Sideslip Angle Research Articles

Approximate analytical solutions for three-dimensional ascent trajectory of a solid-fuel launch vehicle with time-varying mass flow rate

In the scenario that a solid-fuel launch vehicle maneuvers in outer space at high angles of attack and sideslip for energy management, Approximate Analytical Solutions (AAS) for the three-dimensional (3D) ascent flight states are derived, which are the only solutions capable of considering time-varying Mass Flow Rate (MFR) at present. The uneven MFR makes the thrust vary nonlinearly and thus increases the difficulty of the problem greatly. The AAS are derived based on a 3D Generalized Ascent Dynamics Model (GADM) with a normalized mass as the independent variable. To simplify some highly nonlinear terms in the GADM, several approximate functions are introduced carefully, while the errors of the approximations relative to the original terms are regarded as minor perturbations. Notably, a finite series with positive and negative exponents, called Exponent-Symmetry Series (ESS), is proposed for function approximation to decrease the highest exponent in the AAS so as to reduce computer round-off errors. To calculate the ESS coefficients, a method of seeking the Optimal Interpolation Points (OIP) is proposed using the least-squares-approximation theory. Due to the artful design of the approximations, the GADM can be decomposed into two analytically solvable subsystems by a perturbation method, and thus the AAS are obtained successfully. Finally, to help implement the AAS, two indirect methods for measuring the remaining mass and predicting the burnout time in flight are put forward using information from accelerometers. Simulation results verify the superiority of the AAS under the condition of time-varying MFR.

Adaptivity of a leaf-inspired wind energy harvester with respect to wind speed and direction

Environmental wind is a random phenomenon in both speed and direction, though it can be forecasted to some extent. An example of that is a gust which is an abrupt, but short-time change in wind speed and direction. Being a free and clean source for small-scale energy scavenging, attraction of wind is rapidly growing in the world of energy harvesters. In this paper, a leaf-like flapping wind energy harvester is introduced as the base structure in which a short-span airfoil is attached to the free end of a double-deck cantilever beam. A flap mechanism inspired by scales on sharks’ skin and a tail mechanism inspired by birds’ horizontal tail are proposed for integration to the base harvester to make it adaptive with respect to wind speed and direction, respectively. The use of the flap mechanism increases the leaf flapping frequency by +2.1 to +11.5 Hz at wind speeds of 1.5 to 6.0 m s−1. Therefore, since the output power of a vibrational harvester is a function of vibration frequency, a figure of merit or an efficiency parameter related to the output power will increase, as well. On the other hand, if there is a misalignment between the harvester’s heading and wind direction due to change of the latter one, the harvesting performance deteriorates. Although the base harvester can realign in certain ranges of sideslip angle at each wind speed, when the tail mechanism is integrated into that, it broadens the range of realignable sideslip angles at all the investigated wind speeds by up to 80 ∘ .

Path Tracking Control Based on T-S Fuzzy Model for Autonomous Vehicles with Yaw Angle and Heading Angle

Existing vehicle-road models used for road tracking do not take into account the side slip angle, which leads to a reduction in road tracking accuracy in scenarios where the vehicle is at a large side slip angle, such as an emergency lane change. Consequently, this study presents a path-tracking control technique based on the T-S fuzzy model of heading angle vehicle autonomy. In this paper, based on the yaw angle-based vehicle tracking model, a heading angle-based tracking model considering the side slip angle is constructed. Second, since the vehicle speed varies with time, this paper selects the membership function of the vehicle speed to establish the T-S fuzzy model of autonomous vehicle based on the yaw angle and heading angle, respectively, and ensures the robustness and stability over the whole parameter space by the linear parameter variation robust H∞ controller. Then, cost functions based on the yaw angle and heading angle augmented error systems are created separately to optimize the system’s overall performance. Ultimately, simulation and experimentation confirm that the algorithm for control, which is based on the fuzzy model of the heading angle vehicle, has superior autonomous trajectory performance.

Estimation of States and Cornering Stiffness of a Vehicle by LPV‐MHE

In this paper, we consider an estimation problem of sideslip angle and cornering stiffness of a vehicle from measurements of yaw rate, lateral acceleration and steering angle. Firstly, a vehicle dynamical model is expressed as a linear parameter‐varying system with cornering stiffness as uncertain parameters. Then, we show a method to estimate the parameters and the state based on moving horizon estimation. We provide a simple algorithm to solve the estimation problem. The effectiveness of the estimation method is evaluated through numerical simulations. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Miniaturized high signal-to-noise ratio optical air data system using a compact multi-axis parallel transceiver for airborne application

