With the continuous increase in complexity and expense of industrial systems, there is less tolerance for performance degradation, productivity decrease, and safety hazards, which greatly necessitates to detect and identify any kinds of potential abnormalities and faults as early as possible and implement real-time fault-tolerant operation for minimizing performance degradation and avoiding dangerous situations. During the last four decades, fruitful results have been reported about fault diagnosis and fault-tolerant control methods and their applications in a variety of engineering systems. The three-part survey paper aims to give a comprehensive review of real-time fault diagnosis and fault-tolerant control, with particular attention on the results reported in the last decade. In this paper, fault diagnosis approaches and their applications are comprehensively reviewed from model- and signal-based perspectives, respectively.

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A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches

Barrier Lyapunov functions for Nussbaum gain adaptive control of full state constrained nonlinear systems

A review on data-driven fault severity assessment in rolling bearings

This paper concentrates upon the problem of finite-time fault-tolerant control for a class of switched nonlinear systems in lower-triangular form under arbitrary switching signals. Both loss of effectiveness and bias fault in actuator are taken into account. The method developed extends the traditional finite-time convergence from nonswitched lower-triangular nonlinear systems to switched version by designing appropriate controller and adaptive laws. In contrast to the previous results, it is the first time to handle the fault tolerant problem for switched system while the finite-time stability is also necessary. Meanwhile, there exist unknown internal dynamics in the switched system, which are identified by the radial basis function neural networks. It is proved that under the presented control strategy, the system output tracks the reference signal in the sense of finite-time stability. Finally, an illustrative simulation on a resistor-capacitor-inductor circuit is proposed to further demonstrate the effectiveness of the theoretical result.

Neural Networks-Based Adaptive Finite-Time Fault-Tolerant Control for a Class of Strict-Feedback Switched Nonlinear Systems

For parameter identifications of robot systems, most existing works have focused on the estimation veracity, but few works of literature are concerned with the convergence speed. In this paper, we developed a robot control/identification scheme to identify the unknown robot kinematic and dynamic parameters with enhanced convergence rate. Superior to the traditional methods, the information of parameter estimation error was properly integrated into the proposed identification algorithm, such that enhanced estimation performance was achieved. Besides, the Newton–Euler (NE) method was used to build the robot dynamic model, where a singular value decomposition-based model reduction method was designed to remedy the potential singularity problems of the NE regressor. Moreover, an interval excitation condition was employed to relax the requirement of persistent excitation condition for the kinematic estimation. By using the Lyapunov synthesis, explicit analysis of the convergence rate of the tracking errors and the estimated parameters were performed. Simulation studies were conducted to show the accurate and fast convergence of the proposed finite-time (FT) identification algorithm based on a 7-DOF arm of Baxter robot.

Adaptive Parameter Estimation and Control Design for Robot Manipulators With Finite-Time Convergence

This paper proposes adaptive control designs for vehicle active suspension systems with unknown nonlinear dynamics (e.g., nonlinear spring and piece-wise linear damper dynamics). An adaptive control is first proposed to stabilize the vertical vehicle displacement and thus to improve the ride comfort and to guarantee other suspension requirements (e.g., road holding and suspension space limitation) concerning the vehicle safety and mechanical constraints. An augmented neural network is developed to online compensate for the unknown nonlinearities, and a novel adaptive law is developed to estimate both NN weights and uncertain model parameters (e.g., sprung mass), where the parameter estimation error is used as a leakage term superimposed on the classical adaptations. To further improve the control performance and simplify the parameter tuning, a prescribed performance function (PPF) characterizing the error convergence rate, maximum overshoot and steady-state error is used to propose another adaptive control. The stability for the closed-loop system is proved and particular performance requirements are analyzed. Simulations are included to illustrate the effectiveness of the proposed control schemes.

Adaptive control of nonlinear uncertain active suspension systems with prescribed performance.

This paper proposes an adaptive control for vehicle active suspensions with unknown nonlinearities (eg, nonlinear springs and piecewise dampers) A prescribed performance function that characterizes the convergence rate, maximum overshoot, and steady-state error is incorporated into the control design to stabilize the vertical and pitch motions, such that both the transient and steady-state suspension response are guaranteed Moreover, a novel adaptive law is used to achieve precise estimation of essential parameters (eg, mass of vehicle body and moment of inertia for pitch motion), where the parameter estimation error is obtained explicitly and then used as a new leakage term Theoretical studies prove the convergence of the estimated parameters, and compare the suggested controller with generic adaptive controllers using the gradient descent and e-modification schemes In addition to motion displacements, dynamic tire loads and suspension travel constraints are also considered Extensive comparative simulations on a dynamic simulator consisting of commercial vehicle simulation software Carsim 81 and MATLAB Simulink are provided to show the efficacy of the proposed control, and to illustrate the improved performance

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Active Adaptive Estimation and Control for Vehicle Suspensions With Prescribed Performance

This paper presents a novel control strategy for nonlinear uncertain vehicle active suspension systems without using any function approximators [eg, neural networks (NNs) or fuzzy logic systems (FLSs)] Unlike previous results that neglect the effect of actuator dynamics, this paper incorporates the dynamics of a hydraulic actuator that is used to create the required active suspension forces into the controller design To address the nonlinearities of this hydraulic system, an approximation-free control method is introduced In this method, the widely used NNs and FLSs are not needed This leads to reduced computational burden and complexity, and thus, it is more suited for practical applications Moreover, by introducing a prescribed performance function and the associated error transform, the proposed controller can guarantee both the transient and steady-state suspension responses The stability of the closed-loop system and the suspension performance requirements are rigorously proved Finally, comparative simulations are conducted to validate the improved performance and robustness of the proposed method

Approximation-Free Control for Vehicle Active Suspensions With Hydraulic Actuator

An optimized VMD method and its applications in bearing fault diagnosis

This paper presents a new adaptive fuzzy control scheme for active suspension systems subject to control input time delay and unknown nonlinear dynamics. First, a predictor-based compensation scheme is constructed to address the effect of input delay in the closed-loop system. Then, a fuzzy logic system (FLS) is employed as the function approximator to address the unknown nonlinearities. Finally, to enhance the transient suspension response, a novel parameter estimation error-based finite-time (FT) adaptive algorithm is developed to online update the unknown FLS weights, which differs from traditional estimation methods, for example, gradient algorithm with ${e}$ -modification or ${\sigma }$ -modification. In this framework, both the suspension and estimation errors can achieve convergence in FT. A Lyapunov–Krasovskii functional is constructed to prove the closed-loop system stability. Comparative simulation results based on a dynamic simulator built in a professional vehicle simulation software, Carsim, are provided to demonstrate the validity of the proposed control approach, and show its effectiveness to operate active suspension systems safely and reliably in various road conditions.

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Xing Wu

Papers

Adaptive control of nonlinear uncertain active suspension systems with prescribed performance.

Active Adaptive Estimation and Control for Vehicle Suspensions With Prescribed Performance

Approximation-Free Control for Vehicle Active Suspensions With Hydraulic Actuator

An optimized VMD method and its applications in bearing fault diagnosis

Adaptive Finite-Time Fuzzy Control of Nonlinear Active Suspension Systems With Input Delay