Search

Control delay phenomena, such as time delays and actuator lags, can compromise the control performance of autonomous mobility systems, leading to increased control errors. Therefore, it is essential to develop a control delay compensation algorithm. This paper proposes a Lyapunov-based backstepping steering control algorithm to compensate for control delays in autonomous mobility systems. To estimate the control delay in the steering system, the Recursive Least Squares (RLS) algorithm was employed to calculate the time constant in real time. The control delay was estimated using an RLS designed based on a first-order differential equation. A backstepping steering controller was developed to calculate the desired steering angle using simplified error dynamics for reference path tracking. The control errors, specifically the lateral preview and yaw angle errors, were derived by calculating the path error between the current position and the waypoint. The performance of the proposed control algorithm was evaluated using the DC motor and CarMaker software 8.1.1(IPG Automotive, Karlsruhe, Germany) under scenarios involving sinusoidal input and four-curved loop and S-curved paths respectively.

This paper introduces an adaptive path-tracking control algorithm for autonomous mobility based on recursive least squares (RLS) with external conditions and covariance self-tuning. The advancement and commercialization of autonomous driving necessitate universal path-tracking control technologies. In this study, we propose a path-tracking control algorithm that does not rely on vehicle parameters and leverages RLS with self-tuning mechanisms for external conditions and covariance. We designed an integrated error for effective path tracking that combines the lateral preview distance and yaw angle errors. The controller design employs a first-order derivative error dynamics model with the coefficients of the error dynamics estimated through the RLS using a forgetting factor. To ensure stability, we applied the Lyapunov direct method with injection terms and finite convergence conditions. Each regression process incorporates external conditions, and the self-tuning of the injection terms utilizes residuals. The performance of the proposed control algorithm was evaluated using MATLAB®/Simulink® and CarMaker under various path-tracking scenarios. The evaluation demonstrated that the algorithm effectively controlled the front steering angle for autonomous path tracking without vehicle-specific parameters. This controller is expected to provide a versatile and robust path-tracking solution in diverse autonomous driving applications.

Abstract Senescence, a hallmark of aging, is a factor in age-related diseases (ARDs). Therefore, targeting senescence is widely regarded as a practicable method for modulating the effects of aging and ARDs. Here, we report the identification of regorafenib, an inhibitor of multiple receptor tyrosine kinases, as a senescence-attenuating drug. We identified regorafenib by screening an FDA-approved drug library. Treatment with regorafenib at a sublethal dose resulted in effective attenuation of the phenotypes of βPIX knockdown- and doxorubicin-induced senescence and replicative senescence in IMR-90 cells; cell cycle arrest, and increased SA-β-Gal staining and senescence-associated secretory phenotypes, particularly increasing the secretion of interleukin 6 (IL-6) and IL-8. Consistent with this result, slower progression of βPIX depletion-induced senescence was observed in the lungs of mice after treatment with regorafenib. Mechanistically, the results of proteomics analysis in diverse types of senescence indicated that growth differentiation factor 15 and plasminogen activator inhibitor-1 are shared targets of regorafenib. Analysis of arrays for phospho-receptors and kinases identified several receptor tyrosine kinases, including platelet-derived growth factor receptor α and discoidin domain receptor 2, as additional targets of regorafenib and revealed AKT/mTOR, ERK/RSK, and JAK/STAT3 signaling as the major effector pathways. Finally, treatment with regorafenib resulted in attenuation of senescence and amelioration of porcine pancreatic elastase-induced emphysema in mice. Based on these results, regorafenib can be defined as a novel senomorphic drug, suggesting its therapeutic potential in pulmonary emphysema.

This paper presents a data-driven adaptive steady state-integral-derivative (SS-ID) control algorithm that uses gradient descent and recursive least squares (RLS) with a forgetting factor. A simplified first-order differential equation of the control system was designed and its parameters were estimated in real-time using the RLS algorithm. The steady-state control input for target-state tracking was derived based on the estimated parameters and steady-state performance conditions. The gradient of the integrated control error to the gain was estimated based on the least-squares method, using the saved past error and gain data in a finite sliding window to determine the control input. The integral gain was adapted based on the gradient descent method, using the estimated gradient, integrated error, and adaptation rate. Simplified control error dynamics were designed, and their parameter was estimated using the RLS algorithm. The derivative control gain can be adapted in real time using the estimated parameters from the simplified control error dynamics and time constant-based performance conditions. The proposed controller was designed in the MATLAB/Simulink environment. A performance evaluation was conducted under various scenarios using a DC motor simulation model and an actual test platform equipped with an optical encoder.