We have developed a miniaturized multi-channel parallel optical air data system with high signal-to-noise ratio for airborne application. In the system, we designed a fiber amplifier with multi-channel high-energy output that was respectively used as the transmitting signals and a compact multi-axis transceiver with an entrance pupil diameter of 70 mm that was used to receive multi-channel signals simultaneously. We demonstrated the performance of our system both on ground and on board. On ground, the measured line-of-sight speed had an average error of 0.02 m/s and a standard deviation of 0.15 m/s. On board, the standard deviation between the true air speed, angle of attack, and angle of sideslip measured by our system and a commercial Swiss air data system was 1 kt, 0.68°, and 0.54°, respectively, and those standard deviation between our system and a system with the same design but employing multiple single-axis telescopes with entrance pupil diameter of 30 mm was 0.34 kt, 0.36°, and 0.28°, respectively. The signal-to-noise ratio of our system was 4.5 times higher than that of the system with small single-axis telescopes. Our system is very promising for airborne applications because of its small volume, high signal-to-noise ratio, and high data rate.

Multi input multi output lateral dynamics control of an electric vehicle

Full electric vehicles with multiple and independently controlled powertrains allow for an improvement of vehicle handling capabilities both in steady state and in transient manoeuvres. This paper focuses on active lateral dynamics control of an electric vehicle equipped with 4 in-wheel motors and active rear steering. An integral terminal sliding mode controller (ITSMC) is derived starting from the linearized single track model with the addition of the rear wheel steering angle. The controller has a multi-input multi-output structure and is designed to track vehicle yaw rate and sideslip angle reference quantities through torque vectoring and active rear steering actuation. A novel approach for calculating reference sideslip angle and yaw rate using a logistic function is also presented in this paper. The ITSMC relies on real time knowledge of sideslip angle which cannot be measured in the real vehicle, thus it is estimated through the addition of an extended Kalman filter to the control loop. The performance of the controller is tested with VI-CarRealTime 14 degrees of freedom nonlinear model both for steady state and transient manoeuvres. Simulation results show a good tracking of the reference value with no chattering issues and with an improved behaviour if compared to a sliding mode controller from literature.

Data-driven constrained reinforcement learning algorithm for path tracking control of hovercraft

This paper presents a novel Constrained Backstepping Reinforcement Learning (CBRL) approach designed for path tracking of hovercraft. The approach addresses the challenges posed by complex modeling, multiple constraints, and large sideslip angles during high-speed maneuvers of underactuated hovercraft. Initially, a unique state constraint function is formulated, incorporating constraint boundaries related to navigation speed and path curvature. Additionally, transformations are applied to the yaw angular velocity and virtual yaw control law, ensuring that the yaw angular velocity remains within safety limits. Subsequently, a data-driven backstepping reinforcement learning optimal approach, coupled with a line-of-sight guidance law, is employed to guide the hovercraft along the intended path by regulating its yaw angle. Simultaneously, the backstepping reinforcement learning optimal control scheme is used to regulate the surge speed of the hovercraft above the resistance peak speed, thereby preventing excessive sideslip angles during path tracking. Neural network observers are integrated into the design process of both the yaw and surge controllers to monitor system dynamics in the presence of interference. Through simulation, the effectiveness and feasibility of the proposed CBRL algorithm are demonstrated.

Research on Stability Control of Distributed Drive Vehicle with Four-Wheel Steering

The four-wheel steering distributed drive vehicle is a novel type of vehicle with independent control over the four-wheel angle and wheel torque. A method for jointly controlling the distribution of the wheel angle and torque is proposed based on this characteristic. Firstly, the two-degrees-of-freedom model and ideal reference model of four-wheel steering vehicle are established; then, the four-wheel steering controller and torque distribution controller are designed. The rear wheel angle is controlled by the feedforward controller and the feedback controller. The feedforward controller takes the side slip angle of the center of mass as the control target, and the feedback controller takes the yaw angle as the control target. Torque is controlled by two control layers, the additional yaw moment of the upper layer is calculated by the vehicle motion state and fuzzy control theory, and the lower layer distributes wheel torque through the road adhesion coefficient and wheel load. Finally, a simulation platform is established to verify the effectiveness of the proposed control algorithm.

