This paper presents a filter-based Sequential Quadratic Programming (SQP) framework for constrained optimization on Riemannian manifolds. Unlike traditional penalty-function methods, which rely on predefined penalty parameters and often involve nondifferentiable terms, the proposed approach employs a filter mechanism to balance objective reduction with constraint satisfaction, thereby enhancing global convergence and robustness. In addition to integrating the filter strategy into Riemannian SQP, the framework extends several numerical techniques from Euclidean optimization—such as second-order correction—to the manifold setting. The effectiveness of the framework is validated through two representative IoT-related applications: (1) multi-robot local formation planning, and (2) network partitioning via balanced cut, modeling clustering and resource distribution in IoT networks. Detailed modeling, algorithmic design, and simulations demonstrate that the proposed approach attains efficient and feasible convergence, demonstrating its potential for a broad class of optimization problems.

This article introduces a novel framework for achieving leader-steered (L-S) rigid formations within a multirobot vehicle system subject to nonholonomic constraints, while considering field-of-view (FOV) constraints. In contrast to the conventional separation-bearing leader-follower model, this framework incorporates a virtual leader model, established through topological and local agent connections. To achieve L-S rigid formations and address FOV constraints, a transformative approach is employed. In addition to forming L-S rigid formations, the framework ensures visibility maintenance between topologically connected vehicles using onboard cameras. This is achieved through the introduction of a continuous and continuously differentiable switching function, crucial in balancing visibility maintenance with formation adjustments, particularly when the global leader traverses trajectory segments with large curvature. To implement the framework, the distributed control protocol and the distributed observer are developed. Numerical simulations and real-world experiments demonstrate the framework's capability to achieve L-S rigid formations while accommodating FOV constraints, showcasing its practical utility and effectiveness in real-world applications.

As typical and complex mechatronic system, health state of the wind turbine (WT) is of significant importance to the sustained and reliable service. However, it is noted that influenced by the seasonal or fitful wind, WTs unavoidably serve in the dynamically varying environment. In this event, most of the currently available indicators expose deficiency in regard of the false or missed alarms due to the coupled condition interference. To address this issue and improve the reliability of the mechatronic system, a novel statistic discrepancy oriented cyclo-non-stationary (CNS) indicator is developed in this article. First, characteristics of the recorded degradation samples are revealed by a multiparametric model, during which the consistency is verified and improved by the hypothesis test. Second, a specific speed-dependent slicing (SDS) operator is then designed, aiming to alleviate the varying-speed-induced modulation interference at the different degradation stages. With this developed SDS operator, a CNS indicator, which can well adapt to the dynamically varying environment during the operating process, is subsequently developed by incorporating the resampling-based statistic discrepancy evaluating mechanism. Experiments indicate that the proposed method can effectively characterize the health state of the transmission parts of the industrial WT under varying speed conditions.

This article presents a formation control strategy for nonholonomic wheeled mobile robots that integrates both rigid and flexible formations to achieve efficient exploration and robust obstacle avoidance. A two-layer constraint framework is introduced to ensure safe and coordinated formation control. Specifically, the outer layer maintains the perception-based topology by enforcing field-of-view constraints, while the inner layer constructs control barrier functions based on directional obstacle models defined in the robot's local frame, which prevent interrobot collisions and enable safe navigation in unknown environments. Furthermore, the proposed controller guarantees predefined-time convergence and maintains formation objectives even in the presence of unknown obstacles, by using a Lipschitz continuous projection operator to enable smooth transitions between rigid and flexible formations without violating FOV constraints. Finally, the presented framework is validated through a real-world experiment, which confirms its ability to achieve coordinated formation tracking and obstacle avoidance in a complex environment.

The energy management system (EMS) of fuel cell electric buses (FCEBs) significantly affects their operational costs, and its performance is closely related to driving speed. Passenger numbers impact the stop time between stations, providing foresight for speed planning. Additionally, maintaining a stable driving speed helps reduce overall energy consumption and power fluctuations, thus extending the lifespan of the fuel cell. Therefore, this paper proposes a learning-based EMS for FCEBs, which integrates speed planning that accounts for passenger flow prediction and speed stability, improving overall vehicle performance through optimized weight coefficients. The study consists of three main parts: 1) predicting passenger flow based on weather, temperature, time, and holidays, and estimating stop times; 2) planning speed with a focus on punctuality and passenger comfort, while minimizing red light waiting time to reduce frequent stop-start cycles; 3) performing multi-objective optimization weight learning, considering passenger flow prediction and speed planning. The planned speed curve is categorized into three driving modes: acceleration, steady-state, and deceleration. The weight coefficients are learned based on the power requirements in these modes, improving the overall system performance. Finally, simulations validate the effectiveness and advantages of the proposed algorithm in passenger flow prediction, punctuality, and EMS performance optimization. This approach significantly enhances passenger trust in public transportation while reducing bus operation costs.