This study proposes a universal adaptive control algorithm for an unknown multi-input multi-output (MIMO) system using recursive least squares (RLS) and parameter self-tuning. The issue of adjusting the control and system parameters in response to changes in the platform was discussed. The development of a control algorithm that can consistently achieve reliable and robust control performance in various systems is important. This study aimed to develop a control algorithm that can track the reference value for any unknown MIMO system. For the controller design, an nth-order differential error dynamic model was designed, and an RLS with a scale factor was used to estimate the coefficients of the error dynamics. In the current scenario, the numbers of control inputs and error states in the error dynamics were assumed to be equal. It was designed such that the control input is derived based on the Lyapunov stability concept using the estimated coefficients. The scale factor in the RLS and injection term in the control input based on the sliding-mode approach were computed using a self-tuning methodology. The performance of the proposed universal adaptive control algorithm was evaluated using an actual DC motor and CarMaker (version 8.1.1) software tests under various scenarios.

This paper presents an adaptive and weighted model predictive control (MPC) algorithm for autonomous driving with disturbance estimation and prediction. Unexpected and unpredictable disturbances in the real world limit the performance of MPC. To overcome this limitation, this paper proposes adaptive and weighted prediction methods with a sliding mode observer and a weighting function with the grey prediction model. The sliding mode observer is designed for disturbance estimation with finite stability conditions, and the estimated disturbance is predicted using the grey prediction model. Based on the adaptive and weighted prediction method, the length of prediction horizon and cost value of each predicted state are adjusted in real time to eliminate any negative impact on future predicted states. Meanwhile, a variation in the cost value, which is caused by prediction horizon adaptation and weighted prediction, may harm the control performance as it can excessively increase or decrease the model uncertainty. Therefore, an input weighting factor is adapted in the MPC cost function based on an exponential weighting function. The performance of the proposed adaptive control algorithm is evaluated using CarMaker software under longitudinal and lateral autonomous driving scenarios.

This paper presents a sliding mode-based adaptive fault detection and emergency control algorithm for implementation in fail-safe systems of autonomous vehicles. The overall algorithm is comprised of a fault detection part and a fail-safe control part. For the former, sliding mode observer-based fault detection algorithms were developed for environment and chassis sensors, including LiDAR, Radar, and acceleration sensors. Unidentified fault signals from the sensors are reconstructed through the adaptive sliding mode observer. The reconstruction is based on the MIT rule through the use of an estimated sensitivity parameter. For the latter, a sliding mode control (SMC)-based emergency control method designed to respond to fault occurrences has been proposed to ensure the functional safety of autonomous vehicles. An adaptive gain parameter was designed, taking convergence time into consideration, to secure consistent and rapid responses from the controller. When the detection algorithm detects a fault, the appropriate control input is computed by a lower controller for the vehicle. This control input is calculated based on the last scene information obtained from an upper controller. The performance of the proposed fault detection and control algorithms has been evaluated through simulations and actual vehicle tests of various scenarios.

This paper presents an emergency pullover algorithm for fail-safe systems designed for level-4 autonomous vehicles. The proposed algorithm utilizes feedback gain adaptation, based on sensitivity estimation, and cost-based learning. Vehicle failure within this paper does not encompass every type of failure and refers only to any situation where the upper controller or communications from the upper controller shuts down. When this type of failure occurs, the algorithm performs an emergency pullover maneuver. This maneuver does not require any form of independent control from the driver to be performed successfully. However, the highest control priority is still given to the driver if the driver intervenes during the maneuver. The feedback gain adaptation is comprised of two sections: Sensitivity Estimation and Gradient Descent (GD) based Adaptation. For Sensitivity Estimation, a relationship function has been designed with feedback gain, from the feedback gain adaptation, and changes in state error. The sensitivity of state error with respect to feedback gain can then be estimated. This estimation is done through the Recursive Least Squares (RLS) method with multiple forgetting factors through the directional forgetting method. For GD based Adaptation, state errors are applied with parameters for the cost-based learning to give Adaptation Gains. These Adaptation Gains are used in tandem with the estimated sensitivity to update the feedback gain. To reduce the number of tuning parameters required in the GD method, an additional distance condition has been proposed. This condition utilizes feedback change rates and state errors, obtained from the multi-dimensional plane of the feedback gain’s change rates. A proportional coefficient is also required as a tuning parameter for this condition. This parameter is tuned by a cost-based learning algorithm, also designed in this study. Resultantly, these methods allow the adaptive feedback controller to forgo any system information such as mathematical models and system parameters. This indicates that the vehicle model is not expected to hinder performance. Hence, controllers that do not require system information are indeed a preferable algorithm for fail-safe modules. Performance evaluations for the controller has also been conducted with actual vehicle tests, under longitudinal and lateral autonomous driving scenarios.

