For the visual-inertial system, the key frame loss always emerges in the rotation with the weak texture and strong illumination, which seriously affects the camera positioning accuracy. To solve this problem, a tight-coupling-based visual fusion scheme leveraging line features is proposed in our work, called VinsFusion-Line. It first deeply integrates line features, inertial measurement unit(IMU) pre-integration, and stereo visual observations through a tightly-coupled framework. And the infinitely extended line features provide more geometric and structural information, compared with the base point and line feature tracing methods. Hence, the positioning accuracy is improved greatly. In order to further make the calculation of 3D spatial line features faster and more efficient, Plücker coordinates and straight lines are used to represent the features. In addition, the camera state is optimized by a multi-source cost function that embeds both IMU pre-integration residuals and line feature reprojection errors. This design enables tight-coupled fusion of IMU and binocular visual information, rather than relying solely on point features as in existing methods. Finally, experiments on the public data show that under the strong light and weak texture, the binocular VinsFusion-Line system with infinite extension line in our work has better positioning accuracy than the single-purpose Vins-Fusion system.

This work addresses the adaptive prescribed-time performance platoon control (PTP-PC) problem for heterogeneous connected autonomous vehicles, which faces two key challenges: invading vehicle tracking and unknown overall disturbance. An information protection spacing policy is proposed to tackle the issue of tracking platoon by the invading vehicle. Besides, the variable gain nonlinear extended state observers (NESOs) and variable gain hyperbolic tangent tracking differentiators (HTTDs) are designed to effectively address the overall disturbance and complexity explosion issues, respectively. To obtain the specified transient and steady-state performance for the entire platoon, a prescribed-time performance function is established. On the basis of the above presented techniques, a distributed adaptive PTP-PC scheme is developed to ensure the vehicular bistability and superior prescribed performance. For practical considerations, both numerical simulations and co-simulation experiments based on PRESCAN/SIMULINK have been conducted to simulate a traffic scenario involving the transportation of college entrance examination papers, and the results have verified the feasibility of the developed scheme.

This article investigates the security control problem for the heterogeneous autonomous vehicle platoon subjected to dual false data injection attacks, namely position sensor attack and actuator attack, as well as external disturbances. To begin with, an exponential spacing policy is introduced to mitigate the time-lag effects of existing spacing policies and further enhance traffic flow efficiency. Then, dual attacks and matched/unmatched external disturbances are modeled as the lumped disturbance for each channel of the system, and a set of neural-network-based extended state observers is developed to accurately estimate these lumped disturbances. Furthermore, to overcome initial error boundary constraints and improve platoon transient/steady-state performance, a modified prescribed-time performance security control method is proposed via a novel switch-driven error transformation function. Based on the methods mentioned above and relying on the backstepping technique, this study ensures the stable and safe driving of the heterogeneous autonomous vehicle platoon. Ultimately, the effectiveness and superiority of the presented approach are validated by simulation cases and comparative analysis results.

For electric ground vehicles, yaw moment gain-scheduling controller cannot adequately characterize vehicle handling information, especially longitudinal acceleration and calculation cost. This will increase effects of the front steering angle on vehicle stability and make the vehicle handling performance worse. Thus in our work, through LPV sampled-data systems, a yaw moment controller considering longitudinal acceleration is proposed to maximize handling performance and meanwhile minimize computational cost of vehicles. First, a novel integral inequality with the cubic term of integral interval length instead of the reciprocal one is constructed by non-orthogonal polynomials. This can fully utilize slack matrix variables and additional information about sawtooth structural sampling pattern of vehicle handling. Then, based on the constructed integral inequality, a cubic-term-dependent discontinuous exponent discontinuous Lyapunov-Krasovskii functional (LKF) including additional vehicle handling information is designed. To better estimate the upper bound of LKF derivative further, multiple convex function approximation (MCFA) approach is investigated. It can be applied to handle the two-variable polynomial negative definite problem by separating the variable interval into multiple sub-intervals. Thus, sufficient conditions with better performance of vehicle handling are derived for the feasibility of an$H_{\infty } $yaw moment sampled-data controller. In addition, an iterative algorithm is constructed through the inner convex approximation solution technique. And the parameter-dependent bilinear matrix inequalities can be further transformed into linear ones. Hence, it is easy to obtain the desired yaw moment sampled-data controller gain. Finally, the validity and superiority of our developed approaches can be verified, by comparing with previous results and implementing practical experiments. Note to Practitioners—In existing yaw moment controllers, vehicle longitudinal acceleration is not considered. This will destroy vehicle handling stability. Hence a yaw moment controller considering longitudinal acceleration is proposed through LPV systems. Meanwhile, to sufficiently make use of the bandwidth of vehicle, sampled-data controller is designed. As is well known, if the allowable maximum sampling intervals of sampled-data systems are increased, the computational cost of the vehicle can be reduced. To maximize vehicle handling performance and allowable maximum sampling intervals, a novel integral inequality is proposed and an appropriate LKF constructed. Because the constructed LKF derivative is a two-variable polynomial, MCFA is proposed to handle its negative definite problem. The sufficient stability condition via MCFA approach is bilinear matrix inequalities, which are difficult to deal with. Thus, an inner convex approximation algorithm is developed. Then the optimal yaw moment controller of vehicle lateral dynamics can also be obtained without using nonlinear solvers. Hence, a certain degree of freedom can be ensured. In summary, a yaw moment sampled-data controller can maximize vehicle handling performance while minimizing vehicle computational cost by the proposed integral inequality, MCFA approach and an inner convex approximation algorithm in this work.

