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Objective: This study aimed to examine and propagate the medication experience and group formula of traditional Chinese medicine (TCM) Master XIONG Jibo in diagnosing and treating arthralgia syndrome (AS) through data mining. Methods: Data of outpatient cases of Professor XIONG Jibo were collected from January 1, 2014 to December 31, 2018, along with cases recorded in A Real Famous Traditional Chinese Medicine Doctor: XIONG Jibo's Clinical Medical Record 1, which was published in December 2019. The five variables collected from the patients’ data were TCM diagnostic information, TCM and western medicine diagnoses, syndrome, treatment, and prescription. A database was established for the collected data with Excel. Using the Python environment, a customized modified natural language processing (NLP) model for the diagnosis and treatment of AS by Professor XIONG Jibo was established to preprocess the data and to analyze the word cloud. Frequency analysis, association rule analysis, cluster analysis, and visual analysis of AS cases were performed based on the Traditional Chinese Medicine Inheritance Computing Platform (V3.0) and RStudio (V4.0.3). Results: A total of 610 medical records of Professor XIONG Jibo were collected from the case database. A total of 103 medical records were included after data screening criteria, which comprised 187 times (45 kinds) of prescriptions and 1 506 times (125 kinds) of Chinese herbs. The main related meridians were the liver, spleen, and kidney meridians. The properties of Chinese herbs used most were mainly warm, flat, and cold, while the flavors of herbs were mainly bitter, pungent, and sweet. The main patterns of AS included the damp heat, phlegm stasis, and neck arthralgia. The most commonly used herbs for AS were Chuanniuxi (Cyathulae Radix), Huangbo (Phellodendri Chinensis Cortex), Cangzhu (Atractylodis Rhizoma), Qinjiao (Gentianae Macrophyllae Radix), Gancao (Glycyrrhizae Radix et Rhizoma), Huangqi (Astragali Radix), and Chuanxiong (Chuanxiong Rhizoma). The most common effect of the herbs was “promoting blood circulation and removing blood stasis”, followed by “supplementing deficiency (Qi supplementing, blood supplementing, and Yang supplementing)”, and “dispelling wind and dampness”. The data were analyzed with the support ≥ 15% and confidence = 100%, and after de-duplication, five second-order association rules, 39 third-order association rules, 39 fourth-order association rules, and two fifth-order association rules were identified. The top-ranking association rules of each were “Cangzhu (Atractylodis Rhizoma) → Huangbo (Phellodendri Chinensis Cortex)” “Cangzhu (Atractylodis Rhizoma) + Chuanniuxi (Cyathulae Radix) → Huangbo (Phellodendri Chinensis Cortex)” “Chuanniuxi (Cyathulae Radix) + Danggui (Angelicae Sinensis Radix) + Gancao (Glycyrrhizae Radix et Rhizoma) → Qinjiao (Gentianae Macrophyllae Radix)” and “Chuanniuxi (Cyathulae Radix) + Danggui (Angelicae Sinensis Radix) +Gancao (Glycyrrhizae Radix et Rhizoma) + Huangbo (Phellodendri Chinensis Cortex) → Qinjiao (Gentianae Macrophyllae Radix)”, respectively. Five clusters were obtained using cluster analysis of the top 30 herbs. The herbs were mainly drying dampness, supplementing Qi, and promoting blood circulation. The main prescriptions of AS were Ermiao San (二妙散), Gegen Jianghuang San (葛根姜黄散), and Huangqi Chongteng Yin (黄芪虫藤饮). The herbs of core prescription included Cangzhu (Atractylodis Rhizoma), Chuanniuxi (Cyathulae Radix), Gancao (Glycyrrhizae Radix et Rhizoma), Huangbo (Phellodendri Chinensis Cortex), Mugua (Chaenomelis Fructus), Qinjiao (Gentianae Macrophyllae Radix), Danggui (Angelicae Sinensis Radix), and Yiyiren (Coicis Semen). Conclusion: Clearing heat and dampness, relieving collaterals and pain, and invigorating Qi and blood are the most commonly used therapies for the treatment of AS by Professor XIONG Jibo. Additionally, customized NLP model could improve the efficiency of data mining in TCM.

