In this paper, a non-force-sensing variable admittance control approach is proposed for lower limb rehabilitation exoskeleton robots. This approach aims to provide satisfactory assistance and rehabilitation-training performance for users of such exoskeletons. Our method initiates with a novel fixed-time sliding mode observer that estimates human-machine interaction torques according to generalized momentum. This observer-based torque estimation strategy can estimate the interaction torque precisely, thereby enabling a non-force-sensing effect. Next, a variable admittance control framework is formulated for lower limb exoskeleton robots to ensure superior compliance. This framework comprises two essential components. First, a newly designed adaptive fixed-time sliding mode controller based on a class$\kappa _{\infty } $function for the inner loop, which operates without requiring prior knowledge of the upper bound of lumped perturbations and guarantees precise gait trajectory-tracking performance. Second, a variable-parameter admittance model for the outer loop, which utilizes an exponential function to dynamically adjust the admittance parameters, thereby achieving a balance between the exoskeleton’s compliance and gait-correction efficacy. Finally, both simulation and experimental results are presented and analyzed to validate the effectiveness and superiority of the proposed non-force-sensing variable admittance control approach. Specifically, simulation results demonstrate that the root-mean-square (RMS) values of the inner-loop tracking errors for the proposed method are reduced by 26.1% and 20.4% at the hip and knee joints, respectively, compared with the top-performing benchmark algorithm. Meanwhile, the precision of the outer-loop observation is improved by 21% and 18% at these joints. Experimental validation further shows reductions of 14.2% and 20.1% in the RMS inner-loop tracking errors at the hip and knee joints, respectively, versus this benchmark algorithm.Note to Practitioners—Motivated by the problem of how to realize effective control of lower limb exoskeleton robots to provide the wearers with appropriate comfort and gait-correction effect, this paper formulates an adaptive variable admittance control framework. In the inner loop of this framework, a novel gait trajectory-tracking controller using adaptive sliding mode is designed to ensure high tracking accuracy. In the outer loop, a new adaptive observer is designed to estimate the human-robot interaction torque, and an adaptive admittance model is formulated to provide appropriate compliance for the robot. The proposed strategies are experimentally validated and can be utilized in real-word applications. The work of this paper has reference significance for the development of lower limb exoskeleton technologies.

An optimal prescribed performance control with fixed-time for fuzzy mechanical systems is explored. Specifically, a novel bounded performance function is proposed, which pre-assigns the convergence time, transient convergence trend (dynamic, variable and gentle initial stage) and steady-state tracking accuracy. More possibilities for transient performance of fuzzy systems are developed. Then, fuzzy set theory is introduced to describe system uncertainties. A fuzzy mechanical system with prescribed performance is constructed in the homeomorphism mapping space. An adaptive control method with finite-time stability is proposed. Thus, the preassigned performance is met through a two-layer collaborative convergence characteristic, rather than relying solely on performance constraint. High-order adaptive laws reduce control costs and avoid overcompensation. Control design is always deterministic rather than based on fuzzy rules. A more effective way is reflected in fuzzy-based optimization. Based on fuzzy sets to measure uncertainty, a cooperative game optimization strategy is designed to obtain the optimal decision for multiple objectives and control parameters. The best combination of performance and cost is solved. The effectiveness of the proposed method is verified via the steer-by-wire system.

This article is concerned with the leader–follower formation control problem of multiple networked mobile robots (MRs), where the information exchanges over communication networks among the MRs suffer from replay attacks. The central aim is to develop an effective secure control scheme for the multiple-MR system such that the desired leader–follower formation control objectives are accomplished regardless of the simultaneous presence of replay attacks, network-induced delays, system uncertainties, and external disturbances. Toward this aim, an extended state observer is first constructed to estimate the unknown nonlinear terms consisting of system uncertainties and external disturbances. Then, leveraging the time-stamp technique, a dedicated data packet analyzer is developed for each MR to detect and handle replay attacks. Furthermore, a secure leader–follower formation control scheme, consisting of a network-based cooperative kinematic control law and two local kinetic control laws, is designed. Finally, both simulation and experiment results are provided to validate the efficacy of the proposed secure leader–follower formation control scheme.

Advancements in computational technology have led to a shift towards automated detection processes in lung cancer screening, particularly through nodule segmentation techniques. These techniques employ thresholding to distinguish between soft and firm tissues, including cancerous nodules. The challenge of accurately detecting nodules close to critical lung structures such as blood vessels, bronchi, and the pleura highlights the necessity for more sophisticated methods to enhance diagnostic accuracy. This paper proposed combined processing filters for data preparation before using one of the modified Convolutional Neural Networks (CNNs) as the classifier. With refined filters, the nodule targets are solid, semi-solid, and ground glass, ranging from low-stage cancer (cancer screening data) to high-stage cancer. Furthermore, two additional works were added to address juxta-pleural nodules while the pre-processing end and classification are done in a 3-dimensional domain in opposition to the usual image classification. The accuracy output indicates that even using a simple Segmentation Network if modified correctly, can improve the classification result compared to the other eight models. The proposed sequence total accuracy reached 99.7%, with 99.71% cancer class accuracy and 99.82% non-cancer accuracy, much higher than any previous research, which can improve the detection efforts of the radiologist.

