Sideslip Angle Research Articles

Optimized Longitudinal and Lateral Control Strategy of Intelligent Vehicles Based on Adaptive Sliding Mode Control

To improve the tracking accuracy and robustness of the path-tracking control model for intelligent vehicles under longitudinal and lateral coupling constraints, this paper utilizes the Kalman filter algorithm to design a longitudinal and lateral coordinated control (LLCC) strategy optimized by adaptive sliding mode control (ASMC). First, a three-degree-of-freedom (3-DOF) vehicle dynamics model was established. Next, under the fuzzy adaptive Unscented Kalman filter (UKF) theory, the vehicle state parameter estimation and road adhesion coefficient (RAC) observer were designed to estimate vehicle speed (VS), yaw rate (YR), sideslip angle (SA), and RAC. Then, a layered control concept was adopted to design the path-tracking controller, with a target VS, YR, and SA as control objectives. An upper-level adaptive sliding mode controller was designed using RBF neural networks, while a lower-level tire force distribution controller was designed using distributed sequential quadratic programming (DSQP) to obtain an optimal tire driving force. Finally, the control strategy was validated using Carsim and Matlab/Simulink software under different road adhesion coefficients and speeds. The findings indicate that the optimized control strategy is capable of adaptively adjusting control parameters to accommodate various complex conditions, enhancing the tracking precision and robustness of vehicles even further.

High-resolution wind speed measurements with quadcopter uncrewed aerial systems: calibration and verification in a wind tunnel with an active grid

Abstract. As a contribution to closing observational gaps in the atmospheric boundary layer (ABL), the Simultaneous Wind measurement with Uncrewed Flight Systems in 3D (SWUF-3D) fleet of uncrewed aerial systems (UASs) is utilized for in situ measurements of turbulence. To date, the coefficients for the transformation terms used in our algorithm for deriving wind speeds from avionic data have only been determined via calibration flights in the free field. Therefore, we present in this work calibration and verification under laboratory conditions. The UAS measurements are performed in a wind tunnel equipped with an active grid and constant temperature anemometers (CTAs) as a reference. Calibration is performed in x- and y-coordinate directions of the UAS body frame at wind speeds of 2 … 18 m s−1. For systematic verification of the measurement capabilities and identification of limitations, different measurement scenarios like gusts, velocity steps, and turbulence are generated with the active grid. Furthermore, the measurement accuracy under different angles of sideslip (AoSs) and wind speeds is investigated, and we examined whether the calibration coefficients can be ported to other UASs in the fleet. Our analyses show that the uncertainty in measuring the wind speed depends on the wind speed magnitude and increases with extreme velocity changes and with higher wind speeds, resulting in a root-mean-square error (RMSE) of less than 0.2 m s−1 for steady wind speeds. Applying the calibration coefficients from one UAS to others within the fleet results in comparable accuracies. Flights in gusts of different strengths yield an RMSE of up to 0.6 m s−1. The maximal RMSE occurs in the most extreme velocity steps (i.e., a lower speed of 5 m s−1 and an amplitude of 10 m s−1) and exceeds 1.3 m s−1. For variances below approx. 0.5 and 0.3 m2 s−2, the maximal resolvable frequencies of the turbulence are about 2 and 1 Hz, respectively. The results indicate successful calibration but with susceptibility to high AoSs in high wind speeds, no necessity for wind tunnel calibration for individual UASs, and the need for further research regarding turbulence analysis.

Optimizing driving conditions for distributed drive electric vehicles using minimum unit encoded neural networks

Optimizing the control algorithm for distributed drive electric vehicles (DDEVs) is challenging due to the numerous parameters involved and the variability of optimal parameters across different vehicle operating states. Traditional expert-based methods fall short, and the extensive state space of vehicle operations adds complexity to the optimization process. This paper proposes a novel approach using minimum unit encoded neural networks to optimize vehicle control parameters. By reconstructing the data space of typical operating conditions and implementing Monte Carlo minimum unit encoding within the total operating condition space, this method significantly enhances the optimization efficiency of weight coefficients for multi-motor control systems. Our results show that the proposed algorithm outperforms traditional methods, achieving superior optimization across all operating conditions. Simulation results indicate that the vehicle’s longitudinal velocity error is reduced to less than 1.2%, the sideslip angle remains below 8°, and the tire slip ratio is kept under 0.08%.

An Improved ELOS Guidance Law for Path Following of Underactuated Unmanned Surface Vehicles.