This article investigates an event‐triggered adaptive estimated inverse control scheme for uncertain nonlinear systems with hysteresis effects, parametric uncertainties, and unknown disturbances. An online estimated inverse hysteresis compensation mechanism is developed, in which an adaptive technique is designed to obtain the value of unknown hysteresis parameters. Compared with common approaches, its biggest advantage lies in the fact that it eliminates the need for experimental determination of hysteresis parameters, thereby reducing time‐consuming offline identification work and enhance the compatibility of the proposed method. Additionally, an adaptive radial basis functions neural network is applied to approximate the unknown disturbances, and its weight coefficients, along with unknown system parameters, are estimated by means of the adaptive method. Furthermore, the introduction of relative threshold event‐triggered control significantly reduces communication costs resulting from the hysteresis compensation. Through Lyapunov analysis, the proposed controller guarantees all signals are bounded and the errors are convergent. Numerical simulation results demonstrate the superiority of the developed controller. [ABSTRACT FROM AUTHOR]

Manipulators are multi-rigid-body systems composed of multiple moving joints. During movement, the Coriolis force, centrifugal force, and gravity of the system undergo significant changes. The last three degrees of freedom (DOFs) of the wrist joint of a manipulator control the end attitude. Improving the command tracking accuracy of the wrist joint is a key challenge in controlling the end attitude of manipulators. In this study, a dynamics model of the mechanical arm–wrist joint is established based on the Lagrange method. An adaptive continuous robust integral of the sign of the error (ARISE) controller is designed using the reverse step method. Additionally, a linear extended state observer (LESO) is employed to estimate the time-varying interference existing in the system and compensate for it in the designed control rate. The stability of the Lyapunov function and the boundedness of the observer are proven. The proposed control method for the wrist joint is compared with other controllers on an experimental platform of multi-DOF hydraulic manipulators. The results demonstrate that the proposed method improves the control performance of hydraulic manipulators. The application of this method offers a new strategy and idea for achieving high-performance tracking control in hydraulic manipulators. [ABSTRACT FROM AUTHOR]

This paper concerns high‐accuracy tracking control and parameter estimation for semi‐strict feedback systems with unknown time‐varying parameters. The unsmooth and discontinuous parameters and matrix parameters are all taken into consideration. An optimized indirect adaptive controller based on Z‐swapping and improved least‐square algorithm is developed to handle parametric and unmolded uncertainties simultaneously. Each design index of this adaptive law can be adjusted freely according to the order of the magnitude of unknown parameters and state variables so that the coupling of the estimating process can be avoided. The Lyapunov‐based stability analysis illustrates that the proposed method can obtain a prescribed estimating result as well as transient and final tracking performance. Simulations based on two common systems verify the outstanding performance of the proposed method. [ABSTRACT FROM AUTHOR]

Parametric uncertainties and unmodeled disturbances limit the tracking performance improvement of DC motor systems. In this paper, a third-order continuous adaptive integral recursive terminal sliding mode (3-AIRTSM) control scheme is formulated for high-performance motion control of DC motors. The residual parameter uncertainties and unmodeled disturbances are synthesised as the lumped uncertainties. Then, the derivative of the lumped uncertainties is analysed whose upper bound is denoted by system state and control signal dependent terms with unknown coefficients. Meanwhile, a novel third-order sliding mode structure including integral element is constructed to obtain the chattering-free control input. In the proposed controller, the equivalent control term functions as feedforward model compensation, and the reaching control input handles the lumped uncertainties with adaptive gain coefficients, which reduces the design conservation and avoids the high-gain feedback issue. The closed-loop system stability analysis indicates that both tracking error and gain estimation errors converge to zero in finite time under the proposed controller. Comparative experimental results verify the proposed 3-AIRTSM controller can realize the best tracking performance with the modest or the lowest control effort, compared with 3-IRTSM and 3-RTSM controllers. [ABSTRACT FROM AUTHOR]

As a critical support and fixed component of aero engines, electro-hydrostatic actuators, and other equipment, the operation of aviation bearings is often subject to high speed, high-temperature rise, large load, and other continuous complex fluctuation conditions, which makes their health assessment tasks more difficult. To solve this problem, an intelligent health assessment method based on a new Deep Transfer Graph Convolutional Network (DTGCN) is proposed for aviation bearings under large speed fluctuation conditions. First, a new DTGCN algorithm is designed, which mainly uses the domain adaptation mechanism to enhance the performance of Graph Convolutional Network (GCN) and the generalization performance of transfer properties. Specifically, order spectrum analysis is employed to resample the vibration signals of aviation bearings and transform them into order spectral signals. Then, the trained 1dGCN is used as the feature extractor, and the designed Dynamic Multiple Kernel Maximum Mean Discrepancy (DMKMMD) is calculated to match the difference in edge distribution. Finally, the aligned features are fed into the softmax classifier for intelligent health assessment. The effectiveness of the proposed diagnostic algorithm and method are validated by using aviation bearing fault data set under large speed fluctuation conditions. [ABSTRACT FROM AUTHOR]

