Yaw Dynamics Research Articles

Tractor-Semitrailer Vehicle Stability Control Based on TruckSim-Simulink Co-Simulation

AbstractA nonlinear tractor-semitrailer vehicle simulation model is established based on TruckSim used to validate the proposed control scheme. The yaw dynamics of the entire vehicle is stabilized by applying differential braking to the wheels of the tractor and the trailer. The control system, which was designed in Matlab/Simulink, is a two-level structure with the upper yaw moment controller outputting an additional yaw moment and the lower control allocator realizing the additional yaw moment by appropriately coordinating the wheel brake actions of the tractor and the trailer. A Trucksim-Simulink co-simulation model is constructed, and based on which the proposed control scheme is validated by a single-lane change maneuver on a slippery road. Simulation results reveal that designing and evaluating vehicle stability control strategies with the TruckSim-Simulink co-simulation method is effective. The proposed control scheme can significantly improve the vehicle yaw stability and roll stability.

Non-linear control algorithms for an unmanned surface vehicle

Although intrinsically marine craft are known to exhibit non-linear dynamic characteristics, modern marine autopilot system designs continue to be developed based on both linear and non-linear control approaches. This article evaluates two novel non-linear autopilot designs based on non-linear local control network and non-linear model predictive control approaches to establish their effectiveness in terms of control activity expenditure, power consumption and mission duration length under similar operating conditions. From practical point of view, autopilot with less energy consumption would in reality provide the battery-powered vehicle with longer mission duration. The autopilot systems are used to control the non-linear yaw dynamics of an unmanned surface vehicle named Springer. The yaw dynamics of the vehicle being modelled using a multi-layer perceptron-type neural network. Simulation results showed that the autopilot based on local control network method performed better for Springer. Furthermore, on the whole, the local control network methodology can be regarded as a plausible paradigm for marine control system design.

Adaptive optimal control allocation using Lagrangian neural networks for stability control of a 4WS–4WD electric vehicle

This study involves a layered vehicle dynamics control system, which is composed of an adaptive optimal control allocation method using Lagrangian neural networks for optimal distribution of tyre forces and the sliding mode yaw moment observer for robust control of yaw dynamics. The proposed optimal control allocation method eliminates the requirement of solving optimization problem in every time step and it is a convergent and stability guaranteed solution for the optimal tyre force distribution problem. The aim in the sliding mode yaw moment observer is to force the vehicle to track a reference vehicle dynamic behaviour by estimating the equivalent input extended disturbance, which is the required stabilizing virtual yaw moment. The proposed layered stability control scheme has been tested on a four-wheel drive–four-wheel steer electric Fiat Doblo Van, which is modelled in CarSim. Both the sliding mode disturbance observer and the optimal control allocation methods are the first known applications to the stability control problem of road vehicles.

Slip Angle Estimation: Development and Experimental Evaluation

This paper develops a slip angle estimation method using measurements of lateral acceleration and yaw rate. A unique model for slip angle estimation is developed by combining velocity kinematics, yaw dynamics, and lateral translational dynamics, so as to avoid sensor drift and parameter variation effect. The model is used in designing a state estimator to estimate slip angle, which compensates slip angle prediction error using the measurements of lateral acceleration and yaw rate. Experimental performance verification of the proposed slip angle estimation scheme is presented.

Full drive-by-wire dynamic control for four-wheel-steer all-wheel-drive vehicles

Most of the controllers introduced for four-wheel-steer (4WS) vehicles are derived with the assumption that the longitudinal speed of the vehicle is constant. However, in real applications, the longitudinal speed varies, and the longitudinal, lateral, and yaw dynamics are coupled. In this paper, the longitudinal dynamics of the vehicle as well as its lateral and yaw motions are controlled simultaneously. This way, the effect of driving/braking forces of the tires on the lateral and yaw motions of the vehicle are automatically included in the control laws. To address the dynamic parameter uncertainty of the vehicle, a chatter-free variable structure controller is introduced. Elimination of chatter is achieved by introducing a dynamically adaptive boundary layer thickness. It is shown via simulations that the proposed control approach performs more robustly than the controllers developed based on dynamic models, in which longitudinal speed is assumed to be constant, and only lateral speed and yaw rate are used as system states. Furthermore, this approach supports all-wheel-drive vehicles. Front-wheel-drive or rear-wheel-drive vehicles are also supported as special cases of an all-wheel-drive vehicle.