In this paper, targeting the problem that it is difficult to deal with the time-varying sideslip angle of an underactuated unmanned surface vehicle (USV), a line-of-sight (LOS) guidance law based on an improved extended state observer (ESO) is proposed. A reduced-order ESO is introduced into the identification of the sideslip angle caused by the environmental disturbance, which ensures a fast and accurate estimation of the sideslip angle. This enables the USV to follow the reference path with high precision, despite external disturbances from wind, waves, and currents. These unknown disturbances are modeled as drift, which the modified ESO-based LOS guidance law compensates for using the ESO. In the guidance subsystem incorporating the reduced-order state observer, the observer estimation and track errors are proved uniformly ultimately bounded. Simulation and experimental results are presented to validate the effectiveness of the proposed method. The simulation and comparison results demonstrate that the proposed ELOS guidance can help a USV track different types of paths quickly and smoothly. Additionally, the experimental results confirm the feasibility of the method.

An adaptive estimation of ground vehicle state with unknown measurement noise

Accurate information about the vehicle state such as sideslip angle is critical for both advanced assisted driving systems and driverless driving. These vehicle states are used for active safety control and motion planning of the vehicle. Since these state parameters cannot be directly measured by onboard sensors, this paper proposes an adaptive estimation scheme in case of unknown measurement noise. Firstly, an estimation method based on the bicycle model is established using a square-root cubature Kalman filter (SQCKF), and secondly, the expectation maximization (EM) approach is used to dynamically update the statistic parameters of measurement noise and integrate it into SQCKF to form a new expectation maximization square-root cubature Kalman filter (EMSQCKF) algorithm. Simulations and experiments show that EMSQCKF has higher estimation accuracy under different driving conditions compared to the unscented Kalman filter.

Adaptive sliding mode control with nonlinear MPC-based obstacle avoidance using LiDAR for an autonomous surface vehicle under disturbances

This manuscript proposes a path-following and collision avoidance guidance and control system for an autonomous surface vehicle operating in cluttered environments under perturbations. A nonlinear model predictive control strategy handles the state and control constraints for obtaining a guidance law. The approach considers the vessel sideslip angle to provide the desired heading angle, satisfying path-following and obstacle avoidance. Additionally, to solve the nonlinear optimization problem for obtaining the guidance law in real-time, the acados software package is used. Furthermore, in order to guarantee that the vehicle tracks the guidance in presence of model uncertainties and external disturbances, a robust adaptive sliding mode low-level controller is adopted. This controller is finite time convergent, and dynamically adapts its control gain to use only the necessary control inputs. Furthermore, a low-memory and computationally-light obstacle detection strategy employing a LiDAR sensor is presented and proven to run in real-time. Simulation and experimental tests conducted using a customized prototype validate the advantages of the proposed strategy and demonstrate the effectiveness of the path following while evading multiple obstacles in the presence of external disturbances and model discrepancies.

Experimental investigation of the aerodynamics of a UAV inlet with double 90° bends

Unmanned aerial vehicle (UAV) inlets commonly have a compact S-shaped inlet structure for the sake of stealth. Most of the current studies on subsonic inlets have focused on S-shaped configurations with relatively gentle transitions of the flow channel. In this study, the aerodynamic performance and swirl flow characteristics of a UAV inlet, which has double 90° bends in the duct and is integrated with an aircraft fuselage and a volute, are studied both experimentally and numerically. The influences of angle of attack, sideslip angle, AIP (Aerodynamic Interface Plane) Mach number, and freestream airspeed are analyzed. A measure of adding two deflectors in front of the first bend and a baffle at the bottom of the volute is proposed to improve the total pressure distortion and swirl distortion characteristics of the inlet. Finally, a practice of shape optimization is performed to further improve the aerodynamic performance and swirl flow characteristics on the basis of the baseline configuration. The results indicate that the maximum and minimum total pressure recovery coefficients of the baseline inlet configuration are 0.982 and 0.969, respectively, as well as the maximum total pressure distortion index of –0.0814 and the maximum swirl distortion index of 32.1 % under normal operating conditions. Through a total of 8 iterations of optimization, the total pressure recovery coefficient is eventually improved by 0.21 % of that of the baseline configuration, as well as the total pressure distortion index, swirl distortion index and maximum swirl angle reduced by 43.6 %, 4.6 % and 11.1 %, respectively.

