved through two simulation examples.

Funnel-Based Adaptive Neural Fault-Tolerant Control for Nonlinear Systems with Dead-Zone and Actuator Faults: Application to Rigid Robot Manipulator and Inverted Pendulum Systems

This study addresses an adaptive neural funnel fault-tolerant control problem for a class of strict-feedback nonlinear systems with actuator faults and input dead zone. To guarantee the boundedness of the tracking error, a modified transformation for funnel error is devised and incorporated into the control design process. To manage unknown nonlinear functions, radial basis function neural networks (RBFNN) are employed in designing an adaptive neural funnel fault-tolerant controller through the backstepping technique. The proposed controller guarantees the output tracking error stays within a predefined funnel, and all signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, simulations of a rigid robot manipulator system and an inverted pendulum system are conducted to validate the practicality and effectiveness of the proposed control method.

Adaptive Self-Triggered Fault-Tolerant Control for Nonlinear Systems With Input Dead Zone: A Funnel Function Method

Adaptive Self-Triggered Fault-Tolerant Control for Nonlinear Systems With Input Dead Zone: A Funnel Function Method

Adaptive Neural Fault-Tolerant Control for Nonlinear System With Multiple Faults and Dead Zone

Adaptive Neural Fault-Tolerant Control for Nonlinear System With Multiple Faults and Dead Zone

Performance recovery-based low-computation adaptive self-triggered fault-tolerant control for nonlinear systems with actuator replacement and unmatched disturbances

Performance recovery-based low-computation adaptive self-triggered fault-tolerant control for nonlinear systems with actuator replacement and unmatched disturbances

Data-driven fault-tolerant control for nonlinear systems with output saturation

This paper is focused on data-driven fault-tolerant control for nonlinear systems with output saturation. A modified observer with measurement output is built to approximate sensor fault. According to this approximation, corresponding data-driven fault-tolerant controller is proposed by utilizing optimization criteria and adaptive dynamic programming method. Consequently, a triggering rule is constructed by employing instantaneous and average measurement output. Resorting to the Lyapunov method, the resulted error system is uniformly ultimately bounded. The effectiveness of the presented approach is demonstrated by an example comparison.

Fault-tolerant control for nonlinear systems with a dead zone: Reinforcement learning approach.

This paper focuses on the adaptive reinforcement learning-based optimal control problem for standard nonstrict-feedback nonlinear systems with the actuator fault and an unknown dead zone. To simultaneously reduce the computational complexity and eliminate the local optimal problem, a novel neural network weight updated algorithm is presented to replace the classic gradient descent method. By utilizing the backstepping technique, the actor critic-based reinforcement learning control strategy is developed for high-order nonlinear nonstrict-feedback systems. In addition, two auxiliary parameters are presented to deal with the input dead zone and actuator fault respectively. All signals in the system are proven to be semi-globally uniformly ultimately bounded by Lyapunov theory analysis. At the end of the paper, some simulation results are shown to illustrate the remarkable effect of the proposed approach.

Neural-Networks-Based Adaptive Fault-Tolerant Control of Nonlinear Systems With Actuator Faults and Input Quantization

Neural-Networks-Based Adaptive Fault-Tolerant Control of Nonlinear Systems With Actuator Faults and Input Quantization

Multi-dimensional Taylor Network-Based Fault-Tolerant Control for Nonlinear Systems with Unmodeled Dynamics and Actuator Faults

Multi-dimensional Taylor Network-Based Fault-Tolerant Control for Nonlinear Systems with Unmodeled Dynamics and Actuator Faults

Fault-Tolerant Control of Nonlinear Systems With Actuator and Sensor Faults Based on T–S Fuzzy Model and Fuzzy Observer

In this work, we study simultaneous reconstructions of sensor fault and actuator fault, as well as fault-tolerant control (FTC) for nonlinear systems approximated by the T–S fuzzy model. By making the sensor fault as part of the state, a rectangular singular system is firstly given, based on which, a proportional-integral observer (PIO) is synthesized to realize the reconstructions of sensor and actuator faults simultaneously. With the support of these estimation information, an FTC scheme is well provided to maintain the stability of the closed-loop system even in the occurrence of faults. Furthermore, by resorting to linear matrix inequalities (LMIs), the stability criteria are formulated via the fuzzy Lyapunov function method. Finally, the performance of the theoretical result is verified through two real dynamics.

