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The rolling bearing of the servo motor is widely used in precision-controlled mechanical systems. It usually works at variable speed and load, possibly resulting in partial bearing failure. Meanwhile, the varying conditions may cause the smearing of classable features, increasing the diagnostic difficulty. To this end, an intelligent fault diagnosis method of servo motor-cylindrical roller bearings based on adaptive multiscale convolution manifold embedding networks (AMCMENet) under variable working conditions is proposed. The core of the proposed algorithm is to apply the designed intraclass and interclass constraints to reprocess the feature extracted by designed multiscale convolutional neural networks (MSCNN). In this way, the distribution differences of samples could be improved. The training sample under variable conditions is first input to the designed MSCNN for initial feature extraction. Afterward, the constructed locality sensitive discriminant analysis algorithm module is used, which is adjusted to optimal parameters by the particle swarm optimization algorithm, to enlarge the heterogeneous distance and narrow the homogeneous distance of the extracted feature. Finally, the testing subset is provided to the trained AMCMENet algorithm for fault diagnosis. The experimental results of two datasets demonstrate that the proposed intelligent fault diagnosis method performs better under cross working conditions.

In this brief, a disturbance observer based prescribed-time tracking control strategy is proposed for a class of high-order nonlinear systems with non-vanishing uncertainties. A finite time function is firstly introduced to facilitate the disturbance observer and controller design. Then, a prescribed-time disturbance observer is constructed to estimate and compensate for the non-vanishing uncertainties, which ensures that the estimation error converges to zero in a prescribed time. Based on the backstepping design framework, the prescribed -time controller is further designed in the form of scaling the tracking error. Theoretical analysis reveals that the proposed controller achieves the excellent prescribed-time convergence of the full state tracking errors, and the convergence time can be explicitly predetermined, independent of the initial conditions. Finally, the validity and superiority of the proposed controller is demonstrated via numerical simulation.

This article focuses on asymptotic precision motion control for electro-hydraulic axis systems under unknown time-variant parameters, mismatched and matched disturbances. Different from the traditional adaptive results that are applied to dispose of unknown constant parameters only, the unique feature is that an adaptive-gain nonlinear term is introduced into the control design to handle unknown time-variant parameters. Concurrently both mismatched and matched disturbances existing in electro-hydraulic axis systems can also be addressed in this way. With skillful integration of the backstepping technique and the adaptive control, a synthesized controller framework is successfully developed for electro-hydraulic axis systems, in which the coupled interaction between parameter estimation and disturbance estimation is avoided. Accordingly, this designed controller has the capacity of low-computation costs and simpler parameter tuning when compared to the other ones that integrate the adaptive control and observer/estimator-based technique to dividually handle parameter uncertainties and disturbances. Also, a nonlinear filter is designed to eliminate the “explosion of complexity” issue existing in the classical back-stepping technique. The stability analysis uncovers that all the closed-loop signals are bounded and the asymptotic tracking performance is also assured. Finally, contrastive experiment results validate the superiority of the developed method as well.

In this article, a novel neural network (NN)-based adaptive dynamic surface asymptotic tracking controller with guaranteed transient performance is proposed for $n$ -degrees of freedom (DOF) hydraulic manipulators. To fulfill the work, the entire manipulator system model, including hydraulic actuator dynamics, is first established. Then, the neural adaptive dynamic surface controller is designed, in which the NN is utilized to approximate the unknown joint coupling dynamics, while the approximation error and uncertainties of the actuator dynamics are addressed by the nonlinear robust control law with adaptive gains. In addition, a modified funnel function that ensures the joint tracking errors remains within a predefined funnel boundary and is skillfully incorporated into the adaptive dynamic surface control (ADSC) design to achieve a guaranteed transient tracking performance. The theoretical analysis reveals that both the guaranteed transient tracking performance and asymptotic stability can be achieved with the proposed controller. Contrastive simulations are performed on a 2-DOF hydraulic manipulator to demonstrate the superiority of the proposed controller.

