This paper delves a robust neurooptimal shared control tactic for teleoperation systems affected by model uncertainties and external disturbances. A novel shared-control structure is established with an explicitly defined and dynamically adjustable weights mechanism, enabling smooth authority transitions across control modes and consistent task execution. A neural near-optimal adaptive dynamic programming (ADP) framework firstly applied to uncertain teleoperation eschews the conventional dependence on precisely known dynamics to deliver near-optimal control. Moreover, a prescribed-time exponential disturbance observer (PPTEDO) featuring a rigorously designed continuous switching function, thereby overcomes the inherent instability and chattering pitfalls associated with discontinuous PT schemes. Under the impedance control layer, a reset regularized exponentiation sliding mode (RRESM) surface is designed to ensure fast transient-state response while elevating system transparency and improve human-robot interaction fluency. Additionally, the absolute stability of the closed-loop teleoperation system is rigorously proved via establishment of Lyapunov candidate function. Finally, experiment results are presented to demonstrate the practical availability of the designed algorithm.

State constraints are crucial for preventing system hazards by enforcing safety boundaries, yet ensuring state constraints for underactuated mechanical systems (UMSs) operating in complex environments remains unsolved due to their limited workspace and inherent underactuation. To address this critical safety issue involving unactuated states, this paper proposes a novel unified adaptive state-constraints control (UASCC) scheme based on sliding mode control (SMC) for a class of multi-input multi-output (MIMO) UMSs that do not satisfy the strict-feedback form. First, an innovative auxiliary term, integrating a nonlinear state-dependent function (NSDF) and the sliding mode variable signal, is designed to rigorously maintain all state variables within prescribed irregular time-varying constraints. Simultaneously, by designing a new time-dependent shifting function (TDSF), the proposed scheme allows for responses to irregular time-varying constraints under various scenarios without the need to modify the controller structure. Furthermore, this work eliminates the requirement for known uncertainty upper bounds in the SMC of UMSs. By introducing an adaptive time-varying control gain, the resulting continuous control scheme effectively mitigates controller gain overestimation and chattering, thereby enhancing the system’s transient performance. The system’s stability is rigorously proven through Lyapunov theory. Finally, the effectiveness of the proposed control method is experimentally validated on both a laboratory bridge crane platform and a four-degree-of-freedom (4-DOF) tower crane platform.

To alleviate the annotation burden in supervised learning, various weakly-supervised learning (WSL) configurations have emerged-such as pairwise, triplet, and N-tuple based supervision-that leverage instance-level relationships to reduce labeling costs. While these methods have demonstrated strong performance across diverse tasks, most existing methods are limited to homogeneous tuples and do not support learning from mixed-class tuples, particularly when the tuple size exceeds three. To address this gap, this paper introduces a framework called Mixed-class Triplet and Unlabeled Data (MTU) learning, which enables classification from triplets containing instances from different classes, combined with unlabeled data. We further extend this framework to a more general setting, Mixed-class N-tuple and Unlabeled (MNU) learning, which handles arbitrary-length tuples ( N$\geq$3 ) with mixed-class composition. Both MTU and MNU leverage unlabeled instances to enhance supervisory signals and improve classification performance. We formulate tailored empirical risk minimization (ERM) objectives for both learning settings and derive generalization error bounds to provide theoretical guarantees. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed methods under various weak supervision scenarios.

This article presents an advanced digital twin system constructed for a double-pendulum crane (DPC) with complex dynamic characteristics, aiming to address the pressing need for intelligent control of DPC. The high complexity and underactuated nature of the DPC system make virtual-physical mapping, tracking control, and antiswing control highly challenging. To tackle these issues, this study designs a novel adaptive proportional-integral-derivative (PID) coupling tracking control method with an automatic gain adjustment mechanism, achieving real-time and accurate motion mapping from the physical to the virtual DPC and effectively suppressing the double-pendulum swing, which is a key goal. Particularly, to effectively compensate for different lumped disturbances in both the physical and virtual DPC and improve tracking accuracy, a time-delay control strategy is integrated into the underactuated DPC system, and an adaptive method is employed to estimate the influence of state-dependent time-delay errors online, thereby relaxing conventional boundedness assumptions. Finally, synchronous motion experiments demonstrate the effectiveness and superiority of the proposed virtual-physical tracking control method.