Multiaxial hydraulic manipulators are complicated systems with highly nonlinear dynamics and various modeling uncertainties, which hinders the development of high‐performance controller. In this paper, a neural network feedforward with a robust integral of the sign of the error (RISE) feedback is proposed for high precise tracking control of hydraulic manipulator systems. The established nonlinear model takes three‐axis dynamic coupling, hydraulic actuator dynamics, and nonlinear friction effects into consideration. A radial basis function neural network (RBFNN) is synthesized to approximate the uncertain system dynamics and external disturbance, which can greatly reduce the dependence on accurate system model. In addition, a continuous RISE feedback law is judiciously integrated to deal with the residual unknown dynamics. Since the major unknown dynamics can be estimated by the RBFNN and then compensated in the feedforward design, the high‐gain feedback issue in RISE feedback control will be avoided. The proposed RISE‐based neural network robust controller theoretically guarantees an excellent semi‐global asymptotic stability. Comparative simulation is performed on a 3‐DOF hydraulic manipulator, and the obtained results verify the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper focuses on high-accuracy motion control for electromechanical servo systems with time-varying parameter estimation. A robust indirect adaptive controller based on a nonlinear X-swapping technique is proposed to achieve the online estimation of the time-varying parameters. The time-varying parametric uncertainties can be well compensated via the adaptive feedforward cancellation technique, and the residual model uncertainties are suppressed by a nonlinear robust control law. The Lyapunov-based stability analysis illustrates that the proposed method obtains a prescribed estimating result and an excellent asymptotic tracking performance. Extensive experimental results are acquired to affirm the applicability of the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this article, a command filtered asymptotic adaptive prescribed performance control method is presented for nonlinear systems with time‐variant parameters, input delay, and external disturbances. The feature of the concerned system is more general and practical, which makes the controller development more complicated. By exquisitely constructing a set of auxiliary systems, both the command filtered errors and input delay are actively compensated simultaneously. Then a smooth nonlinear term with an updated gain is introduced into the controller design to concurrently compensate for time‐variant parameters and external disturbances. With the integration of the command filtered technique, the adaptive control and the prescribed performance function, a command filter‐based adaptive prescribed performance control framework is skillfully exploited, in which tracking errors of all the states are restricted into the preset regions. Based on the Lyapunov theory, both the transient and zero‐error asymptotic convergence performance can be guaranteed with the developed control method. Finally, simulation results demonstrate the preponderance of the exploited approach. [ABSTRACT FROM AUTHOR]

Summary: This article develops a high‐precision tracking control without velocity measurement for electro‐hydraulic actuators subject to mismatched and matched disturbances. Compared with previous studies, the presented control method is attractive from a practical point of view. First, velocity signal is often approximately derived by numerical differentiation on position signal, nevertheless, accompanied heavy noise will severely deteriorate tracking performance. Thus, to avert using noise‐polluted velocity signal, a third‐order super‐twisting algorithm (STA) is developed to achieve velocity estimation. Second, the STA‐based disturbance estimator is employed to estimate and compensate mismatched and matched disturbances in finite time. Then a composite STA‐based control law is constructed to achieve a velocity‐free high‐performance tracking control, in which an employed modified STA only with an adjustable gain is easier to be performed for practical implementation. The stability analysis reveals the proposed controller is asymptotically stable while existing time‐variant disturbances. Comparative experiment results validate the superiority of the presented method. [ABSTRACT FROM AUTHOR]

The rotor-bearing system is widely used in various high-end electro-hydraulic equipment, which provides specific support, rotation, and other integral functions. However, the fluctuating working conditions of the rotor-bearing system will cause more significant disordered fluctuations in the measured signals. This article proposes a new algorithm called multiscale deep graph convolutional networks (MS-DGCNs) to alleviate this problem. The designed MS-DGCNs algorithm combines a new multiscale intra-class fine coarse-grained processing and multiscale graph convolution kernels. Accordingly, an intelligent fault diagnosis method based on MS-DGCNs for the rotor-bearing system under fluctuating conditions is designed to learn more feature representations and accuracy. First, a sliding window is employed to divide the collected vibration signals into a series of subsignals. The multiscale signal processing is performed to obtain different degrees of the fine-coarse time series. Then, a graph convolution with the multiscale convolution kernel is designed. Finally, the soft-max classifier is combined for intelligent fault diagnosis. The experimental results of the double-span rotor-bearing system under fluctuating conditions well demonstrate that the method has the higher accuracy and generalization. [ABSTRACT FROM AUTHOR]