Research on Vehicle Stability Control Based on a Union Disturbance Observer and Improved Adaptive Unscented Kalman Filter

This paper considers external disturbances imposed on vehicle systems. Based on a vehicle dynamics model of the vehicle with three degrees of freedom (3-DOFs), a union disturbance observer (UDO) composed of a nonlinear disturbance observer (NDO) and an extended state observer (ESO) was designed to obtain external disturbances and unmodeled items. Meanwhile, an improved adaptive unscented Kalman filter (iAUKF) with anti-disturbance and anti-noise properties is proposed, based on the UDO and the unscented Kalman filter (UKF) method, to evaluate the sideslip angle of vehicle systems. Finally, a vehicle yaw stability controller was designed based on UDO and the global fast terminal sliding mode control (GFTSMC) method. The results of co-simulation demonstrated that the proposed UDO was effectively able to observe external disturbances and unmodeled items. The proposed iAUKF, which considers external disturbances, not only achieves adaptive updating and adjustment of filtering parameters under different sensor noise intensities but can also resist external disturbances, improving the estimation accuracy and robustness of the UKF. In the anti-disturbance performance test, the maximum estimation error of the sideslip angle of the iAUKF under the three working conditions was less than 0.1°, 0.02°, and 0.5°, respectively. Based on the UDO and the GFTSMC, a vehicle yaw stability controller is described, which improves the accuracy of control and the robustness of the vehicle’s stability control system and greatly strengthens the driving safety of the vehicle.

Angle-of-attack and angle-of-sideslip estimation and complementary filter design for civil aircraft

Angle-of-Attack (AOA) and angle-of-sideslip (AOS) are critical flight parameters affecting the flight safety, and their accuracy and reliability directly impact the operating status and performance of some significant airborne systems. To enhance the redundancy and accuracy of AOA and AOS, this article investigates the problem of the airflow angles estimation and complementary filter design for civil aircraft. Specifically, an extended Kalman filter based AOA and AOS estimation method considering acceleration correction is developed to increase the redundancy. Subsequently, a novel inertial AOA and inertial AOS calculation method using attitude angles, azimuth angle, and flight path angle is introduced, and two schemes for designing the discrete complementary filter based on Tustin transform are presented to improve the accuracy. Through simulations, the developed algorithms are verified, and the results illustrate that the AOA estimation error is within ± 0.6°, and the AOS estimation error is within ± 0.3°.

Utilization of genetic algorithm in tuning the hyper-parameters of hybrid NN-based side-slip angle estimators

This paper proposes a solution to enhance and compare different neural network (NN)-based side-slip angle estimators. The feed-forward neural networks (FFNNs), recurrent neural networks, long short-term memory units (LSTMs), and gated recurrent units are investigated. However, there is a lack in the selection criteria of the architectures’ hyper-parameters. Therefore, the genetic algorithm is integrated with the NN-based estimators to find the optimal hyper-parameters for the studied architectures. The tuned hyper-parameters in this work include the number of neurons, number of layers, activation function, optimizer type, and learning rate. The objective function of the optimization problem is minimizing the root-mean-square error (RMSE) on multiple testing data. The optimal models are further included in the design of a hybrid NN estimator with Kalman filter. In the hybrid estimators, the optimal NN estimators are used as virtual sensors to correct the prediction of the side-slip angle resulting from the mathematical lateral vehicle model. Eventually, the performance of the best selected model is evaluated in terms of different metrics; mean RMSE, mean error variance, mean training time, and mean estimation time. LSTMs are found to achieve the lowest mean RMSE while being tested on highly generalized data yielding the highest training and estimation time. However, FFNNs achieve the lowest RMSE while being tested on low generalized data and the lowest training and estimation time. Meanwhile, it is observed that the hybrid estimators achieved lower RMSE with great enhancement compared to the non-hybridized ones proving the effectiveness of the proposed approach and increasing the side-slip estimation generalization ability in unknown environments with high uncertainties, which are not covered by the training dataset for the NNs estimators.

Interacting multiple model-based yaw stability control for distributed drive electric vehicle via adaptive model predictive control algorithm

The vehicle lateral stability control is a challenging problem due to the tire nonlinearity and the immeasurable sideslip angle. Thus, an adaptive model predictive control (AMPC) scheme based on interacting multiple model (IMM) vehicle sideslip angle observer is proposed in this paper. First, the observer is composed of the Extended Kalman filter (EKF) and the Unscented Kalman filter (UKF), which improves the real-time performance as well as the observation accuracy. Then, a multi-objective controller based on adaptive vehicle model prediction is designed using model predictive control algorithm. This controller aims to achieve a balance between the actuation and state constraints. The T-S fuzzy algorithm is used to observe the tire cornering stiffness and design an adaptive vehicle model. By utilizing the appropriate objective function and a quadratic programing solver, the controller output is obtained to achieve vehicle stability control. Finally, the effectiveness of the designed AMPC scheme under various working conditions is verified by Carsim-Simulink joint simulation platform and hardware-in-the-loop (HIL) test.

