Time-varying Barrier Lyapunov Function Based Adaptive Neural Controller Design for Nonlinear Pure-feedback Systems with Unknown Hysteresis

Tang, Li; Li, Dong-Juan

doi:10.1007/s12555-018-0745-y

Cited by 32 publications

(35 citation statements)

References 35 publications

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“…The vibration signal collected during the running of rolling bearings reflects the change of the bearing with the running time [33,34]. Aiming at this time correlation, a moving window algorithm was used to update the model in real time to achieve adaptive detection.…”

Section: Adaptive Dynamic Methodsmentioning

confidence: 99%

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

et al. 2020

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The incipient fault detection technology of rolling bearings is the key to ensure its normal operation and is of great significance for most industrial processes. However, the vibration signals of rolling bearings are a set of time series with non-linear and timing correlation, and weak incipient fault characteristics of rolling bearings bring about obstructions for the fault detection. This paper proposes a nonlinear dynamic incipient fault detection method for rolling bearings to solve these problems. The kernel function and the moving window algorithm are used to establish a non-linear dynamic model, and the real-time characteristics of the system are obtained. At the same time, the deep decomposition method is used to extract weak fault characteristics under the strong noise, and the incipient failures of rolling bearings are detected. Finally, the validity and feasibility of the scheme are verified by two simulation experiments. Experimental results show that the fault detection rate based on the proposed method is higher than 85% for incipient fault of rolling bearings, and the detection delay is almost zero. Compared with the detection performance of traditional methods, the proposed nonlinear dynamic incipient fault detection method is of better accuracy and applicability.

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Section: Adaptive Dynamic Methodsmentioning

confidence: 99%

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

et al. 2020

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“…are compensated for directly by the control law u ref (t) instead of the adaptive parameter̂1(t),̂2(t), as expressed in (25). Notice that…”

Section: Steady-state Performancementioning

confidence: 99%

“…are unknown, and this reference system is only used for analysis. The L 1 -norm condition in (24) is necessary to guarantee the stability of the closed-loop reference system in (25). Using a contradictive argument, the performance bounds for x ref (t) and u ref (t) are derived.…”

Section: Steady-state Performancementioning

confidence: 99%

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Filtered adaptive constrained sampled‐data control for uncertain multivariable nonlinear systems

2020

Adaptive Control & Signal

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A filtered adaptive constrained sampled‐data controller for uncertain multivariable nonlinear systems in the presence of various constraints is synthesized in this paper. A piecewise constant adaptive law drives that estimation error dynamics to zero at each sampling time instant yields adaptive parameters. The filtered control scheme consists of two components. Based on an estimation/cancellation strategy, a disturbance rejection control law is designed to compensate the nonlinear uncertainties within the bandwidth of low‐pass filters, whereas a constraint violation avoidance control law is designed to solve an online constrained optimization problem. Although a reduced sampling time helps to minimize the estimation error caused by the neglect of unknowns, the resulting aggressive signals put more restrictions on the control law. Greater sacrifice of tracking performance is required to satisfy the constraints. The constraints violation avoidance control law is in favor of a larger sampling time. Sufficient conditions are given to guarantee the stability of the closed‐loop system with the sampled‐data controller, where the input/output signals are held constant over the sampling period. Numerical examples are provided to validate the theoretical results, comparisons between the constrained sampled‐data controller and unconstrained adaptive controller with the implementation of different sampling times are carried out.

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“…In recent years, neural networks have been widely applied in various fields, both in science and engineering [12], [13], [13]- [19]. As a significant application, the kinematic control of robot redundant is frequently solved by the neural network method [20].…”

Section: Introductionmentioning

confidence: 99%

Two DTZNN Models of O(τ⁴) Pattern for Online Solving Dynamic System of Linear Equations: Application to Manipulator Motion Generation

et al. 2020

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In this paper, firstly, a high accuracy one-step-ahead numerical differentiation formula with O(τ 4) pattern is proposed for discretization. Meanwhile, a high precision first-order derivative formula of the backward difference rule with O(τ 4) pattern error is given to approximate the derivative information. Then, two high accuracy discrete-time zeroing-type models (HADTZTM) with O(τ 4) pattern, i.e., HADTZTM with derivative information known (HADTZTM-K) and HADTZTM with derivative information unknown (HADTZTM-U), are developed, analyzed and investigated for online solving the dynamic system of linear equations (DSLEs). In addition, the 0-stability, consistency, and convergence of the HADTZTM-K and HADTZTM-U are verified for DSLEs. From a theoretical/numerical viewpoint, the classical models are revisited and analyzed for online solving DSLEs. Ultimately, simulation experiment including an application to the path-tracking of the four-link planar manipulator is conducted to demonstrate the efficiency and superiority of the HADTZTM-K and HADTZTM-U, where the HADTZTM-U overcomes the difficulty of derivative information unknown in practical applications. INDEX TERMS Dynamic system of linear equations (DSLEs), discrete-time zeroing neural network (DTZNN), backward difference formula, theoretical results, steady-state residual error.

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Time-varying Barrier Lyapunov Function Based Adaptive Neural Controller Design for Nonlinear Pure-feedback Systems with Unknown Hysteresis

Cited by 32 publications

References 35 publications

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

Filtered adaptive constrained sampled‐data control for uncertain multivariable nonlinear systems

Two DTZNN Models of O(τ⁴) Pattern for Online Solving Dynamic System of Linear Equations: Application to Manipulator Motion Generation

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Time-varying Barrier Lyapunov Function Based Adaptive Neural Controller Design for Nonlinear Pure-feedback Systems with Unknown Hysteresis

Cited by 32 publications

References 35 publications

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

Filtered adaptive constrained sampled‐data control for uncertain multivariable nonlinear systems

Two DTZNN Models of O(τ4) Pattern for Online Solving Dynamic System of Linear Equations: Application to Manipulator Motion Generation

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Product

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Two DTZNN Models of O(τ⁴) Pattern for Online Solving Dynamic System of Linear Equations: Application to Manipulator Motion Generation