Summary: This article investigates the precise motion control of multi‐degrees of freedom (DoF) hydraulic manipulators, and proposes an active disturbance rejection adaptive control (ADRAC) architecture based on full state feedback with consideration of both unmatched and matched uncertainties, that is, parametric uncertainties and uncertain nonlinearities. The new control strategy integrates adaptive control with extended state observer (ESO) via the backstepping method. Parametric uncertainties are handled by the synthesized adaptive law, and ESO is designed to estimate the remaining uncertainties before being compensated in a feedforward way. In particular, the unmatched and matched uncertainties can be observed in real‐time by constructing an ESO for each channel of the hydraulic manipulator system. The burden on ESO should be greatly reduced since parameter adaptation offsets most parametric uncertainties. Accordingly, high‐gain feedback is avoided in the resulting controller, and improved servo performance can thus be expected. In theory, the suggested ADRAC controller can attain asymptotic tracking performance under the influence of not only parametric uncertainties derived from various hydraulic parameters but also invariant disturbances. Furthermore, when uncertain nonlinearities become time‐variant, prescribed transient tracking performance and final tracking accuracy can also be ensured. Comparative simulations concerning both matched and unmatched uncertainties are obtained to illustrate the high tracking performance nature of the developed control algorithm. [ABSTRACT FROM AUTHOR]

A dual-cylinder erection mechanism, in which two telescopic cylinders physically connect to a load, is a nonlinear system with model uncertainties and coupled dynamics. In this paper, a novel synchronous control algorithm with thrust-allocation law is proposed for eliminating the excessive internal forces caused by the unbalanced rotation and lateral moments during the erection process. With regulated internal forces, the "pull and drag" issue is attenuated and better synchronization performance is attained. For improved tracking accuracy, the inter-stage collision dynamics of the telescopic cylinder are considered for model compensation to enhance stage-changing and in-position performance. A radial basis function (RBF) neural network is utilized to estimate the model uncertainties and external disturbances, which alleviates reliance upon the accuracy of a system model for controller implementation. As a result, theoretical analysis revealed that the semi-global asymptotic stability and synchronized motion performance with decreased internal forces can be achieved via the presented synchronous controller with thrust-allocation strategy. Contrasting simulations were implemented on a multi-link erection mechanism and the results confirmed the superiority and effectiveness of the proposed synchronous control algorithm. [ABSTRACT FROM AUTHOR]

In this article, the motion control problem of hydraulic lifting systems subject to parametric uncertainties, unmodeled disturbances, and a valve dead-zone is studied. To surmount the problem, an active disturbance rejection adaptive controller was developed for hydraulic lifting systems. Firstly, the dynamics, including both mechanical dynamics and hydraulic actuator dynamics with a valve dead-zone of the hydraulic lifting system, were modeled. Then, by adopting the system model and a backstepping technique, a composite parameter adaptation law and extended state disturbance observer were successfully combined, which were employed to dispose of the parametric uncertainties and unmodeled disturbances, respectively. This much decreased the learning burden of the extended state disturbance observer, and the high-gain feedback issue could be shunned. An ultimately bounded tracking performance can be assured with the developed control method based on the Lyapunov theory. A simulation example of a hydraulic lifting system was carried out to demonstrate the validity of the proposed controller. [ABSTRACT FROM AUTHOR]

This article focuses on the asymptotic tracking control problem for uncertain nonlinear systems subject to both multiple disturbances and parametric uncertainties. To address this issue, a parameter adaptation law is synthesized to deal with the parametric uncertainties, and an adaptive-gain disturbance estimator (ADE) is constructed to estimate the mismatched and matched disturbances, and compensate them in feedforward channels, which eliminates the impact of disturbances on tracking performance. Meanwhile, an updated law for estimator gain driven by the estimation errors is utilized in the ADE when facing unknown upper bounds of disturbances, which reduces the conservatism of estimator gain selection and is beneficial to practical implementation. Based on the parameter adaption technique and the presented ADE approach, a composite controller is proposed to ensure an excellent asymptotic output tracking performance. The stability analysis shows the proposed controller can attain asymptotic tracking performance in the presence of both time-variant disturbances and parametric uncertainties. Comparative simulation results of the application to a robot manipulator reveal the validity of the developed approach. [ABSTRACT FROM AUTHOR]

Uncertainties, especially mismatched uncertainties, pose great challenges to high accuracy tracking controller design for mechanical servosystems. In this article, a novel recursive robust integral of the sign of the error (RISE) control method is proposed for mechanical servosystems with mismatched uncertainties. In the controller development, two auxiliary error signals are introduced into the recursive backstepping design framework, and then, RISE feedbacks are synthesized to eliminate the matched and mismatcheduncertainties simultaneously. Moreover, to reduce the design conservatism, an adaptive recursive RISE control law is also developed for mechanical servosystems suffering from both parametric uncertainties and unmodeled disturbances, in which desired-trajectory-based adaptation law is synthesized to achieve compensation for parametric uncertainties. The proposed control methods can theoretically achieve remarkable asymptotic tracking performance with zero steady-state error in spite of matched and mismatched time-variant uncertainties. The proposed controllers are applied to an actual hydraulic servosystem and comparative experiments are performed to verify their effectiveness. [ABSTRACT FROM AUTHOR]