This paper presents an improved equivalent-input-disturbance (EID) approach to deal with exogenous disturbances and system nonlinearities. The disturbances and nonlinearities are regarded as a lumped disturbance on the control input channel. The EID approach, which has a state observer and an EID estimator, is used to estimate the lumped disturbance. There is a constraint between the observer and the estimator on the design of the control system for the conventional EID approach. A stable ze-ro was added to the estimator to remove the constraint and to improve the stability of the system in this study. This ensures that the observer and the estimator can be designed independently. An analysis of the improved EID approach explains the mechanism of disturbance rejection. The simulation and experimental results of position control of a ball-and-beam system demonstrate the validity of the method.

This paper addresses the control problem of an open-loop unstable linear time-invariant system with an input delay and an unknown disturbance. An observer-predictor-based control method is presented for such a system in which only output information is available. First, the system state is reconstructed and the disturbance is estimated using the equivalent-input-disturbance approach. Next, the future information on the state and the disturbance is predicted to reduce the effect of the input delay. Then, a new predictive control scheme is developed. The closed-loop system is simplified into two subsystems for analysis. Stability conditions are derived for each subsystem separately. Finally, a comparison with previous approaches through simulations shows the superiority of the presented method over others for disturbance rejection.

Disturbance rejection and time-delay compensation are important for control systems in practice. This article presents an observer-and-predictor-based method to reject an unknown exogenous disturbance for an input-delay system in which only delayed output is available. First, the equivalent-input-disturbance method is employed to reconstruct the system state and to estimate the disturbance. A prediction of the disturbance is calculated to reduce the effect of a phase lag caused by the delays. A new predictive scheme is designed for feedback control. Next, the system is divided into two subsystems for analysis and design: one for reference tracking and the other for disturbance rejection. Sufficient conditions are derived to guarantee the system’s stability. An analysis of the method reveals that the disturbance rejection can be viewed as a patch to the predictor-based feedback control. Finally, two case studies are used to show the validity and superiority of the method.

As solid-state LiDAR technology advances, mobile robotics and autonomous driving increasingly rely on multiple solid-state LiDARs for perception. However, limited or non-overlapping fields of view (FoV) among these sensors pose significant challenges for extrinsic calibration. Moreover, there are no quantitative indicators to evaluate calibration quality currently. To tackle these challenges, we introduce LiDAR-Link, a novel approach for calibration and evaluation, consisting of LiDAR-Bridge and LiDAR-Align. LiDAR-Bridge uses a wide-angle LiDAR to act as an intermediary, aligning point clouds from small-angle solid-state LiDARs. These small LiDARs have limited or non-overlapping FoVs, allowing us to indirectly determine the extrinsics between them. LiDAR-Align adaptively constructs a Voxel Probabilistic Plane Map to efficiently match point clouds and differentiate the contributions of various high-quality planar features. To align these point clouds, Iterated Extended Kalman Filter (IEKF) is utilized, which is based on point-to-plane residuals. The resulting covariance verifies whether point cloud alignment constraints are met and assesses calibration reliability. Furthermore, LiDAR-Align supports multi-scene joint calibration to overcome the limitations of fewer constraints caused by smaller LiDAR FoVs. We validate the accuracy of our method and the significance of observability analysis through a comprehensive set of experiments.