Observer-based fuzzy fault-tolerant control for nonlinear systems in the presence of general noise

Observer-based fuzzy fault-tolerant control for nonlinear systems in the presence of general noise

Event-driven-observer-based fuzzy fault-tolerant control for nonlinear system with actuator fault

Event-driven-observer-based fuzzy fault-tolerant control for nonlinear system with actuator fault

Actuator fault-tolerant iterative learning control of the magnetic brake system

Design of fault-tolerant control for nonlinear systems is usually focused on maxi-mizing some criteria defining the level of robustness with respect to potential faults without significant degradation of the system performance. Therefore, strong interest is produced by compromise approaches which would generate decent control quality for the broadest possible class of potential faulty scenarios. Here, an approach is proposed with the application to magnetic brake system making use of the repetitive character of the control task. The concept of iterative learning control driven by measurement data is utilized to properly update the control signal in order to adapt to possible actuator fault states. A learning controller is adopted build on a mixture of neural networks for various operating points. Thus, it is able to adapt to changing working conditions of the device. Moreover, using the procedure employing an ensemble of inverse models, actuator faults can be successfully accommodated. The paper provides the complete iterative learning procedure including the system identification, fault estimation and fault-tolerant control. The numerical example on the reference tracking problem for magnetic brake system is discussed, taking into account various faulty scenarios.

Robust immersion and invariance adaptive fault-tolerant control of nonlinear systems with non-linearly parameterized faults

This paper presents a robust adaptive fault-tolerant control (FTC) scheme based on Immersion and Invariance (I&I) manifold method for a class of disturbed nonlinear systems on the occurrence of non-linearly parameterized (NLP) faults. The fault detection method based on adaptive observer is proposed firstly, and the fault tolerant controller for the NLP faults based on robustified I&I adaptation law is presented on the basis of nominal control law. Different from traditional adaptation law, the proposed robustified I&I adaptation law can not only eliminate the parameterized uncertainty but also shape the convergnce performance of the parameter estimates. The stability of the closed-loop system is rigorously analyzed. Numerical simulations are carried out to verify the effectiveness of the proposed scheme. • Different from linearly parameterized faults, a class of disturbed nonlinear systems on occurrence of non-linear parameterized (NLP) faults are considered in this paper. • A new robust adaptation law based on Immersion and Invariance manifold and non-certainty equivalence principle is proposed to shape the transient performance of the parameters convergence in the NLP faults. • Before a fault is detected, the nominal controller of the system can guarantee output tracking in the absence of fault and the stability of the system in the presence of faults. As the fault is detected, the fault-tolerant control (FTC) scheme can guarantee all the signals of the closed-loop systems are uniformly ultimately bounded and render the output tracking error converge to a small neighborhood of zero with satisfying transient performance.

Fuzzy Logic Based Metaheuristic Algorithm for Optimization of Type-1 Fuzzy Controller: Fault-Tolerant Control for Nonlinear System with Actuator Fault

This research offers a fuzzy-based harmonic search (HS) metaheuristic technique for optimizing type-1 fuzzy controller for Fault-Tolerant Control (FTC) for nonlinear level control application subject to two uncertainties (i.e. actuator fault and external process disturbances), using type-1 and interval type-2 fuzzy-based HS algorithms. The effectiveness of a fuzzy logic-based adaptive HS algorithm in a nonlinear two-tank level control process with the primary actuator has dwindled (LOE). The work key contribution is the discovery of the best technique for constructing an optimal vector of values for the fuzzy controller’s membership functions (MFs) optimization. This is made to improve dynamic response by bringing the process value of the two-tank level control process close to the target process value (set-point). It’s worth noting that the type-1 fuzzy controller’s optimized MFs use an interval type-2 fuzzy system for parameter adaptation of the HS algorithm, which can handle greater uncertainty than a type-1 fuzzy system. In this case, the limiting MFs of interval type-2 fuzzy sets are type-1 fuzzy sets, which defne the footprint of uncertainty (FOU). Simulation results show that FHSO using an interval type-2 fuzzy system outperforms FHSO using a type-1 fuzzy system in the optimal design of a type-1 fuzzy controller.

Finite-time adaptive event-triggered fault-tolerant control of nonlinear systems based on fuzzy observer

This paper addresses the adaptive event-triggered tracking control problem for a class of uncertain nonlinear systems with actuator failures and disturbances. By designing a fuzzy observer to estimate the unmeasurable states, a novel event-triggered adaptive fuzzy control scheme with a sign function is proposed. Unlike the existing methods, the event-triggered controller with a parameter-updated law containing only one online modulated variable can dynamically compensate for the approximation error caused at each step. The proposed control strategy guarantees that all the signals of the closed-loop systems are bounded, and that the tracking errors can be regulated to a compact set around the origin in finite-time without Zeno behavior. Finally, a numerical example and a practical example validate the effectiveness of the designed scheme.