Research on lateral stability control strategy for distributed drive electric vehicles considering driving style

To improve the handling stability of distributed drive electric vehicle (DDEV), a lateral stability control strategy considering driving style is proposed. Firstly, a driving style recognition model based on support vector machines (SVM) was constructed to identify driving styles in different lateral motion scenarios. Then, a layered architecture is used to design a driver/Active Front Steering (AFS)/Direct Yaw Control (DYC) stability coordination control strategy. The upper layer is based on the phase plane of the sideslip angle and extension control theory, dividing the vehicle operating range into classical, extension, and non domains. In the middle level, three control strategies are designed for different work domains. In the classical domain, a driver/AFS coordinated control strategy based on non-cooperative Nash games is constructed, and the weight coefficients in the AFS system cost function are modified based on driving characteristics; Construct a coordinated control strategy for AFS/DYC systems based on cooperative Pareto games in the extension domain; In non-domain, construct a direct yaw torque control strategy based on twin-delayed deep deterministic policy gradient (TD3) reinforcement learning. The lower layer aims to minimize tire utilization and optimizes the allocation of additional yaw moment. Finally, by establishing Hardware in the Loop (HIL) experiment platform, the effectiveness of the proposed control strategy is verified in lane changing scenarios of different styles of drivers. Compared to DYC stability control based solely on TD3, the maximum yaw rate and standard deviation of the lateral stability control strategy considering driving style reduce by 2.4 % and 10.82 %, respectively, and the maximum and standard deviation of the sideslip angle reduce by 29.29 % and 39.24 %, respectively, thereby improving the vehicle's handling stability.

Research on layered control of path tracking for unmanned industrial vehicles based on fully hydraulic steering leakage compensation

Industrial vehicles work in complex terrain, the variable centre of mass position, leakage nonlinearity of full hydraulic steering and other issues lead to poor path tracking stability and accuracy. In this paper, an intelligent hierarchical controller for industrial vehicles’ path tracking is designed with full hydraulic steering leakage compensation, including an upper decision layer and a lower execution layer. The upper decision layer observes the pavement-tyre adhesion coefficients through an extended Kalman filter algorithm, and uses the real-time observations of the adhesion coefficients to establish a variable constraint control for the linear time-varying MPC, and thus performs the path tracking control. The lower actuator layer receives the upper layer’s target steering wheel angle output and performs steering operations. Based on the establishment of a mathematical model of full hydraulic steering considering the leakage characteristics, it analyses the leakage disturbance factors and constructs a fuzzy feed-forward steering leakage compensation controller to compensate for the leakage disturbances in real time for path tracking steering. Simulation and experimental results show that the lateral acceleration, sideslip angle, tyre side slip angle and tracking error of intelligent industrial vehicles under different loads and working conditions are improved by more than 32.4%, 35.8%, 40.2% and 45.8% respectively, and the designed hierarchical controller for the path tracking of intelligent industrial vehicles, which considers the compensation of all-hydraulic steering leakage, can effectively improve the path tracking stability and tracking accuracy of intelligent industrial vehicles under different loads and working conditions.

Bifurcation Analysis of a Non-Linear Vehicle Model Under Wet Surface Road Condition

The vehicles are prone to accidents during cornering on a wet or low friction coefficient roads if the longitudinal velocity (Vx) and steering angle (δ) are increased beyond a certain limit. Therefore, it is of major concern to analyze the behaviour and define the stability boundary of the vehicle for such scenarios. In this paper, stability analysis of a 2 degrees of freedom nonlinear bicycle model replicating a car model including lateral (sideslip angle β) and yaw (yaw rate r) dynamics only operating on a wet surface road has been performed. The stability is analysed by utilizing the phase plane method and bifurcation analysis. The obtained converging and diverging nature of the trajectories (β, r) depicts the stable and unstable equilibrium points in the phase plane. The movement of these points results in the transition of the stability known as bifurcation due to the change in the control parameters (Vx, δ). The Matcont/Matlab is utilized to obtain the bifurcation diagrams and the nature of bifurcations. The obtained results show that a saddle node (SNB) and a subcritical Hopf bifurcation (HB) exists for steering angle (±0.08 rad) and higher than (±0.08 rad) with Vx = (10-40) m/s respectively. The SNB and HB denotes the spinning of the vehicle and sliding of the vehicle respectively, thus generating a unstable behaviour. A stability boundary is obtained representing the stable and unstable range of parameters.

Path tracking with personalized handling stability constraints matched to driving styles

In the path-tracking control process, drivers have different expectations of the lateral response of vehicles. To meet drivers’ expectations of handling stability, this paper proposes a model predictive control (MPC)-based path-tracking controller with personalized handling stability constraints matched to driving style. Firstly, the categories of driving styles are obtained by applying the K-Means algorithm to the collected driving data. Then the sideslip angle-longitudinal velocity map is constructed based on the nonlinear dynamics model of the vehicle to generate the front-wheel steering angle constraints that are matched with the maximum sideslip of the vehicle expected by drivers of different styles. After that, an MPC-based path-tracking controller has been built, and the front-wheel steering angle is incorporated into the MPC controller. Furthermore, compared to the strategy of directly using sideslip angle constraints for handling stability control, this approach avoids introducing additional constraint variables, leading to improved real-time performance. Simulation experiments are conducted to compare the dynamic vehicle responses between the handling stability control that takes into account the driving style and the control system that does not consider the driving style. In addition, the subjective evaluation is conducted to verify the proposed path-tracking control method that considers the stability constraints of driving style. The evaluation results show that the proposed method can obtain better subjective evaluation results than a path-tracking controller that does not consider the driving style. Finally, through the hardware-in-the-loop experiments, the real-time computational capability of the proposed controller is verified.