This article proposes a tracking model for non-holonomic constraint robots, enabling the realization of leadersteered rigid geometry formations. By employing cameras and local leader-based approaches, the challenge posed by traditional separation-bearing control methods, which are incapable of establishing rigid formation for both translational and rotational control, is resolved. In addition, to maintain the connectivity of the sensing topology, the field-of-view (FOV) constraints of the on-board cameras are integrated into the controller design. A conversion approach is used to translate the FOV constraints into a rigid geometry formation. Additionally, there is a trade-off between visibility constraints and the leader-steered rigid geometry formation, particularly when the trajectory of the global leader has significant curvature. To address this problem, a continuously smooth transition function is employed. Ultimately, a fixedtime distributed control protocol and distributed observers are developed to realize the formation framework. Experimental results demonstrate that the proposed control protocol effectively achieves rigid geometric formations and satisfies FOV constraints.

This article focuses on the problem of fixed-time path following control for the automated ground vehicles subject to the error constraints and steering dead zone. First, the mean value theorem is employed to extract the input signal embedded in the dead zone function, converting the function into an unknown time-varying control coefficient for the input signal. Then, the Nussbaum-type function is adopted to address this control coefficient in the steering system, eliminating the requirement for prior knowledge of the dead-zone property. By combining with the adaptive law, the path-following performance can be ensured even when the vehicular parameters are unknown. In addition, a fixed-time prescribed performance function is designed to constrain the preview error. Through the homeomorphic mapping transformation technique, the preview error can converge to a small region around the origin within a fixed time. Finally, the experimental studies demonstrate the superior tracking performance of the proposed control scheme.

This work focuses on the yaw stabilization of the four-wheel-independent-drive electric vehicle (FWID-EV) with the constrained active front steering (AFS) and direct yaw-moment control (DYC). First, a modified tire model is employed in the design of the unscented Kalman filter to realize the estimation of the tire-road friction coefficient (TRFC), and a backpropagation neural network is developed to online estimate the tire cornering stiffness; Second, a yaw stabilization supervisor is designed to solve the conflicts between the AFS and DYC systems, and the mode-boundary maps of the tire operating regions are utilized to generate the triggered signals so as to activate the systems; Third, a hierarchical adaptive model predictive control (MPC), including the estimation, activation, compensation, and distribution layers is proposed for yaw stabilization of the FWID-EV under the extreme conditions. Emergency maneuvers under big path curvature, low TRFC, and high vehicle speed are designed. Both software-in-the-loop and hardware-in-the-loop tests are performed to examine the effectiveness and practicability of the proposed methods, respectively.

Resorting to the principle of rigid body kinematics, a novel framework for a multirobot network is proposed to form and maintain an invariant rigid geometric shape. Unlike consensus-based formation, this approach can perform both translational and rotational movements of the formation geometry, ensuring that the entire formation motion remains consistent with the leader. To achieve the target formation shape and motion, a distributed control protocol for multiple Euler-Lagrange robotic vehicles subject to nonholonomic constraints is developed. The proposed protocol includes a novel prescribed performance control (PPC) algorithm that addresses the second-order dynamics of the robotic vehicles by employing a combination of nonsingular sliding manifold and adaptive law. Finally, the effectiveness of the proposed formation framework and control protocol is demonstrated through the numerical simulations and practical experiments with a team of four robotic vehicles.

This article proposes an observer-based discrete-time cascaded control (ODCC) strategy for lateral stabilization of Steer-by-Wire (SbW) vehicles with consideration of uncertainties and disturbances. First, for the observation of the sideslip angle and yaw rate, an information fusion-based unscented Kalman filter (IFUKF) is designed to reduce the negative effect from the variation of the parameters; Second, aiming to eliminate the errors of control variables for lateral stabilization of SbW vehicles, a discrete-time sliding mode predictive control (DSMPC) is presented to deal with matched and mismatched uncertainties and input constraint; Third, to reduce the tracking error between the actual front wheel steering angle and the desired one generated by the DSMPC, a combination of discrete-time fast terminal sliding mode and active disturbance rejection control is proposed to tackle the problems of parameter uncertainties and disturbances in the SbW system. Performance evaluations are conducted via both software-in-the-loop and hardware-in-the-loop to examine the availability and practicability of the ODCC strategy.

This paper investigates the problem of accurate voltage tracking control for direct current-direct current (DC-DC) boost converter under unknown system parameters and load. Firstly, uncertainties caused by the perturbation of the inductor, capacitor, input voltage and load are approximated by neural networks. Meanwhile, a finite-time observer is proposed to obtain the estimates of lumped uncertainty without any true parameters of the system. Finally, to improve the convergence of output voltage, a finite-time control scheme is proposed for the DC-DC boost converter. It is proven that all signals of the closed-loop system are bounded and both the estimate errors and tracking errors can converge to a small neighborhood of the origin in finite time. Numerical simulations and real-time experiments are presented to demonstrate the effectiveness and superiority of the proposed controller.