Two-stage auxiliary drifting path tracking control for distributed driving three-axle commercial vehicles

When maneuvering corners at high speeds, commercial vehicles experience significant sideslip angles and tire force saturation, which can lead to severe traffic accidents. Incorporating intelligent driving technology to develop a controllable scheme that surpasses stability constraints and maintains the vehicle in a drift state is crucial for enhancing driving safety. Therefore, based on the model characteristics of distributed drive three-axle(DDTA) commercial vehicles, a two-stage auxiliary drift controller is proposed. In the auxiliary drift stage, time-varying model predictive control (MPC) is employed to track the desired states and achieve steady-state drift path tracking under extreme working conditions. A two-stage controller switching strategy is implemented based on road information. In the yaw stability control stage, an advanced auxiliary system facilitates cooperative control to smoothly restore tire attachment and vehicle yaw. Simulation results demonstrate that the control strategy ensures consistent path tracking performance even when adhesion of the middle and rear axle saturates and peak vehicle sideslip angle reaches 32.09°. After completing the drifting, vehicle yaw successfully returns to a stable state. Subsequently, miniaturized vehicle tests qualitatively analyze relevant conclusions by elucidating transient instability evolution in vehicles subjected to steering and distributed drive. The controllable stability boundary of the vehicle is thus expanded, thereby enhancing the engineering feasibility of drift technology.

Distributed Drive Electric Vehicle Sideslip Angle Estimation Based on the AVOA-MCSCKF Algorithm

Distributed Drive Electric Vehicle Sideslip Angle Estimation Based on the AVOA-MCSCKF Algorithm

Comparison of KF-Based Vehicle Sideslip Estimation Logics with Increasing Complexity for a Passenger Car.

Nowadays, control is pervasive in vehicles, and a full and accurate knowledge of vehicle states is crucial to guarantee safety levels and support the development of Advanced Driver-Assistance Systems (ADASs). In this scenario, real-time monitoring of the vehicle sideslip angle becomes fundamental, and various virtual sensing techniques based on both vehicle dynamics models and data-driven methods are widely presented in the literature. Given the need for on-board embedded device solutions in autonomous vehicles, it is mandatory to find the correct balance between estimation accuracy and the computational burden required. This work mainly presents different physical KF-based methodologies and proposes both mathematical and graphical analysis to explore the effectiveness of these solutions, all employing equal tire and vehicle simplified models. For this purpose, results are compared with accurate sensor acquisition provided by the on-track campaign on passenger vehicles; moreover, to truthfully represent the possibility of using such virtual sensing techniques in real-world scenarios, the vehicle is also equipped with low-end sensors that provide information to all the employed observers.

Experimental Study of the Aerodynamic Performance and Flow Characteristics of an Integrated UAV Inlet with Double 90° Bends

Many UAVs today have an S-bend inlet for the sake of stealth; however, the majority of them have a relatively gentle transition of the flow channel. This study presents an experimental investigation of the aerodynamic performance and swirl flow characteristics of a UAV inlet with double 90° bends, which is also integrated with an aircraft fuselage as well as a volute. The influences of angle of attack, sideslip angle, AIP Mach number and freestream speed are explored in detail. The influences of the deflectors installed ahead of the first 90° bend of the inlet and the baffle installed at the bottom of the volute are revealed. It is found that both the deflectors and the baffle are beneficial in enhancing the aerodynamic performance of the inlet and alleviating the intensity of the swirl flow inside the volute.

Development of an Adaptive Fuzzy Integral-Derivative Line-of-Sight Method for Bathymetric LiDAR Onboard Unmanned Surface Vessel

Previous control methods developed by our research team cannot satisfy the high accuracy requirements of unmanned surface vessel (USV) path-tracking during bathymetric mapping because of the excessive overshoot and slow convergence speed. For this reason, this study developed an adaptive fuzzy integral-derivative line-of-sight (AFIDLOS) method for USV path-tracking control. Integral and derivative terms were added to counteract the effect of the sideslip angle with which the USV could be quickly guided to converge to the planned path for bathymetric mapping. To obtain high accuracy of the look-ahead distance, a fuzzy control method was proposed. The proposed method was verified using simulations and outdoor experiments. The results demonstrate that the AFIDLOS method can reduce the overshoot by 79.85%, shorten the settling time by 55.32% in simulation experiments, reduce the average cross-track error by 10.91% and can ensure a 30% overlap of neighboring strips of bathymetric LiDAR outdoor mapping when compared with the traditional guidance law.