The industrial gearboxes usually work in harsh and variable conditions, which results in partial failure of gears or bearings. Accordingly, the continuous irregular fluctuations of gearbox under variable conditions maybe increase the intraclass difference and reduce the interclass difference for the monitored samples. To this end, a new intelligent fault diagnosis method of gearbox based on adaptive intraclass and interclass convolutional neural network (AIICNN) under variable working conditions is proposed. The core of the proposed algorithm is to apply the designed intraclass and interclass constraints to improve the distribution differences of samples. Meanwhile, the adaptive activation function is added into the 1-D convolutional neural network (1dCNN) to enlarge the heterogeneous distance and narrow the homogeneous distance of samples. Specifically, the training sample subset with intraclass and interclass spacing fluctuations under variable conditions is first converted into frequency domain through the fast Fourier transform (FFT), and the designed AIICNN algorithm is employed for model training. Afterward, the testing subset is provided to the trained AIICNN algorithm for fault diagnosis. The experimental data of the planetary gearbox test rig verify the feasibility of the proposed diagnosis method and algorithm. Compared with other methods, this method can eliminate the difference of sample distribution under variable conditions and improve its diagnostic generalization.

Unknown dynamics including mismatched mechanical dynamics (i.e., parametric uncertainties, unmodeled friction and external disturbances) and matched actuator dynamics (i.e., pressure and flow characteristic uncertainties) broadly exist in hydraulic actuation systems (HASs), which can hinder the achievement of high-precision motion axis control. To surmount the practical issue, an observer-based control framework with a simple structure and low computation is developed for HASs. First, a simple observer is utilized to estimate mismatched and matched unknown dynamics for feedforward compensation. Then combining the backstepping design and adaptive control, an appropriate observer-based composite controller is provided, in which nonlinear feedback terms with updated gains are adopted to further improve the tracking accuracy. Moreover, a smooth nonlinear filter is introduced to shun the “explosion of complexity” and attenuate the impact of sensor noise on control performance. As a result, this synthesized controller is more suitable for practical use. Stability analysis uncovers that the developed controller assures the asymptotic convergence of the tracking error. The merits of the proposed approach are validated via comparative experiment results applied in an HAS with an inertial load as well.

A winding system is a time-varying system that considers complex nonlinear characteristics, and how to control the stability of the winding tension during the winding process is the primary problem that has hindered development in this field in recent years. Many nonlinear factors affect the tension in the winding process, such as friction, structured uncertainties, unstructured uncertainties, and external interference. These terms severely restrict the tension tracking performance. Existing tension control strategies are mainly based on the composite control of the tension and speed loops, and previous studies involve complex decoupling operations. Owing to the large number of calculations required for this method, it is inconvenient for practical engineering applications. To simplify the tension generation mechanism and the influence of the nonlinear characteristics of the winding system, a simpler nonlinear dynamic model of the winding tension was established. An adaptive method was applied to update the feedback gain of the continuous robust integral of the sign of the error (RISE). Furthermore, an extended state observer was used to estimate modeling errors and external disturbances. The model disturbance term can be compensated for in the designed RISE controller. The asymptotic stability of the system was proven according to the Lyapunov stability theory. Finally, a comparative analysis of the proposed nonlinear controller and several other controllers was performed. The results indicated that the control of the winding tension was significantly enhanced.

Hydraulic manipulators are extremely complicated systems due to the highly nonlinear characteristics, strong coupling among multiple joints, and heavy modeling uncertainties, which greatly complicate the high-performance tracking controller development compared with conventional manipulators driven by electrical motors. To overcome the above obstacles, this article proposes a novel radial basis function neural network (RBFNN)-based adaptive asymptotic prescribed performance controller for $n$ -degrees of freedom (DOF) hydraulic manipulators. First, the entire manipulator system model containing actuator dynamics is derived. Then, the adaptive asymptotic prescribed performance controller is synthesized based on the backstepping framework, in which the RBFNN is employed to estimate the unknown joint coupling dynamics, while the RBFNN reconstruction error and uncertainties of the actuator dynamics are handled by the robust integral of the sign of the error (RISE) feedbacks. Meanwhile, a prescribed performance function (PPF), which characterizes both the certain transient and steady-state performance, is innovatively incorporated into the control design to restrict the joint tracking errors. The theoretical analysis proves that the proposed control strategy can achieve a prescribed tracking performance and the asymptotic stability of the whole closed-loop system can also be ensured. Finally, comparative simulations are conducted to verify the validity of the proposed controller.