This article proposes a novel Stackelberg-game-oriented optimal control approach to address the bounded constraint-following control problem for uncertain mechanical systems. First, the uncertainties (possibly fast time-varying) in the system are assumed to be bounded with an unknown boundary, which lies in a specified fuzzy evidence number. In practical engineering, bounded system performance is always demanded, such as the inequality constraint. A diffeomorphism transformation approach is proposed to transform the constrained system into a restructured one satisfying the bounded constraint. Second, we propose an adaptive robust control oriented by the constraint-following control to render the restructured system to follow the specified constraints accurately with deterministic performance (guaranteeing uniform boundedness and uniform ultimate boundedness). The self-adjusting adaptive law (leakage-type) can compensate for the uncertainties and avoid overcompensation. Third, a Stackelberg-game-oriented optimization approach is proposed to obtain the optimal control parameters based on the fuzzy evidence theory. In the optimization approach, the two control parameters$\sigma$and$\varepsilon$are considered as two players with respective cost functions related to system performance and control cost. Furthermore, the optimization problem is solved by obtaining the Stackelberg strategy, which is proved to exist in analytic form. Ultimately, the permanent magnet synchronous linear motor system simulation is presented to show the design process and the excellent performance of the proposed optimal control scheme.

Rollover is a potential dangerous factor for mobile robots to accomplish a task. However, to our best knowledge, there still lacks a systematic research on the rollover mechanism and active rollover prevention control of mobile robots in the literature. This paper aims to propose a general control framework for rollover prevention of high-speed wheeled mobile robots. First, the lateral dynamics of the robot is modelled and the Load Transfer Ratio (LTR) is used as an index to measure the rollover level. Second, an optimal algorithm-based reference governor (RG) is developed, by which the wheel speed command that satisfies the constraint is induced, retaining the actual LTR within the threshold safety value. In addition, an integral sliding mode (ISM) wheel speed tracking controller is proposed. Lastly, simulations results show that for both trajectory tracking and path following cases, the controlled robot avoids possible rollover successfully.

The tracking performance of linear motor (LM) positioners is subject to payload uncertainty and external time-varying disturbances. Conventional robust controllers typically employ a high control gain that is substantially greater than the known a priori upper bound of the disturbance to ensure tracking error convergence. The main disadvantage of those controllers lies in that when the disturbance decreases, the control input is often overly generated, which then results in undesired control chattering effect or even actuator saturation. To overcome this problem, this article develops a robust tracking controller based on barrier function adaptive sliding mode (BFASM) for the LM positioners. The main benefits of BFASM are twofold: first, the controller is designed without the need for any disturbance information; second, its control gain is adaptively adjusted in terms of the amplitude of disturbance and, thus, leads to decreased control input when the disturbance becomes small. Furthermore, a modified barrier function (MBF) is proposed for applications with actuator saturation. It is proved that both the BFASM and MBF-based controllers can ensure the convergence of the tracking error into a prespecified neighborhood of zero in finite time. Experimental results on a real LM positioner demonstrate the superior properties of the developed controllers in comparison with two existing robust control schemes.

This paper is concerned with the collective behaviors of robots beyond the nearest neighbor rules, i.e., dispersion and flocking, when robots interact with others by applying an acute angle test (AAT)-based interaction rule. Different from a conventional nearest neighbor rule or its variations, the AAT-based interaction rule allows interactions with some far-neighbors and excludes unnecessary nearest neighbors. The resulting dispersion and flocking hold the advantages of scalability, connectivity, robustness, and effective area coverage. For the dispersion, a spring-like controller is proposed to achieve collision-free coordination. With switching topology, a new fixed-time consensus-based energy function is developed to guarantee the system stability. An upper bound of settling time for energy consensus is obtained, and a uniform time interval is accordingly set so that energy distribution is conducted in a fair manner. For the flocking, based on a class of generalized potential functions taking nonsmooth switching into account, a new controller is proposed to ensure that the same velocity for all robots is eventually reached. A co-optimizing problem is further investigated to accomplish additional tasks, such as enhancing communication performance, while maintaining the collective behaviors of mobile robots. Simulation results are presented to show the effectiveness of the theoretical results.