Fault tolerant autonomous rendezvous and docking architecture for spacecraft in presence of control actuator failures

PurposeThe purpose of this paper is to present novel robust fault tolerant control design architecture to detect and isolate spacecraft attitude control actuators and reconfigure to redundant backups to improve the practicality of actuator fault detection.Design/methodology/approachThe Robust Fault Tolerant Control is designed for spacecraft Autonomous Rendezvous and Docking (AR&D) using Lyapunov direct approach applied to non‐linear model. An extended Kalman observer is used to accurately estimate the state of the attitude control actuators. Actuators on all three axes (roll/pitch/yaw) sequentially fail one after another and the robust fault tolerant controller acts to reconfigure to redundant backups to stabilize the spacecrafts and complete the required maneuver.FindingsIn the simulations, the roll, pitch and yaw dynamics of the spacecraft are considered and the attitude control actuators failures are detected and isolated. Furthermore, by switching to redundant backups, the guarantee of overall stability performance is demonstrated.Research limitations/implicationsA real time actuator failure detection and reconfiguration process using robust fault tolerant control is applied for spacecraft AR&D maneuvers. Finding an appropriate Lyapunov function for the non‐linear dynamics is not easy and always challenging. Failure of actuators on all three axes at the same time is not considered. It is a very useful approach to solve self‐assembly problems in space, spacecraft proximity maneuvers as well as co‐operative control of planetary vehicles in presence of actuator failures.Originality/valueAn approach has been proposed to detect, isolate and reconfigure spacecraft actuator failures occurred in the spacecraft attitude control system. A Robust Fault Tolerant Control scheme has been developed for the nonlinear AR&D maneuver for two spacecrafts. Failures that affect the control performance characteristics are considered and overall performance is guaranteed even in presence of control actuator failures. The architecture is demonstrated through model‐based simulation.

Development of Integrated Control of Electronic Stability Control, Continuous Damping Control and Active Anti-Roll Bar for Vehicle Yaw Stability

This paper describes an investigation into coordinated control of electronic stability control (ESC), continuous damping control (CDC) and active roll control system (ARC). The coordinated control is suggested to improve the vehicle stability and agility features by yaw rate control. At first, the relation of roll moment distribution and yaw dynamics is analyzed based on simplified tire model. The proposed integrated chassis control algorithm consists of a supervisor, control algorithms, and a coordinator. The supervisor monitors the vehicle status and determines desired vehicle motions such as a desired yaw rate and desired roll motion based on control modes to improve vehicle stability. According to the corresponding the desired vehicle dynamics, the control algorithm calculated a desired yaw moment and desired roll moment, respectively. Based on the desired yaw moment and the desired roll moment, the coordinator determines the brake pressures, the ARC motor torques and damper current based on control strategies. The ARC motor torque has been calculated to generate the roll moment and yaw moment simultaneously. Closed loop simulations with a driver-vehicle-controller system were conducted to investigate the performance of the proposed control strategy using CarSim vehicle dynamics software and the integrated controller coded using Matlab/Simulink.

Nonlinear yaw dynamics analysis and control for the tractor-semitrailer vehicle

This study mainly focuses on the lateral dynamics, stability and the control problems of a typical tractor-semitrailer vehicle in critical situations. First, the lateral dynamics and stability are analysed by nonlinear dynamics theories involving equilibrium, stability and bifurcation, based on a nonlinear vehicle model whose physical parameters can vary with the actual load conditions during transportation. The lateral instability forms of jackknife, trailer swing and trailer oscillation reported in literature are analysed and discussed based on nonlinear dynamics analysis and then suggestions for the stability controller design are also proposed. As another main work of this study, the stability control of the tractor semi-trailer vehicle based on Sliding Model Control (SMC) method is discussed and especially the design of the sliding surface to improve the control performance and robustness against the variation of load conditions is analysed in detail. The effectiveness of the proposed control scheme is evaluated by numerical simulations based on a full nonlinear vehicle model constructed in Appendix 1. Some important and valuable suggestions have been proposed for the stability control of the tractor-semitrailer vehicle on the basis of this work.