In this article, an innovative backstepping controller for hydraulic systems is proposed to handle system uncertainties and accomplish specified performance tracking without violating the full-state constraints. To deal with uncertainties, extended state observers ESOsand an adaptive law are integrated. ESOs are structured to estimate disturbances, whereas adaptive law is applied to approximate unknown parameters. The estimated uncertainties are then incorporated into a constrained controller, ensuring that both the prescribed transient tracking performance and the nonviolation of full-state constraints can be guaranteed. A prescribed performance function (PPF) and the barrier Lyapunov function (BLF) are synthesized to guarantee the transient behavior of the tracking error and all state errors within desirable boundaries. Then, an adaptive prescribed performance controller with uncertainty compensation is constructed by merging the BLF and PPF through back-stepping design to stabilize the closed-loop system. Finally, abundant comparative experimental results validate the proposed controller's tracking performance.

Wire arc additive manufacturing (WAAM) has been used extensively in metal manufacturing and other fields because of its low cost and high efficiency. In the manufacturing process, WAAM technology is often unable to be further applied due to issues such as component dimensional accuracy, layered morphology, and metallurgical defects. To overcome these problems, online monitoring and process control of the welding are necessary. Based on the monitoring of weld width and reinforcement, a regression network for extracting the global information of molten pool is proposed, and an active disturbance rejection control (ADRC) is designed to adjust the welding current. To the best of our knowledge, this is the first time to realize the coordinated monitoring and control of the width and reinforcement of the deposited layer in the WAAM process. Experiments show that the method can obtain satisfactory molten pool width and reinforcement control accuracy, which provides a feasible way for monitoring and control the weld shape in the WAAM welding process.

In the process of wire arc additive manufacturing (WAAM), in order to improve the welding quality, the width of deposited layer needs to be monitored, and a feedback control strategy for the width of deposited layer must be developed. In this study, the feedback control of layer width based on melt pool vision was realized in WAAM process. The vision sensing system is used to collect the melt pool image during the welding process, and the weld width is determined according to the segmentation network EPNet designed. Finally, an active disturbance rejection control (ADRC) algorithm was designed to achieve real-time control of the melt pool width during welding. The experimental results show that the designed control algorithm can guarantee satisfactory control accuracy of melt pool width, which provides necessary strategies for online monitoring and control of weld width during WAAM.

Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of all-electric tank, it behaves more drastically in high maneuvering environments. In this work, the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved. By utilizing the smooth adaptive backlash inverse model, a nonlinear robust adaptive feedback control scheme is presented. The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term. Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation, which ensures the system stability. Meanwhile, the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality. Finally, Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash. Contrast co-simulations and experiments illustrate the advantages of the proposed approach.

High dynamic tracking performance is a key technical index of hydraulic flight motion simulator (HFMS). However, the strong nonlinearities, various model uncertainties and measurement noise in hydraulic actuation systems limit the high dynamic performance improvement. In this paper, the outer axis frame of a HFMS is taken as a case study and its nonlinear dynamic model with consideration of strong nonlinearities, matched and mismatched uncertainties is established. A novel cascaded extended state observer (ESO) is proposed to estimate the unavailable system states to avoid the adverse effect of measurement noise on control performance. Meanwhile, the designed cascaded ESO also produces estimates of matched and mismatched uncertainties. Then, an output feedback robust controller (OFRC) is proposed by integrating the cascaded ESO with a robust integral of the sign of the error (RISE) feedback based on the backstepping framework. The proposed controller achieves compensation of both matched and mismatched model uncertainties in an output feedback form. Theoretical analysis indicates that the proposed OFRC ensures the boundedness of all closed-loop system signals in the presence of matched and mismatched time-varying model uncertainties. Excellent asymptotic tracking performance can also be obtained when the model uncertainties are time-invariant. Comparative experimental results show that the proposed OFRC achieves significant performance improvement compared with the extensively employed PI control with velocity feedforward (VFPI).