Twin-in-the-loop state estimation for vehicle dynamics control: Theory and experiments

In vehicle dynamics control, many variables of interest cannot be directly measured, as sensors might be costly, fragile or even not available. Therefore, real-time estimation techniques need to be used. The previous approach suffers from two main drawbacks: (i) the approximations due to model mismatch might jeopardize the performance of the final estimation-based control; (ii) each new estimator requires the calibration from scratch of a dedicated model. In this paper, we propose a twin-in-the-loop scheme, where the ad-hoc model is replaced by an accurate full-fledged simulator of the vehicle, typically available to vehicles manufacturers and suitable for the estimation of any on-board variable, coupled with a compensator within a closed-loop observer scheme. Given the black-box nature of the digital twin, a data-driven methodology for observer tuning is developed, based on Bayesian optimization. The effectiveness of the proposed estimation method for the estimation of vehicle states and forces, as compared to traditional model-based Kalman filtering, is experimentally shown on a dataset collected with a sport car. In summary, the proposed approach achieves, in the most aggressive driving scenarios, an average improvement of 0.5° for the side-slip angle estimation, and of more than 500 N for the front lateral forces estimation.

Experimental Tests and Numerical Analysis of Aerodynamic Properties of the Composite-Made Jet-Propelled Aerial Target.

The design of an aircraft's internal structure, and therefore the appropriate choice of material type, is a direct function of the performed tasks and the magnitude and type of the acting loads. The design of a durable aircraft structure with appropriate stiffness and lightness requires knowledge of the loads that will be applied to the structure. Therefore, this paper presents the results of an aerodynamic experimental test and numerical analysis of a newly designed jet-propelled aerial target. The experimental tests were carried out in a low-speed wind tunnel for a wide range of angles of attack and sideslips. Moreover, they were performed for various configurations of the airplane model. In addition, the results of the experimental test were supplemented with the results of the numerical analysis performed using computational fluid dynamics methods. During numerical analysis, specialized software based on solving partial differential equations using the Finite Volumes Method was used. This article presents the methodology of the conducted research. The results of the aerodynamic analysis are presented in the form of diagrams showing the aerodynamic force and moment components as a function of the angle of attack and sideslip. In addition, qualitative results of the flow around the plane have been presented. The results obtained prove that the adopted methods are sufficient to solve these types of problem. The aerial system was positively verified during the qualification tests of the system at the Polish Air Force training range and finally received the type certificate.

Adaptive Dynamic Model-Based Path Following Controller Design for an Unmanned Surface Vessel

Abstract The effective design of a path following controller for Unmanned Surface Vessels (USVs) under uncertain influences induced by various factors such as environmental disturbances is a challenging task. In this study, we propose to ful?lling this task through taking benefits from an online parameter identi?cation technique, the discrete-time sliding mode control (DSMC) method, and the improved line of sight (LOS) algorithm. The Particle Swarm Optimization algorithm (PSO) was adopted to provide initial settings for the straightforward online identification method, i.e., the Forgetting Factor Recursive Least Square method (FFRLS). In order to handle the time-varying sideslip angle of a ship that exists in reality due to environmental disturbances, a multi-model course control scheme is proposed to improve the control performance. For this control scheme, a flexible selection mechanism in-between a heading angle or a course angle tracking controller based on the DSMC method is designed. A solution to ?xing the tracking deviation problem of the LOS guidance law is investigated for which the gradient descent method is introduced. A series of experiments are carried out at sea with a USV called Orca to verify and validate the proposed hybrid path following approach. The results showed that tracking errors mainly induced by environmental disturbances existed but the maximum magnitude among them was small enough and remaining within the acceptable range especially from the marine engineering point of view. These results, to a high degree, validated the robustness and precision of the proposed controller.

Predefined-time prescribed performance second-order sliding mode path following control for underactuated marine surface vehicles using self-structuring NN

This paper proposes a predefined time-prescribed performance second-order sliding mode path following controller for underactuated marine surface vehicles (MSVs) with unknown external environmental disturbances based on predefined time predictor line-of-sight (PTPLOS) guidance. First, a predefined time predictor is designed to address the problem of time-varying sideslip angles, and then a guidance law is designed. Second, a prescribed performance function with predefined times is proposed, which can not only set the rate of convergence and the final convergence range but also set an upper bound on the convergence time. Third, the predefined-time controller is designed in combination with second-order sliding mode control. Then, a self-structuring neural network (SSNN) is developed to approximate external disturbances and uncertainties, and this approach can adaptively adjust the number of neurons, reducing the computational burden while maintaining high approximation accuracy. After that, the closed-loop system is proven to be predefined-time stable by using of the Lyapunov stability theory. Finally, the control method proposed in this paper is validated through numerical simulations, demonstrating its effectiveness.