It is quite desirable yet challenging to plan specified motion behaviors from cooperation to opposition in terms of practical human–robot interaction tasks if the settling time, symmetric/asymmetric constraint, limited resolution, and less bandwidth occupation are involved. Based on this fact, this work is devoted to the assimilation control of human–robot interaction, which can flexibly reshape the physical trajectory in practical interaction work. Then, the adaptive parameter estimation terms are designed to address the effect of limited resolution and unknown robotic dynamics. In particular, a novel easy-to-implement self-triggered quantized mechanism is developed, which can better balance the relationship between system performance and resource utilization. Meanwhile, benefiting from the piecewise exponential function and bias state transformation, the practically prescribed-time stability of the controlled robotic dynamics can be guaranteed. The prominent feature of this design lies in that the settling time and convergence precision can be decoupled into separately user-preassigned parameters, and the symmetric/asymmetric output constraints can be implemented in a unified framework. Afterward, experiment results on a robot system verify the benefits and efficacy of the resultant scheme.

It is greatly desirable to carry out the secure practical prescribed-time human-robot co-transportation task. The implementation of such application becomes even more theoretical and practical challenge if uncertainties in the robot model, unmeasured velocity vector and multiple-dynamic-obstacles environment are involved, yet certain behavior indices are also pursued. In this work, a settling time regulator is introduced and it is integrated with the dynamic surface-based backstepping design embedded with specific system transformation. This results in a solution that both constrained and unconstrained cases can be accommodated uniformly, concurrently, the settling time and tracking precision can be preset by user as required. Furthermore, a fuzzy velocity observer is designed with aid of the fuzzy logic technique, which is nontrivial to perform a control design of robot dynamics with unmeasured velocity vector and modeling uncertainties. In particular, benefiting from integral multiplicative barrier-Lyapunov function, an improved adaptive obstacle-avoiding controller is designed, which, without control singular issue, is capable of achieving desired tracking while avoiding obstacles encountered. The validity and benefits of the resultant control strategy are eventually substantiated via the simulation results of a two-DOF robotic manipulator.

This article investigates the singularity-free finite-time adaptive optimal control problem for coordinated robots, where the position and velocity are constrained within the asymmetric yet time-varying ranges. Different from the existing results concerning constrained control, the imposed feasibility conditions are relaxed by skillfully integrating a nonlinear state-dependent function into the backstepping design procedure. Therein, the typical feature of the designed finite-time controller lies in the application of the modified smooth switching function, rendering the designed controller powerful enough to eliminate singularity problem. Notably, with the aid of the constructed optimal cost function and neural network-based critic architecture, the optimal control law is established under the backstepping design framework. It is theoretically verified that the designed controller is of satisfied optimization and finite-time tracking ability, and desired constrained objective in the meanwhile. The validity of the resulting control algorithm is eventually substantiated via two robotic manipulators.

This article investigates the command filter-based decentralized prescribed performance adaptive fault-tolerant compensation control strategy for uncertain nonlinear large-scale systems subject to asymmetric time-varying full-state constraints. Via integrating the performance function with command filter-based backstepping technique, the prescribed performance control problem is addressed, under which the complexity of controller design is reduced. Under the prescribed performance control framework, the nonlinear transformed function is constructed so as to ensure that the asymmetric time-varying full-state constraints free from feasibility conditions imposed on virtual control signals are not violated. Besides, the effect of infinite number of time-varying actuator faults is compensated with the aid of projection adaption design. Furthermore, based on the piecewise Lyapunov function analysis, it is rigorously testified that entire involved signals are bounded, desired constraints are not breached and tracking errors within the predefined domains. Finally, the effective performances of the developed control algorithm are confirmed by some simulation results.

This article investigates global asymptotic stability for neural networks (NNs) with time-varying delay, which is differentiable and uniformly bounded, and the delay derivative exists and is upper-bounded. First, we propose the extended secondary delay partitioning technique to construct the novel Lyapunov-Krasovskii functional, where both single-integral and double-integral state variables are considered, while the single-integral ones are only solved by the traditional secondary delay partitioning. Second, a novel free-weight matrix equality (FWME) is presented to resolve the reciprocal convex combination problem equivalently and directly without Schur complement, which eliminates the need of positive definite matrices, and is less conservative and restrictive compared with various improved reciprocal convex inequalities. Furthermore, by the present extended secondary delay partitioning, equivalent reciprocal convex combination technique, and Bessel-Legendre inequality, two different relaxed sufficient conditions ensuring global asymptotic stability for NNs are obtained, for time-varying delays, respectively, with unknown and known lower bounds of the delay derivative. Finally, two examples are given to illustrate the superiority and effectiveness of the presented method.

This paper is concerned with the reliable H∞control problem for discrete-time Takagi-Sugeno (T-S) fuzzy systems with time-varying delays and stochastic actuator faults based on a novel summation inequality. A discrete-time homogeneous Markov chain is used to represent the stochastic behavior of actuator faults. By employing a fuzzy basis-dependent Lyapunov functional and the novel summation inequality, an improved sufficient condition is established to ensure that the resultant closed-loop system is stochastically stable with an H∞performance index. Meanwhile, the solvability condition for the reliable H∞control is also established, by which the reliable H∞fuzzy controller can be solved from linear matrix inequalities. A numerical example is provided to demonstrate the effectiveness of the present approach.