Learning-based double layer control method of yaw stability simulation research for rear wheel independent drive electric tractor plowing operation

Complex field environments and variable soil conditions make tractor plowing operations susceptible to maneuvering instability, especially challenging in yaw stability. A learning-based double layer control (LDLC) method of yaw stability for rear wheel independent drive electric tractor (ET) plowing operation is proposed in this paper in order to solve this problem. Firstly, a sliding window self-learning traction resistance predictor is proposed based on Gaussian process regression (GPR) theory to predict the future traction resistance of plowing operation. Then, a discrete nonlinear model predictive control (NMPC) supervisor yaw stability controller is designed based on the dynamics model of the plowing unit and the tire-soil model to solve for the additional yaw torque required to maintain the yaw stability of the tractor. Finally, considering the additional yaw moment and driver demand torque, the subordinate controller composed of a particle swarm optimization algorithm optimally allocates the driving torque of the two driving wheels. Hardware-in-the-loop test results show that the maximum mean tracking errors of yaw rate and sideslip angle are 0.002 rad/s and 0.01 rad for the LDLC controlled ET during straight line and continuous steering under normal plowing operation, the maximum mean tracking errors of yaw rate and sideslip angle are 0.007 rad/s and 0.012 rad for straight line and continuous steering plowing operations under harsh plowing operation. The tracking accuracy is significantly higher than traditional cascade PID and equal distribution control methods. The proposed LDLC method effectively ensures the yaw stability of the ET during straight line and continuous steering under normal and harsh plowing operation conditions.

Adaptive Nonsingular Fast Terminal Sliding Mode-Based Direct Yaw Moment Control for DDEV under Emergency Conditions

Adaptive Nonsingular Fast Terminal Sliding Mode-Based Direct Yaw Moment Control for DDEV under Emergency Conditions

Sideslip Angle Estimation for Distributed Drive Electric Vehicles Based on Robust Unscented Particle Filter

An accurate and reliable sideslip angle is crucial for active safety control systems and advanced driver-assistance systems (ADAS). The direct measurement method of the sideslip angle suffers from challenges of high costs and environmental sensitivity, so sideslip angle estimation has always been a significant research issue. To improve the precision and robustness of sideslip angle estimation for distributed drive electric vehicles (DDEV) in extreme maneuvering scenarios, this paper presents a novel robust unscented particle filter (RUPF) algorithm based on low-cost onboard sensors. Firstly, a nonlinear dynamics model of DDEV is constructed, providing a theoretical foundation for the design of the RUPF algorithm. Then, the RUPF algorithm, which incorporates the unscented Kalman filter (UKF) to update importance density and utilizes systematic random resampling to mitigate particle degradation, is designed for estimation. Eventually, the availability of the proposed RUPF algorithm is validated on the co-simulation platform with non-Gaussian noises. Simulation results demonstrate that RUPF algorithm attains a higher precision and stronger robustness compared with the traditional PF and UKF algorithms.

Numerical simulation of postlaunching behaviors for a “balloon-borne UAV system”

PurposeThis paper aims to present numerical simulations for a series of flight processes for the postlaunching stage of the “balloon-borne UAV system.” It includes the balloon further ascent motion after airborne launching. In terms of unmanned aerial vehicles (UAVs), the tailspin state and the charge-out process with an anti-tailspin parachute-assisted suspending are analyzed. Then, the authors conduct trajectory optimization simulations for the long-distance gliding process.Design/methodology/approachThe balloon kinematics model and the parachute Kane multibody dynamic model are established. Using steady-state tailspin to reduced-order analysis and achieving change-out simulation by parachute suspension dynamic model. A reentry optimization control problem is developed and the Radau pseudo-spectral method is used to calculate the glide trajectory.FindingsThe established dynamic model and trajectory optimization method can effectively simulate the motion process of balloons and UAVs. The system mass reduction for launching UAVs will not cause damage to the balloon structure. The anti-tailspin parachute can reduce the UAV attack angles effectively. The UAV can glide to the designated target position by adjusting the attack angle and sideslip angle. The farthest flight distance after launching from 20 km height is 94 km and the gliding time is 40 min, which demonstrates the potential application advantage of high-altitude launching.Practical implicationsThe research content and related conclusions of this article achieve a closed-loop analysis of the flight mission chain for the “balloon-borne UAV system,” which provides simulation references for relevant balloon launching experiments.Originality/valueThis paper establishes a complete set of numerical simulation models and can effectively analyze various postlaunching behaviors.

