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This paper proposes an extreme learning machine (ELM)-based adaptive sliding mode control strategy for vehicular electronic throttle (VET) systems with parametric uncertainties, lumped uncertainty and actuator constraint. The proposed control strategy adopts a fixed-time sliding mode (FTSM) dynamical structure which ensures a fixed time convergence property for both reaching motion and sliding motion. For relaxing the upper bound information constraint of sliding mode control design, an ELM-based mechanism is utilized for learning the lumped uncertainty bound online, while the ELM output weights are updated adaptively in the sense of Lyapunov. Also, considering that the input voltage is normally limited due to the vehicular battery constraint for practical applications, to handle the input saturation scenario, an auxiliary system (AS) is further designed to guarantee a fixed time convergence of auxiliary state and the state is correspondingly fed into the controller design. The global stability analysis of the closed-loop system is rigidly given. Comparative experimental studies are conducted to illustrate the excellent performance of the proposed control.

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.

The trajectory tracking control performance of the wheeled mobile robot (WMR) is subject to position constraints, system uncertainties, and external disturbances. This paper explores the mechanism of the position constraints (equality and inequality) and considers the system uncertainties to be time-varying but bounded with fuzzy prescribed bounds. To solve the position constraints and uncertainties problems, a cooperative game-oriented optimal robust control method is proposed to provide high-precision, fast-response performance and robustness for WMR. First, a creative diffeomorphism transformation is proposed to convert the bounded state variables (subject to inequality constraints) into unbounded state variables. Accordingly, a restructured WMR system with the new state variables is established, which is free from inequality constraints. The uncertainties’ bound is described as a fuzzy number by employing fuzzy set theory, which builds the fuzzy dynamic system of the WMR. Second, the control goal for this study is to drive the WMR to follow the position constraints despite uncertainties. An optimal robust control is developed to guarantee that the tracking errors of the WMR converge to a predefined neighborhood of origin. Using Lyapunov stability analysis, a fuzzy performance index is constructed, including steady-state performance, time performance, and control effort. The fuzzy performance index is employed as the cost function based on the cooperative game theory. The optimal control design is formulated to seek the Pareto-optimal solution of control parameters. Finally, the stability analysis and comparative experiments demonstrate that the proposed control algorithm can ensure the WMR’s superior trajectory tracking convergence performance and strong robustness against uncertainties/disturbances.

The trajectory tracking control performance of nonholonomic wheeled mobile robots (NWMRs) is subject to nonholonomic constraints, system uncertainties, and external disturbances. This paper proposes a barrier function-based adaptive sliding mode control (BFASMC) method to provide high-precision, fast-response performance and robustness for NWMRs. Compared with the conventional adaptive sliding mode control, the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds. Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function. The benefit is that the overestimation of control gain can be eliminated, resulting in chattering reduction. Moreover, a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator. The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

This paper proposes a novel fixed-time sliding mode control approach for trajectory-tracking tasks of a mecanum-wheeled omnidirectional mobile robot. First, the idea of two-phase attractors is introduced into the domain of sliding mode control, and a new fixed-time sliding surface is proposed. Then, according to this sliding surface, a new type of nonsingular fast terminal sliding mode control algorithm is designed for the omnidirectional mobile robot, which can realize a fast fixed-time convergence property. The stability of the control system is proven scrupulously, and a guideline for control-parameter tuning is expounded. Finally, experiments are implemented to test the trajectory-tracking performance of the robot. Experimental results demonstrate the superiority of the proposed sliding surface and the corresponding control scheme in comparison with benchmark controllers.

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.

Obstacle avoidance is of great significance to autonomous vehicles. In this paper, an obstacle-avoidance path planning and tracking control method is developed for an autonomous vehicle. First, a path-planning algorithm based on artificial potential field is designed for the autonomous vehicle to bypass obstacles, and the generated discrete path model is transformed into a continuous one for the path-tracking control design. Then, according to the lateral dynamics and path-tracking characteristics of the vehicle, a kinematic-and-dynamic model is established for the autonomous vehicle. Afterward, an extended state observer-based adaptive recursive terminal sliding mode control scheme is proposed such that the vehicle can track the planned path precisely. The stability of the control system is proved according to the Lyapunov stability theory. Finally, MATLAB/Simulink-Carsim co-simulations are implemented to demonstrate the performance of the presented method under the conditions of different vehicle velocities and road surfaces. The simulation results indicate that the designed path-planning algorithm can plan an obstacle-avoidance path effectively, and the proposed control scheme possesses higher control precision and stronger robustness than the benchmark controllers.

