Nonlinear multi-agent systems (MASs) capitalize on cooperative output regulation strategies to achieve synchronized behaviors. However, the influence of unknown time-varying parameters complicates the control design, and denial-of-service (DoS) attacks present risks to the stability of the communication topology. To this end, a prescribed-time hierarchical control framework is proposed for MASs with time-varying parameters and DoS attacks. First, a distributed resilient observer with predefined time convergence is proposed to obtain the information of the exosystem signal. To obtain the exosystem state within a predefined time and guarantee the reference signal with the existence of high-order derivatives, a high-order signal generator based on the concept of high-order filters is established, with its dynamics driven by the observer state. Then, by viewing the output of the high-order signal generator observer as the reference signal and utilizing a prescribed-time adjustment function, a prescribed-time controller is designed for each agent to solve the reference-tracking problem in the back-stepping design framework. By introducing a smooth function with positive integrable time-varying properties, novel adaptive laws are developed and incorporated into the controller design, in which a new compensation mechanism is constructed to more effectively counteract the effects of unknown time-varying parameters. It is concluded by Lyapunov stability theorem that the proposed method can not only guarantee that all the closed-loop signals are globally bounded, but also ensure that the output regulation errors converge to zero within prescribed time. Finally, an example is employed to validate the correctness and good performance of the designed algorithm.

This paper presents a novel integrated estimation-control framework to solve the path following problem for autonomous ground vehicles under coupled uncertainties in mass and cornering stiffness. The proposed method is characterized by the synergistic integration of three key innovations: 1) a fuzzy confidence-enhanced recursive least-squares algorithm (FC-RLS-MFF) that employs a rule-based mechanism to dynamically assess excitation sufficiency, ensuring robust online mass estimation; 2) a tightly-coupled identification scheme, where the estimated mass actively refines cornering stiffness in real time through tire vertical load updates, departing from the use of fixed nominal values; and 3) an adaptive fast nonsingular terminal sliding mode controller (AFNTSMC) that leverages this real-time parameter stream to enable model-based compensation, rather than mere disturbance rejection. Co-simulations, including ablation studies, validate the framework and demonstrate that this tight integration yields emergent performance gains, such as a significant reduction in path-tracking error.

This paper investigates the problem of path tracking for intelligent connected vehicles subjected to limited communication bandwidth, unknown parameters, and nonzero sideslip angles, and an adaptive event-triggered fixed-time path tracking control strategy is proposed based on the bound estimation method and smooth functions. Different from the existing control methods, the path tracking problem is first converted into a yaw angle tracking problem, where the sideslip angle compensation is incorporated into the desired yaw angle for realizing a more accurate path tracking maneuver. Then, an improved dynamic event-triggered mechanism (ETM) is designed to schedule the signal transmission intervals. Unlike traditional ETMs, the non-monotonic internal dynamic variable is designed to achieve a better tradeoff between resource utilization and system performance. Third, the lower bounds of unknown control coefficients are incorporated into the Lyapunov function to eliminate the requirement of vehicle parameter identification. Moreover, the prescribed performance constraints, formulated based on the time-varying nature of vehicle stability margins, are taken into account for the yaw stability and the transient performance improvement. Finally, the effectiveness of the proposed control method is validated by experimental results.

This paper investigates the fixed-time distributed optimization of nonlinear multi-agent systems (MASs) under switching topologies. In contrast to existing optimization strategies, this paper considers uncertain high-order dynamics and realizes fixed-time stability. To overcome the challenges brought by the coexistence of the switching topologies and high-order uncertain dynamics, a novel distributed optimization control method via two-layer framework is constructed, which consists of a Network Layer-optimization estimator design and a Physical Layer-Agents’ reference-tracking control law design. In Network Layer, the fixed-time optimal signal generator is constructed by using information interaction between agent and real-time feedback value of local gradient information over switching topologies. Then, in Physical Layer, the fixed-time fuzzy adaptive tracking control strategy is designed via backstepping technology to track the virtual signal generated from the Network-Layer. A fast fixed-time filter (FFTF) is introduced to avoid taking the derivation of discontinuous gradient functions in the process of backstepping. Furthermore, fuzzy logic systems (FLSs) are used to handle unknown nonlinear functions. By using the convex optimization theory, Lyapunov stability theory, and the fixed-time stability criterion, we analyze the convergence of the system and fixed-time stability. Finally, a simulation example is given to validate the control strategy.

This paper investigates the problem of path tracking for autonomous vehicles subject to unknown tire cornering stiffness and full state constraints. First, by analyzing the effects of nonlinear tire dynamics on path tracking performance, the tire cornering stiffnesses are treated as unknown parameters in the modeling process, which contributes to improving the robustness of the vehicle control system. Second, to handle the unknown tire cornering stiffness in system matrices and control coefficients, an adaptive fixed-time path tracking control strategy with high-gain observer-based output feedback is developed. Different from the existing methods, the lower bounds of unknown control coefficients are incorporated into the Lyapunov function to effectively eliminate the requirement of tire cornering stiffness and their bounds information. Moreover, the full state constraints, formulated based on the vehicle stability margins, are taken into account to enhance the dynamic stability of the vehicle. Based on the barrier Lyapunov stability theory, it is proven that the designed adaptive backstepping controller guarantees the fixed-time stability of the closed-loop system, and that all vehicle states remain within the constraints of stability region. Finally, the effectiveness of the proposed control scheme is demonstrated through simulation and comparison results.

Online and robust localization in a large scale outdoor environment is an essential component for self-driving vehicles. This paper addresses the theoretical and experimental development of a 6DOF localization approach with an online Unscented Rauch-Tung-Striebel (RTS) Smoother. This work focuses on the performance evaluation of the Unscented RTS smoother from a low-cost inertial sensor and consumer-grade Differential Global Positioning System (DGPS). The method is evaluated on a publicly available dataset (with centimetre accuracy benchmark) where we compare our results against the stand-alone Unscented Kaiman filter (UKF) and an offline unscented RTS smoother. The extensive evaluation against the conventional approaches demonstrates the effectiveness of our approach, capable of providing accurate/online vehicle's localization information.

Map joining is an efficient strategy for solving feature based SLAM problems. This paper demonstrates that joining of two 2D local maps, formulated as a nonlinear least squares problem has at most two local minima, when the associated uncertainties can be described using spherical covariance matrices. Necessary and sufficient condition for the existence of two minima is derived and it is shown that more than one minimum exists only when the quality of the local maps used for map joining is extremely poor. The analysis explains to some extent why a number of optimization based SLAM algorithms proposed in the recent literature that rely on local search strategies are successful in converging to the globally optimal solution from poor initial conditions, particularly when covariance matrices are spherical. It also demonstrates that the map joining problem has special properties that may be exploited to reliably obtain globally optimal solutions to the SLAM problem.

This paper demonstrates that 2D pose SLAM has an underlining near convex structure when formulated as a least squares (LS) optimization problem. By introducing new variables and some approximations, the LS pose SLAM problem can be formulated as a quadratically constrained quadratic programming (QCQP) problem. The QCQP formulation can then be relaxed into a semi-definite programming (SDP) problem which is convex. Unique solution to the convex SDP problem can be obtained without initial state estimate and can be used to construct a candidate solution to the original LS pose SLAM problem. Simulation datasets and the Intel Research Lab dataset have been used to demonstrate that when the relative pose information contain noises with reasonable level, the candidate solution obtained through the relaxation is very close to the optimal solution to the LS SLAM problem. Thus in practice, the candidate solution can serve as either an approximate solution or a good initial guess for a local optimization algorithm to obtain the optimal solution to the LS pose SLAM problem.