IN THE above article [1], an incorrect abstract appeared alongside the article. The correct abstract is provided below.

For uncertain link-type underactuated mechanical systems (LUMSs), existing fully actuated system (FAS)-based approaches are often difficult to apply because the required structural conditions are not satisfied. This article develops an adaptive prescribed-time controller for a class of LUMSs by combining FAS theory with singular perturbation (SP). First, the underactuated dynamics are decomposed into slow and fast subsystems, and the reduced slow subsystem is transformed into a lower-order FAS without approximating the key nonlinear terms. Second, a novel prescribed-time gain (NPTG) is incorporated to improve the early transient response while alleviating the sharp-braking effect and reducing the risk of actuator saturation associated with conventional prescribed-time gains. In addition, a prescribed-time adaptive law is introduced without requiring a priori disturbance bounds. Lyapunov analysis establishes prescribed-time convergence of the reduced slow subsystem, while standard SP arguments justify practical regulation of the full-order closed-loop system. Experiments on an overhead crane verify the effectiveness and robustness of the proposed method.

This paper focuses on the rapid jib and trolley positioning and payload sway suppression of the 4-degree of freedom (4-DOF) tower crane systems under uncertain system dynamics, external disturbances and control input constraints. A new adaptive proportional-integral-derivative sliding mode control (PID-SMC) method is proposed, which is model-free, does not need to linearize the system, and dispense with the uncertainty upper bound information required by traditional sliding mode control (SMC). In particular, a new time-varying scale function is used to constrain the system error to ensure the given-performance, that is, the actual transient-state and steady-state control performance of the system can be predetermined according to practical application requirements, and the quantified steady-state and transient-state properties of the 4-DOF tower crane system are obtained for the first time. In addition, although the actuator saturation upper bound is completely unknown the above mentioned given-performance can also be guaranteed by designing new parameter adaptive law. Finally, the effectiveness and superior performance of the control method are verified through rigorous theoretical analysis and experiments conducted on a 4-DOF tower crane platform. Note to Practitioners—The primary objective of this study is to tackle the challenges associated with rapid target positioning and effective suppression of swing in 4-DOF tower crane systems, amid presence of multiple practical problems. Traditional SMC strategies typically presume the prior knowledge of upper bounds for uncertainties—a condition that is seldom met in actual operations. Furthermore, their disregard for transient performance criteria further curtails their applicability in real-life scenarios. To overcome these limitations, we integrate an adaptive law for the estimation of previously indeterminable uncertainty boundaries. The incorporation of a performance function also allows us to impose stringent controls over both transient-state and steady-state performance, thereby enhancing operational efficiency and guaranteeing safety. Moreover, to address the issue of actuator saturation, this paper utilizes a parameter adaptive strategy that maximizes the utilization of the actuator’s potential without resorting to overly cautious presumptions regarding controller limits. In our future work, we plan to apply and validate this control methodology in actual tower crane applications.

In this paper, a novel prescribed-time composite learning-enhanced fuzzy (PrTCLF) cooperative control approach is proposed for the multiple-master/multiple-slave(MMMS) teleoperation systems in presence of system model uncertainties and external interferences. Firstly, compared with traditional MMMS systems, a unifying virtual master-slave teleoperation control framework, notably applicative for more general situations where the number of master and slave robots is not the same, is constructed by introducing scheduled control authority for different operators, especially in cooperative control mode. A significant feature of this paper is that the first result of a novel time-dependent function integrated PrTCLF learning law rendering all the synchronization errors of uncertain MMMS teleoperation system to zero is creatively derived, by which the effect of traditional fuzzy learning error on the precision of system convergence is essentially solved. Meanwhile, in order to ensure the high efficiency and robustness of cooperative work, a new class of nonsingular prescribed-time terminal sliding mode (PrTTSM) surface is designed without any switching behavior. Besides, the sufficient conditions for maintaining the prescribed-time stability of the MMMS teleoperation system are provided through systematic Lyapunov stability analysis. Finally, the effectiveness of the control structure and algorithm is verified by a large number of simulation and experimental results.