This article aims to present a barrier function based adaptive sliding mode (BFASM) control scheme for a class of uncertain nonlinear systems with actuator saturation. In conventional adaptive sliding mode control for saturated systems, the adaptive gain is often monotonically increasing with respect to the persistent disturbance, which results in aggressive control input with severe chattering that is often giving rise to system instability. In order to solve this problem, this article proposes a BFASM controller with explicit consideration of actuator saturation and uncertainties. The proposed method contains a reaching control input that can adapt to the time-varying disturbances. In addition, the proposed method does not require the upper bound information of disturbance that is commonly requested in conventional sliding mode control and thus removes the conservative assumption on a sufficiently large control gain. Stability analysis proves that the tracking error under the proposed control can asymptotically converge into a prespecified region in the presence of actuator saturation, parameter uncertainties, and external disturbances. The proposed method is also applied to an air-floating positioning system with input saturation, and experimental results demonstrate its efficacy.

Advanced functional materials, drives and transmissions, sensors and mechanisms provide solid foundations for developing intelligent robots, for which modelling and simulation tools have contributed greatly to these developments, and facilitated deployments and applications of intelligent robots. Motivated by rapid developments of sensor fusion, intelligent control and machine learning tools and their widely availability at low cost, this special issue aims at disseminating new modelling and simulation methods for developing intelligent robotic systems and their applications. With the emerging applications to robotics, this focused section competitively selects the 10 research papers covering a spectrum of modeling of novel single robots, human-robot interactive systems, and even robotic automation process, which includes modeling of biped and wearable robots, parallel mechanisms, autonomous robots, and robot-environment interaction, as well as modeling with robot visions.

In this paper, an active steering control strategy is proposed to prevent rollover of a vehicle with a time-varying forward speed. The controller is designed with the aim of reducing the rollover index (RI) from an initial dangerous status to an absolutely safe status by active steering. The controller consists of two parts. The nominal control is firstly designed on the basis of the fundamental equation of constrained motion (FECM) of the vehicle, which will guarantee the nominal vehicle to track the desired states and thus rollover is prevented. To handle system uncertainties, compensatory sliding mode control (SMC) is proposed, by which the actual states are forced to track the desired ones. In the controller, the chattering problem can be alleviated by selecting a suitable performance function. To indicate the merits of the designed controller, simulation is conducted. Simulation results demonstrate that compared to conventional controllers, the proposed controller can prevent the vehicle from rollover with stronger robustness and without system chattering.

In myoelectric control, nonlinear regression models, Gaussian process (GP) in specific, have shown promising accuracy in estimation, but no study has been conducted to evaluate the real-time performance of GP regression. In this work, the real-time performance of sparse GP regression is evaluated with 17 able-bodied subjects. Unlike the existing training methods, in which training protocols are strictly pre-determined, a novel training method is proposed. The subjects' real-time performance adjusts training time and the number of training samples. While the majority of subjects showed similar learning rates, there was a significant difference between a few subjects (p< 0.05). As a result of real-time performance, the subjects completed 97% of the average tasks and achieved 80% path efficiency comparable to existing methods.

This paper developed a modified barrier function-based adaptive sliding mode control (MBFASMC) method to improve the tracking precision and robustness performance of the nonholonomic wheeled mobile robot (NWMR), which is subject to actuator saturation and external disturbance. Owing to the modified positive semi-definite barrier function (MPSDBF), the requirement for disturbance bound and the overestimation of control gain are eliminated. The nonsingular terminal sliding manifold and MPSDBF-based adaptive mechanism can guarantee that the sliding variables and the posture errors of NWMR converge to precisely predefined ultimate bounds in a finite time. In order to compensate for the adverse effect of input saturation, an auxiliary dynamics is proposed based on saturation error and used to construct the MBFASMC. Compared to the recent anti-saturation control methods, the demand for saturation level is released in the proposed control method. Moreover, a modified barrier function-based adaptive fuzzy sliding mode control (MBFAFSMC) is provided to relieve the control chattering problem. By Lyapunov analysis, it is validated that the proposed methods can ensure the pre-specified and finite-time convergence performance of the uncertain NWMR. The MBFASMC and MBFAFSMC are also applied to an actual NWMR experimental platform with artificial disturbances to verify the effectiveness of the proposed methods. • The finite-time control with predefined precision for nonholonomic wheeled mobile robot with actuator saturation is achieved. • The proposed method eliminates the requirements for saturation level and guarantees the non-overestimation for control gain. • The signal chattering issue is alleviated by introducing an adaptive fuzzy scheme without sacrificing control performance. [ABSTRACT FROM AUTHOR]

In recent years, the finite-time and fixed-time control techniques have drawn much attention. This letter will present a new method for designing a predefined-time adaptive sliding mode controller with prescribed convergent region. More specifically, class $\mathcal{K}^{1}$ function is used to construct the sliding function, and to achieve a real sliding mode, the function is also adopted in designing the adaptive gain without knowing the disturbance's upper bound (DUB). Compared to the existing finite-time and fixed-time controllers, the key superiority of the proposed method is that the system can converge to a prescribed arbitrarily small region in predefined time irrespective of the initial condition. In addition, the control signal is bounded along the settling period, where the settling time instance can be estimated without conservation. The proposed method is applicable to the control of a wide range of uncertain nonlinear systems such as networked control systems with significant network-induced delay.