In this article, a generalized fuzzy subset (GFS) method is proposed to assess the time-varying multistate reliability of the perturbation failure coupling measurement system. First, a perturbation-failure coupling mechanism is designed to define the propagation chain of perturbations so as to integrate all the possible perturbations as the inputs of the GFS method. Second, to assess the time-varying multistate reliability, a GFS reliability model is constructed based on the composite limit state. Furthermore, the concept of the uncertain subset boundary is presented to conduct the reliability assessment via a group of embedded interval type-2 fuzzy sets. To address the deficiency of the GFS reliability model, a data-driven strategy is designed to establish the implicit relation between the limited expert knowledge and the membership function. Finally, the experimental tests are carried out to examine the superiority of the GFS method, and parametric studies are also conducted to study the reliability of the PFCM system.

Without any knowledge of the vehicle model and its parameters, a novel complete model-free path-following control strategy is developed for autonomous vehicles based on the time-delay estimation (TDE) technique. Different from the existing time-delay control (TDC) approaches, an adaptive nonsingular terminal sliding mode (ANTSM) control law is designed to stabilize the path-following errors without any information of the suitable control gain, which is significant in the conventional TDC scheme, and the boundary of the TDE error, which is necessary for the sliding-mode-based control scheme. The proposed model-free control structure can dynamically update the gain of the designed controller and the boundaries of the TDE error, and the practical finite-time convergence of the preview error can be achieved. In the HIL tests, the comparative results demonstrate that the proposed ANTSM model-free control strategy can provide superior comprehensive tracking performance over both the model-based sliding mode controller and the conventional TDC controller, while the autonomous vehicle follows desired paths.

With consideration of tire force saturation in vehicle motions, a novel path-following controller is developed for autonomous vehicles with unknown-bound disturbances and unknown actuator parameters. An adaptive sliding-mode fault-tolerant control (ASM-FTC) strategy is designed to stabilize the path-following errors without any information of disturbance boundaries, actuator fault boundaries and steering ratio from the steering wheel to the front wheels. By selecting the distance from the center of gravity to the center of percussion as the preview length, the effects of the lateral rear-tire force are decoupled and cancelled out, and then the preview error, which represents the path-following performance, can be only commanded by the front-tire force. To further address the issue of unknown tire-road friction limits, a modified ASM-FTC strategy is presented to improve the path-following performance as the lateral tire force is saturated. Simulation results show that the modified ASM-FTC controller demonstrates superior tracking performance over the normal ASM-FTC while the autonomous vehicle follows desired paths.

With the consideration of actuator faults, including the unknown steering mechanism misalignments and motor traction losses, this article presents a fixed-time control protocol to follow reference paths and velocities for autonomous ground vehicles (AGVs) with preset performance constraints. To provide sufficient large boundaries for the initial states, the hyperbolic tangent function is employed to predefine the constraints with respect to the path-following and velocity control performance. Based on the homeomorphic mapping and barrier Lyapunov theorem, the fixed-time prescribed performance control (PPC) objective-integrated fault-tolerant scheme can be achieved for the controlled AGV. In comparison to three different fixed-time controllers without the fault-tolerant or PPC scheme, the hardware-in-the-loop (HIL) test results demonstrate that the proposed control protocol can always provide superior control performance for the AGV under various maneuvering conditions.

As tire forces approach road friction limits, vehicles may always exhibit performance degradation and even instability. The actual capacity of the available road friction imposes coupled limits on a vehicle’s longitudinal and lateral accelerations. In this paper, a varying speed method is proposed to design feasible speeds and accelerations, which ensure that the autonomous vehicle will not reach the tire-road friction limits during traversing a clothoid-based path. With the consideration of uncertain traction losses and vehicle parameters, a second-order super-twisting (ST) based speed control strategy is proposed to track above feasible speeds based on varying speed method, and to stabilize the sliding-mode variable of the speed tracking error with relative degree 1. Meanwhile, a second-order quasi-continuous (QC) based path-following control strategy is proposed to follow a desired transition path, and to stabilize the sliding-mode variable of the composite path-following errors with relative degree 2. On this basis, the proposed controllers have been verified to lead good robustness for tracking the ideal speeds and following the desired paths. As compared with the boundary of the autonomous vehicle running at a constant speed, the feasible speed boundary using varying speed method without exceeding the tire-road friction limits can be enlarged up to about 1.59 times, which is decided by the direction change between the entry and exit points of the desired path.