A new active suspension control system that replaces the existing complex hydraulic systems was proposed in this paper by reviewing recent research and development trends. We studied the actuator system characteristics, force, and damping control by conducting studies on actuator dynamics, which are yet to be studied in the literature. Moreover, many damping and active force mechanism concept studies have been conducted based on several proposed hydraulic circuits. Based on the conducted studies, a new decentralized actuator system was designed for compact and efficient vehicle control. For the piecewise control, a model-based actuator force control algorithm was proposed with consideration of the individual main component non-linearity for force application scalability. Based on a semi-active system applied to the existing commercialization, the on-demand electric pump at each wheel is integrated into the system circuit to propose a realistic, cost-effective solution. Additionally, from the vehicle control point of view, an integrated control algorithm for active suspension was developed using a model-based control method and conventional map-based inverse control methods, considering nonlinear actuator characteristics and road input disturbance. Finally, the performance of the proposed control system was evaluated using a simulation technique and an actual test platform.

The increasing complexity of mathematical models developed as part of the recent advancements in autonomous mobility platforms has led to an escalation in uncertainty. Despite the intricate nature of such models, the detection, decision, and control methods for autonomous mobility path tracking remain critical. This study aims to achieve path tracking based on pixel-based control errors without parameters in the mathematical model. The proposed approach entails deriving control errors from a multi-particle filter based on a camera, estimating the error dynamics coefficients through a recursive least squares (RLS) approach, and using the sliding mode approach and weighted injection to formulate a cost function that leverages the estimated coefficients and control errors. The resultant adaptive steering control expedites the convergence of control errors towards zero by determining the magnitude of the injection variable based on the control errors and the finite-time convergence condition. The efficacy of the proposed approach is evaluated through an S-curved and elliptical path using autonomous mobility equipped with a single steering and driving module. The results demonstrate the capability of the approach to reasonably track target paths through driving and steering control facilitated by a multi-particle filter and a lidar-based obstacle detection system.

Recently, in various fields, research into the path tracking of autonomous vehicles and automated guided vehicles has been conducted to improve worker safety, convenience, and work efficiency. For path tracking of various systems applied to autonomous driving technology, it is necessary to recognize the surrounding environment, determine technology accordingly, and develop control methods. Various sensors and artificial-intelligence-based perception methods have limitations in that they must learn a large amount of data. Therefore, a particle-filter-based path tracking algorithm using a monocular camera was used for the recognition of target RGB. The path tracking errors were calculated and a linear-quadratic-regulator-based desired steering angle were derived. The autonomous trucks were steered and driven using a pulse-width-modulation-based steering and driving motor. Based on an autonomous truck with a single steering and driving module, it was verified that the path tracking could be used in three evaluation scenarios. To compare the LQR-based path tracking control performance proposed in this paper, an elliptical path tracking scenario using a conventional sliding mode control with robust control performance was performed. The results show that the RMS of the lateral preview error of the SMC was approximately 18% larger than that of the LQR-based method.

This paper presents a sliding mode control (SMC)-based path-tracking algorithm for autonomous vehicles by considering model-free adaptive feedback actions. In autonomous vehicles, safe path tracking requires adaptive and robust control algorithms because driving environment and vehicle conditions vary in real time. In this study, the SMC was adopted as a robust control method to adjust the switching gain, taking into account the sliding surface and unknown uncertainty to make the control error zero. The sliding surface can be designed mathematically, but it is difficult to express the unknown uncertainty mathematically. Information of priori bounded uncertainties is needed to obtain closed-loop stability of the control system, and the unknown uncertainty can vary with changes in internal and external factors. In the literature, ongoing efforts have been made to overcome the limitation of losing control stability due to unknown uncertainty. This study proposes an integrated method of adaptive feedback control (AFC) and SMC that can adjust a bounded uncertainty. Some illustrative and representative examples, such as autonomous driving scenarios, are also provided to show the main properties of the designed integrated controller. The examples show superior control performance, and it is expected that the integrated controller could be widely used for the path-tracking algorithms of autonomous vehicles.