Research on Yaw Stability Control of Front-Wheel Dual-Motor-Driven Driverless Formula Racing Car

In order to improve the yaw stability of a front-wheel dual-motor-driven driverless vehicle, a yaw stability control strategy is proposed for a front-wheel dual-motor-driven formula student driverless racing car. A hierarchical control structure is adopted to design the upper torque distributor based on the integral sliding mode theory, which establishes a linear two-degree-of-freedom model of the racing car to calculate the expected yaw angular velocity and the expected side slip angle and calculates the additional yaw moments of the two front wheels. The lower layer is the torque distributor, which optimally distributes the additional moments to the motors of the two front wheels based on torque optimization objectives and torque distribution rules. Two typical test conditions were selected to carry out simulation experiments. The results show that the driverless formula racing car can track the expected yaw angular velocity and the expected side slip angle better after adding the yaw stability controller designed in this paper, effectively improving driving stability.

Studying Modern Formula 1 Front Wing at Medium Cornering Speeds

A simulation-based study of three different types of front wing designs used in modern Formula 1 cars was done. The study mainly focuses on the aerodynamic forces that a Formula One car generates mainly the Downforce, the Drag force, & the Lateral force at low cornering speeds. These forces were studied in detail & taking a closer look at how they migrate during the dynamic conditions the car is thrown at various Side Slip (Yaw) Angles, these results were compared with the wing Scuderia Ferrari used in the 1998 formula 1 championship to better understand the inherent problems faced in those previous designs. A brief study of the flow field & flow lines was conducted along with the vortex generation for all three wings. Vortex formation and management is a prominent part of research being carried out for a formula 1 car, so a brief study on the phenomenon of vortex generation & Y250 vortex formation was also carried out. The studies were carried out over typical medium-speed corners where the speed ranges between 150-220 KM/Hr. A study on the effect of the flow field of the top element on the lower element was carried out where the 5th element was removed from each of the three wings & the effect on the downforce & drag value was analysed along with the pressure field.
 Keywords: Modern formula 1, Front wing designs, Cornering speeds, aerodynamic forces, Side Slip (Yaw) Angles, Centre of pressure (CoP), Lateral force, CFD, Downforce

Flexible calorimetric flow sensor with unprecedented sensitivity and directional resolution for multiple flight parameter detection

The accurate perception of multiple flight parameters, such as the angle of attack, angle of sideslip, and airflow velocity, is essential for the flight control of micro air vehicles, which conventionally rely on arrays of pressure or airflow velocity sensors. Here, we present the estimation of multiple flight parameters using a single flexible calorimetric flow sensor featuring a sophisticated structural design with a suspended array of highly sensitive vanadium oxide thermistors. The proposed sensor achieves an unprecedented velocity resolution of 0.11 mm·s−1 and angular resolution of 0.1°. By attaching the sensor to a wing model, the angles of attack and slip were estimated simultaneously. The triaxial flight velocities and wing vibrations can also be estimated by sensing the relative airflow velocity due to its high sensitivity and fast response. Overall, the proposed sensor has many promising applications in weak airflow sensing and flight control of micro air vehicles.

Practical System Identification and Incremental Control Design for a Subscale Fixed-Wing Aircraft

An incremental differential proportional integral (iDPI) control law using eigenstructure assignment gain design is tested in flight on a subscale platform to validate its suitability for fixed-wing flight control. A kinematic relation for the aerodynamic side-slip angle rate is developed to apply a pseudo full state feedback. In order to perform the gain design and assessment, a plant model is estimated using flight test data from gyro, accelerometer, airspeed and surface deflection measurements during sine-sweep excitations. Transfer function models for the actuators and surface deflections are identified both in-flight and on the ground for several different actuators and control surfaces using hall sensor surface deflection measurements. The analysis reveals a large variation in bandwidth between the different types of servo motors. Flight test results are presented which demonstrates that the plant model estimates based on tests with good frequency excitation, high bandwidth actuators and surface deflection measurements can be used to reasonably predict the closed-loop dynamic behavior of the aircraft. The closed-loop flight test results of the iDPi control law show good performance and lays the groundwork for further development.