This paper presents a dynamic vehicle rollover model and robust controller for rollover prevention by using a steer-by-wire (SbW) system. Firstly, a linear vehicle dynamic model is derived whose behaviours vary as a function of the time-varying vehicle speed. Next, a load transfer ratio (LTR) model is proposed for measuring the rollover conditions in a vehicle. In particular, the LTR model is generalised based on the conventional one by explicitly considering the rolling motion of sprung mass. Moreover, the relationship of LTR between steering angle and vehicle speed is analysed. To prevent the undesired rollovers, a sliding mode control (SMC) approach is then utilised to design the robust controller for rollover prevention. Finally, simulation results are shown to verify the efficiency of rollover prevention under the proposed controller and its robustness against the vehicle speed and vehicle parameter variations.

Nowadays, zero-emission electric vehicles become more and more attractive personnel transport vehicles for mining industry. Owing to the special working environment and complex road conditions, a robust controller needs to be designed to achieve a stable and reliable vehicle system for underground mining electric vehicles (UMEVs). This paper first presents the UMEV model with system uncertainties. Then, a new controller based on the sliding mode control (SMC) is designed for UMEVs to track the longitudinal velocity while maintaining longitudinal slip ratio in a desired linear region in the presence of the bounded system uncertainties. The comparison of the simulation results for both the proposed SMC controller and a traditional SMC controller shows that the proposed SMC controller has a better performance for the long-distance up/down slopes with varying rolling resistance coefficients.

This paper presents a framework for the nonlinear control of dual-stage actuators (DSA). Motivated by various nonlinear controllers that make use of sector bounded and £∞nonlinearities for the control of saturated linear systems, a methodology for integrating such nonlinear functions in order to improve the performance of DSA is presented. The stability of the closed-loop system is assessed by casting the nonlinearities in a mixed sector-bounded plus quasi-LinearParameter Varying (LPV) framework, leading to a set of linear matrix inequalities (LMIs) to be satisfied by the controller parameters. Taking advantage of the developed framework, a new £∞function is proposed to avoid the saturation of the secondary actuator. Simulation results illustrate the validity of the proposed framework and its potential for the performance improvement of DSA.

This paper studies a new nested switching control (NSC) scheme for piezoelectric (PZT) actuators to achieve fast nanopositioning. Conventional PZT controllers typically neglect the actuator saturation nonlinearity for design simplicity but at the cost of reduced performance. By optimizing a quadratic performance cost function that explicitly involves the actuator saturation, the NSC can not only guarantee the system stability in the presence of saturation but also improve the tracking speed. The experimental results on an actual PZT nanopositioner show that the new control scheme has outperformed the conventional control by more than 12% in settling time within the full PZT operational range and with nanoscale precision.

Abstract: In this paper we present a modular algorithm for interpretation of low altitude aerial images of non-urban environment. Non-urban land-covers, e.g., rivers, grass, unlike urban land-covers, have naturally unstructured boundaries and are usually containing diverse combination of colour and texture. The proposed method consists of a coarse and computationally efficient module, and a fine interpretation module. The coarse module is able to produce approximate estimations of land-covers using a single colour-base feature and contextual information. In cases when the coarse module fails, the fine module is able to accurately classify the desired land-cover. The fine module uses a combination of boundary, colour, texture and context features for accurate interpretation of the land-covers. The modular method inherits the high accuracy from the fine module and low computational expense from the coarse interpretation module. Experimental results show that the proposed algorithm can detect the target land-covers in low altitude aerial images of non-urban environment with acceptable accuracy and low computational requirements. [Copyright &y& Elsevier]

In this paper, the tracking control problem of uncertain Euler–Lagrange systems under control input saturation is studied. To handle system uncertainties, a leakage-type (LT) adaptive law is introduced to update the control gains to approach the disturbance variations without knowing the uncertainty upper bound a priori. In addition, an auxiliary dynamics is designed to deal with the saturation nonlinearity by introducing the auxiliary variables in the controller design. Lyapunov analysis verifies that based on the proposed method, the tracking error will be asymptotically bounded by a neighborhood around the origin. To demonstrate the proposed method, simulations are finally carried out on a two-link robot manipulator. Simulation results show that in the presence of actuator saturation, the proposed method induces less chattering signal in the control input compared to conventional sliding mode controllers. [ABSTRACT FROM AUTHOR]