Real-time visual object tracking (VOT) may suffer from performance degradation and even divergence owing to inaccurate noise statistics typically engendered by non-stationary video sequences or alterations in the tracked object. This paper presents a novel adaptive Kalman filter (AKF) algorithm, termed AKF-ALS, based on the autocovariance least square estimation (ALS) methodology to improve the accuracy and robustness of VOT. The AKF-ALS algorithm involves object detection via an adaptive thresholding-based background subtraction technique and object tracking through real-time state estimation via the Kalman filter (KF) and noise covariance estimation using the ALS method. The proposed algorithm offers a robust and efficient solution to adapting the system model mismatches or invalid offline calibration, significantly improving the state estimation accuracy in VOT. The computation complexity of the AKF-ALS algorithm is derived and a numerical analysis is conducted to show its real-time efficiency. Experimental validations on tracking the centroid of a moving ball subjected to projectile motion, free-fall bouncing motion, and back-and-forth linear motion, reveal that the AKF-ALS algorithm outperforms a standard KF with fixed noise statistics.

It is a challenge to design a satisfactory controller for a complex multivariable industrial system with minimal offsetting and a slow response. An internal model control method is proposed for rank-deficient systems with a time delay based on a damped pseudo-inverse. An internal model control was designed to obtain the desired dynamic characteristics of the system by transforming the time-delay system into a system without a time delay, following the Pade approximation approach. By introducing a damping factor, the internal model controller was designed based on a damped pseudo-inverse, since the inverse matrix of the rank-deficient system does not exist. Furthermore, a singular value decomposition was used to analyze the steady-state performance of the system. The selection of the damping factor was also presented, and a μ analysis was made to evaluate the stability of the system. To demonstrate the effectiveness of the proposed method, a crude distillation process with five inputs and four outputs was considered as an example. The simulation results illustrate that not only can the proposed strategy guarantee the system’s stability, but it also has a relatively good dynamic performance.

As one of the sub-systems for comprehensive research facility for fusion technology (CRAFT), the SC material testing facility will be used for performance research on superconducting wires/tapes, structural materials, insulations, and other sub-elements of fusion magnet systems. The SC material testing facility consists of 6 platforms including the superconducting material performance research platform, the AC loss research platform, the structure material performance research platform, the thermo-hydraulic research platform, the non-destructive examination technology research platform, and the high voltage research platform. The following key technologies are expected to be solved during the establishment of the platform: the design and manufacturing technologies of a 19 T hybrid superconducting magnet with an aperture larger than 70 mm, the design of the multifunctional critical current characterization sample holders, precise control of the vertical and radial deviation of a loading system simulating cyclic loads for AC loss measurement, the establishment of multi-media and complex structural parts simulation model for NDE. The SC material testing facility is planned to be accomplished and open by the end of 2024.

Model predictive control (MPC) is an effective method in lateral-longitudinal integrated control with safety guarantee for autonomous vehicles. But its computational burden is significant, making it challenging to meet real-time requirements. The contribution of this paper is to propose an explicitly solvable autonomous vehicle motion controller with lateral-longitudinal integrated characteristics and safety guarantee, achieved by integrating input-output controllers from exponential control Lyapunov function (ECLF) and exponential control barrier function (ECBF). We performed simulations using a high-fidelity dynamics model in CarSim for simple urban traffic scenarios. The simulation results demonstrate that our designed controller has good trajectory tracking performance, safety guarantee, low computational burden and a certain level of robustness.

Mapless autonomous navigation in constrained pedestrian environments requires mobile robots to transform local semantic and geometric perception into physically executable motion commands. This paper presents a semantic-geometric fusion navigation framework for a quadruped robot operating on tactile paving with static and dynamic obstacles. A morphology-based topological reconstruction module converts visual semantic masks into continuous centerline references without relying on pre-built maps, while also improving robustness to local fractures and noise. Visual tactile-paving trajectories and LiDAR obstacle observations are then projected into a unified robot body frame, providing a metric representation for local planning. Based on this representation, a task-specific nonlinear model predictive control (NMPC) scheme integrates semantic path tracking, geometric obstacle avoidance, angular-velocity response approximation, command smoothing, and execution-layer safety filtering. Experiments on a Unitree Go2 quadruped robot equipped with RGB-D and LiDAR sensors demonstrate improved path coverage, gap-repair accuracy, collision-free traversal, and reduced command oscillation compared with conventional path extraction and local planning baselines. The results show that the proposed framework improves the robustness and executability of quadruped navigation in tactile-paving environments.