Control design for nonstrict feedback nonlinear systems (NSFNS) with nonlower triangular structure may encounter algebraic loop problem, which is a significant, challenging, yet complicate issue to develop a controller with unknown control directions, especially in the presence of external disturbances and time-varying parameters. To solve these issues, unlike existing studies on prescribed-time stability under unknown control directions, a novel zero-error prescribed-time tracking control scheme is proposed for NSFNS based on the fuzzy logic approximation and adaptive technique. Moreover, a new switching event-triggered mechanism that focuses on triggering strategies and conditions to minimize resource wastage from continuous controller sampling is constructed. This approach effectively reduces the frequency of sampling and energy loss within the controller. In addition, the boundedness of the Lyapunov function is analyzed using invariant set theory. It demonstrates that the tracking error converges to zero within a user-defined time, which simultaneously ensures all signals of the closed-loop system be bounded and Zeno-free phenomenon. Finally, the efficacy of the proposed control algorithm is validated through numerical simulation results.

The teleoperation system of mobile manipulator has been widely applied for space exploration, medical assistance, and other fields. To improve operational efficiency, this paper presents a novel prescribed performance synchronization control scheme without applying any approximation functions for a class of teleoperation system of the mobile manipulator with time-varying delays and nonholonomic constraints. Compared with the general manipulator system, the mobile manipulator has a larger workspace and stronger operability. Yet, the heterogeneity of master-slave robots and the nonholonomic constraint of slave robot bring many difficulties to the control of the system. Firstly, a prescribed performance function (PPF) is designed to guarantee that the position synchronization errors are remained within the predetermined boundary and converge to a small preset area within a preset time. Then, the direct force feedback information is introduced into the controller to improve the transparency of the teleoperation. In addition, there are no approximation functions applied in the proposed controller, which can significantly reduce the complexity and computation of the system. Finally, simulation and experimental results are made to illustrate the availability of this method for practical application.Note to Practitioners—The motivation of this paper is to develop an algorithm to improve the steady-state and transient-state performance of mobile manipulator teleoperation system without increasing the complexity of the controller. Compared with the existing works, in this paper, the control algorithm without any approximators is proposed and a time-varying prescribed performance function is introduced, which promotes the steady-state and transient-state performance of the system essentially. At the same time, the introduction of force feedback information improves the transparency of the teleoperation system. The feasibility and effectiveness of the algorithm are verified on UR5e-Husky experimental platform. In the future, we will strive to further improve the transparency of the mobile manipulator teleoperation system in order to achieve the immersive effect of operator.

This article is devoted to the fixed-time synchronous control for a class of uncertain flexible telerobotic systems. The presence of unknown joint flexible coupling, time-varying system uncertainties, and external disturbances makes the system different from those in the related works. First, the lumped system dynamics uncertainties and external disturbances are estimated successfully by designing a new composite adaptive neural networks (CANNs) learning law skillfully. Moreover, the fast-transient, satisfactory robustness, and high-precision position/force synchronization are also realized by design of fixed-time impedance control strategies. Furthermore, the "complexity explosion" issue triggered by traditional backstepping technology is averted efficiently via a novel fixed-time command filter and filter compensation signals. And then, sufficient conditions of system controller parameters and fixed-time stability are theoretically given by establishing the Lyapunov stability theorem. Besides, the absolute stability of the two-port networked system under complex transmission time delays is rigorously proved. Finally, simulations are performed with 2-link flexible telerobotic systems under two cases, results are presented to realistically verify the proposed control algorithm available.

This article devotes to propose a novel synchronization control algorithm for networked nonlinear master–slave system under a scheduled shared-control structure to skillfully solve the problems of easy fatigue caused for continuous operation on human operators in the existing immobilized teleoperation system. Specially, a novel composite fuzzy learning control (CFLC) law is innovatively designed by incorporating the current and past values of the learning data to more effectively compensate what the system uncertainties cause. Then, a new practical preassigned fixed-time nonsingular terminal sliding-mode (PFNTSM) control algorithm with the shared-control framework is subsequently established, which is beneficial to sustain the continuity, nonsingularity, and higher robustness to system uncertainties. A significant feature of this study is that a complete force-position synchronous tracking and transparency-lifting human–robot interaction are realized. Moreover, the absolute stability of the two-port networked system under complex transmission time delays is rigorously proved. Finally, compared experimental results are presented to realistically verify the proposed control theory available.