In this paper, we study the fixed-time cooperative tracking control problem for multi-robot systems with doubleintegrator dynamics. First, a novel distributed observer is proposed for each follower to estimate the leader state in a fixed time, then a local tracking controller based on sliding mode technique is proposed such that the estimated leader state is tracked in a fixed time. Both cases of a stationary leader and a dynamic leader are investigated. Since nonholonomic dynamics can better describe the mobile robots in reality, we further extend the results to achieve fixed-time cooperative tracking for multi-robot systems with nonholonomic dynamics. Different from the conventional finite-time cooperative tracking strategies, the fixed-time approach in this work guarantees that an upper bound of settling time can be prescribed without dependence on initial states of robots, which provides additional system information in advance. Finally, numerical simulations are given to demonstrate the effectiveness of the theoretical results.

Suppose a disaster happens, and several groups of robots are dispatched from distant control stations. To enable rescue staff to make collective decisions, reliable and robust connections need to be established among stations. Motivated by this scenario, a two-stage robot deployment strategy is proposed for wireless robotic networks (WRNs). In the first stage, robots in distant groups are merged into one group that covers a desirable area. Since connectivity alone cannot guarantee a high communication quality, in the second stage, the flow between any two stations is further optimized in terms of expected number of transmissions per successfully delivered packet. In both stages, a distributed collision-free controller is proposed to regulate the interactive force among robots. The stability issues of WRNs, where the proposed controller together with a class of interaction models based on an acute angle test is implemented for robots, are analyzed under both fixed and switching topology. In order to efficiently switch the neighbor set for each robot, a new energy function is constructed taking finite-time consensus into account. To guarantee that energy agreement is achieved before the next topology change, a fixed-time consensus approach is further proposed and an upper bound of the settling time for the energy agreement is obtained. Numerical simulations are provided to demonstrate the effectiveness of the two-stage deployment strategy.

This paper develops a fair and efficient congestion control framework using robust sliding mode control. It not only considers the limitations of current optimal congestion control approach, but also guarantees the performance of heterogeneous applications with different Quality of Service (QoS) requirements. By further proposing enhanced sliding mode-like congestion control algorithms, the paper addresses two critical issues raised from previous work [1], namely, the rigorous stability of the system and the sensitivity of design parameter. The thorough treatment makes the framework practically applicable, as well as retains its appealing properties. Moreover, the paper summarizes the applications of sliding mode approach in communication networks and highlights its great potential ahead.

In this paper, we treat mobility as a network control primitive in wireless robotic networks (WRNs), which consist of a number of mobile robots. The aim is to achieve better communication performance by making use of the mobility of the robots. In the literature, it has been shown that for a graph-based connectivity model, an equal-space configuration of robot nodes is optimal in terms of energy efficiency. To be more realistic, we adopt a signal to interference plus noise ratio (SINR)-based physical model in this work. With this model, we show that the equal-space configuration of robots is no longer optimal from the perspective of network interference. Instead, an equal-SINR configuration of robots is demonstrated to be optimal for a unicast topology in WRNs. Based on our network interference reduction method, a distributed mobility control algorithm is proposed to achieve the equal-SINR configuration. Simulation results verify that the proposed algorithm can drive robots to the desired positions where the network interference is minimized.

In this paper, a robust control scheme (RCS) for Steer-by-Wire (SbW) systems with partially known dynamics is proposed. It is shown that an SbW system can be represented by a nominal model and an unknown portion. A nominal feedback controller can then be used to stabilize the nominal model and a sliding mode compensator (SMC) is designed to remove the effects of both the unknown system dynamics and uncertain road conditions on the steering performance. For practical consideration, robust exact differentiator (RED) technique is utilized to estimate the derivatives of the position signals for controller design. It is further shown that the designed RCS is able to guarantee a robust steering performance against system and road uncertainties. The comparative experimental studies are given to verify the excellent performance of the proposed RCS for SbW systems.

Hysteresis and resonance dynamics are typical adverse effects associated with the piezoelectric (PZT) actuators. To eliminate the loss of positioning accuracy due to these effects, we propose a simple two degree-of-freedom (2DOF) control scheme for the PZT actuators, which consists of a feedforward and a feedback compensator for hysteresis and resonance compensation and for robust step reference tracking. Despite the proposed controller is on par with the existing 2DOF control methods, its specific structure offers significant design simplicity. The experimental results on an actual PZT nanopositioner show that the designed controller can be easily implemented and achieve superior performance for hysteresis and resonance compensation.

Narrow-band position error at mid-frequencies around the open-loop crossover frequency can not be effectively reduced using a conventional peak filter, because the attenuation of sensitivity gains has to compromise with the associated decrease of phase margin. This paper presents a general second-order filter that is applicable to reject narrow-band disturbances at any frequency range. The filter zero is designed to minimally degrade the closed-loop system stability and obtain a smooth sensitivity curve around the disturbance frequency. A nonlinear optimization procedure is developed to select the filter parameters such that the statistical position error is minimized. Experimental results of a PZT-actuated head positioning control system on spinstand demonstrate that the add-on filter can further reduce the mid-frequency PES NRRO by 8% and preserve the stability margin of the original control system