In this article, the predefined-performance (PP) trajectory tracking control problem is investigated for a class of nonholonomic mobile manipulator systems subject to modeling errors, system uncertainties, and external disturbances. Extended frompredefined-time stability,PP stabilityis developed for the first time. In the proposed PP control framework, a predefined-time segmented time-varying sliding mode (SM) without reaching time is constructed to ensure faster zero-error tracking at the predefined time. Then, a performance function related with the symbol of error is designed to assure that the system tracking errors converge to the predetermined boundary within the preset time without evident overshoot. In this way, thePP stabilityis realized. A significant feature of this article is that thePP stabilityis guaranteed theoretically and practically in the presence of system uncertainties without utilizing any approximation function. Finally, simulation and experiment comparisons among the proposed PP control and existing conventional finite/fixed-time control are conducted to demonstrate the superior performance of the proposed control scheme.

This article proposes the design and verification of a novel barrier function (BF)-based adaptive composite sliding mode control method dedicated to the multiple-inputs–multiple-outputs underactuated systems. The proposed scheme is constructed by a composite sliding mode surface along with dual adaptive parameters. The main advantage of this scheme is that the actuated and underactuated state errors are integrated on the same proportional-integral-differential sliding mode surface, which not only enhances the coupling between the actuated and underactuated states, but also introduces an integral term in the sliding mode surface to improve performance of the system. In addition, a novel adaptive scheme of BF is proposed, which ensures that the system is bounded to converge and the range of the boundary can be set artificially even though the upper bounds of the external disturbances and the nonlinear phenomenon of the actuator are unknown. Rigorous stability analysis, based on the Lyapunov function, demonstrates the convergence of the composite sliding mode surface as well as all error states within the system. Finally, the proposed control algorithm is validated for its effectiveness and practicality through experimental results obtained from a four-degrees of freedom tower crane system.

This article studies the guaranteed performance synchronization control problem for networked bilateral teleoperation systems with system uncertainties. The communication channel connecting the master and the slave is subject to asymmetrical varying time delays with unknown upper bounds. The first result on prescribed performance synchronization control for the bilateral teleoperation system under such a weak assumption on the communication time delays is provided. Moreover, a novel composite adaptive control algorithm is proposed under a much weaker interval-excitation (IE) condition. More specifically, parameter adaptive estimation accuracy and speed are quantificationally ensured by employing a composite technique. Therefore, both steady-state performance and transient-state performance are achieved for the position synchronization and parameter estimation with the proposed control strategy. The Lyapunov function and the multidimensional small-gain framework are utilized to derive system stability criteria. It demonstrates that the allowable maximal derivatives of the transmission delays can be easily computed with the given parameters of the control algorithm and the nonlinear performance functions. Finally, both simulation and experimental results are provided to demonstrate the feasibility and superiority of the proposed composite adaptive strategy.

The main method of modern ironmaking is blast furnace ironmaking, which is a very complex nonlinear dynamic process with complex physical-chemical coupling. The hot metal is the final product of blast furnace, and its silicon content not only reflects the quality of hot metal but also characterizes the operation status of the blast furnace, so its accurate prediction is very important for the operation of the blast furnace. Given the bottleneck problem in the application of the existing prediction model of hot metal silicon content in the blast furnace, this article proposed a novel data-driven modeling method. First, a nonlinear Takagi–Sugeno (T–S) fuzzy model is constructed for the hot metal silicon content to completely capture the nonlinear dynamics of the blast furnace process. Then, considering the doubts of blast furnace operators about the predicted results of the model, the Bayesian method is used to identify the consequent parameters of the fuzzy model to obtain the probability output, to present the credibility of the predicted results. Furthermore, to improve the robustness of the fuzzy model to the initial fuzzy rules, the sparse priori is adopted to construct a compact fuzzy model with strong generalization performance, in which the key fuzzy rules were screened out. In addition, two optimization methods are derived for each of the above models. Finally, the validity of the proposed methods is verified by the test of actual blast furnace data.

For complex and difficult-to-control blast furnace systems with hour-level delay, accurate prediction of molten iron quality plays a very important role in guaranteeing the stable and smooth operation. Recently, some data-driven multi-input multi-output (MIMO) modeling methods have been proposed to model multiple molten iron quality indicators including molten iron temperature, silicon content ([Si]), phosphorus content ([P]), and sulfur content ([S]). However, those data-driven MIMO models ignore the interindicator correlation, which leads to the suboptimal model for the estimation of multiple molten iron quality indicators. Moreover, the above methods do not pay attention to the molten iron quality indicators missing issue, which often occurs on blast furnace. To address the above two issues, this article proposed a novel MIMO Takagi-Sugeno (T-S) fuzzy model by utilizing an output transfer matrix. In the novel method, the interindicator correlation was explicitly modeled by a low-rank learning of the correlation matrix that overcame the great challenge of jointly determining the fuzzy rules of the MIMO T-S model and the interindicator correlation. Moreover, a new complete complementary matrix can be obtained by the output transfer from the original incomplete matrix resulting from molten iron quality indicators missing issues. For the corresponding optimization problem, an effective alternating optimization algorithm is presented, and the convergence of the optimization algorithm is also rigorously proved. The validity of the proposed method is verified by comparison with some related methods on real blast furnace data.

Since the hot metal silicon content simultaneously reflects the product quality and the thermal state of the blast furnace, accurately predicting the development tendency of hot metal silicon content has the immensely guiding role for blast furnace operators. This paper focuses on fuzzy classifier design for the development tendency of hot metal silicon content based on blast furnace operation data. The cross characteristic of binary classification problem was found via embedding high-dimensional blast furnace data into a two-dimensional space. Then, presented a nonparallel hyperplanes based fuzzy classifier, which conquered the cross classification still holding the interpretability advantage as fuzzy classifier. The proposed method was tested on No.2 blast furnace of Liuzhou Steel in China, that demonstrated the excellent performance compared with some other classifier algorithms.

Due to the cognitive limitations of the human operator and lack of complete information about the remote environment, the work performance of such teleoperation systems cannot be guaranteed in most cases. However, some practical tasks conducted by the teleoperation system require high performances, such as tele-surgery needs satisfactory high speed and more precision control results to guarantee patient' health status. To obtain some satisfactory performances, the error constrained control is employed by applying the barrier Lyapunov function (BLF). With the constrained synchronization errors, some high performances, such as, high convergence speed, small overshoot, and an arbitrarily predefined small residual constrained synchronization error can be achieved simultaneously. Nevertheless, like many classical control schemes only the asymptotic/exponential convergence, i.e., the synchronization errors converge to zero as time goes infinity can be achieved with the error constrained control. It is clear that finite time convergence is more desirable. To obtain a finite-time synchronization performance, the terminal sliding mode (TSM)-based finite time control method is developed for teleoperation system with position error constrained in this paper. First, a new nonsingular fast terminal sliding mode (NFTSM) surface with new transformed synchronization errors is proposed. Second, adaptive neural network system is applied for dealing with the system uncertainties and the external disturbances. Third, the BLF is applied to prove the stability and the nonviolation of the synchronization errors constraints. Finally, some comparisons are conducted in simulation and experiment results are also presented to show the effectiveness of the proposed method.

The master-slave control design problem is considered for the networked teleoperation system with friction and external disturbances. A new finite-time synchronization control method is proposed with the help of adaptive fuzzy approximation. We develop a new nonsingular fast terminal sliding mode (NFTSM) to provide faster convergence and higher precision than the linear hyperplane-sliding mode and the classic terminal-sliding mode (TSM). Then, the adaptive fuzzy-logic system is employed to approximate the system uncertainties, and the corresponding adaptive fuzzy NFTSM controller is designed. By constructing Lyapunov function, the stability and finite-time synchronization performance are proved with the new controller in the presence of system uncertainties and external disturbances. Compared with the traditional teleoperation design method, the new control scheme achieves better transient-state performance and steady-state performance. Finally, the simulations are performed and the comparisons are shown among the proposed method, the P+d method, the PD+d method, the DFF method, and the classic TSM FTSM. The simulation results further demonstrate the effectiveness of the proposed method.