Study on the Time Lag between Steering Input and Vehicle Lateral Acceleration Response under Different Key Vehicle Parameters

A passively suspended road vehicle rolls outwards under the influence of lateral acceleration when cornering, which is very dangerous under large lateral acceleration. In this paper, time lag between steering input and vehicle lateral acceleration response is systematically studied to implement the active roll control algorithm from the viewpoint of vehicle system dynamics. A 3 DOF yaw-roll vehicle model is established based on vehicle lateral, roll and yaw dynamics. Vehicle parameters of a 1997 Jeep Cherokee is used for parametric study, where the influences of vehicle velocity, steering frequency, mass, length, roll, yaw moment of inertia, position of vehicle centre of gravity, and tyre cornering stiffness are studied via numerical simulation. The analysis results will help improve the real time rollover warning/control algorithm design for vehicle safety.

Modelling the yaw dynamics of an uninhabited surface vehicle for navigation and control systems design

Marine and Industrial Dynamic Analysis Research Group, Advanced Engineering Systems and Interactions, School of Marine Science and Engineering, Plymouth University, UK In many navigation and control systems a key element in both their software architectures is a model which describes the dynamic characteristics of the given vessel. Thus, to ensure uninhabited surface vehicles are capable of successfully completing missions, it is essential that the marine control systems designer has available bona fide dynamic models that are accurate and possess good prediction capabilities. Since hydrodynamic modelling is usually very expensive, time-consuming and requires the use of specialist equipment in the form of a tank testing facility, it was considered more appropriate to model the vehicle yaw dynamics using Black-box identification techniques. In this paper five new models of the yaw dynamics of the Springer uninhabited surface vehicle are developed and presented. Two of the models that are based on auto-regressive with exogenous variable and autoregressive moving average with exogenous variable techniques are linear in composition and expressed in their state space formats; while the others are nonlinear and described by multi-layer perception, radial basis function and recursive neural networks. In all cases, the models were optimised using a steady-state genetic algorithm. The models are compared using a Mean Square Error criterion and residual correlation analysis. From the evaluation undertaken, it is shown that the yaw dynamic model based on the recursive neural network outperformed the others in terms of predictability and exactness. Therefore, it is concluded the recursive neural network model is the best candidate for navigation and control systems design studies involving the Springer vehicle.

Steering control strategy guide by two preview vision cues

Vision cues play an important role in states feedback in motion control. However, the existing driver steering models consider little about vision cues utilized by human drivers during their steering procedure. This paper presents a novel steering control strategy based on two preview points (far point and near point). The far point is used to compensate the steering wheel by predicting the upcoming curvature change with respect to the lane, while the near point as vision feedback, which is used to tune the steering wheel by estimating the errors of vehicle states and lane center. To obtain much smoother lateral acceleration during steering, a forward internal model is established using a second-order yaw dynamics system that captures the influence of yaw angular acceleration caused by steering wheel angle. The input parameter of the second-order system is the vision cues of both the near and far points, and the output parameters are the ideal yaw angle and yaw rate. To calculate suitable the steering wheel angle, an adaptive controller is designed using fuzzy sliding technology, which is used as the input of the vehicle system dynamics. Numerical simulation results show that the proposed method performs better than the existing driver steering models in case of imitating human drivers’ behavior, and exhibits excellent adaption to the lane curvature change.

An Autopilot Based on a Local Control Network Design for an Unmanned Surface Vehicle

Over recent years, a number of marine autopilots designed using linear techniques have underperformed owing to their inability to cope with nonlinear vessel dynamics. To this end, a new design framework for the development of nonlinear autopilots is proposed. Local Control Networks (LCNs) can be used in the design of nonlinear control systems. In this paper, a LCN approach is taken in the design of a nonlinear autopilot for controlling the nonlinear yaw dynamics of an unmanned surface vehicle known as Springer. It is considered the approach is the first of its kind to be used in marine control systems design. Simulation results are presented and the performance of the nonlinear autopilot is compared with that of an existing Springer Linear Quadratic Gaussian (LQG) autopilot using standard system performance criteria. From the results it can be concluded the LCN autopilot out-performed that based on LQG techniques in terms of the selected criteria. Also it provided more energy saving control strategies and would thereby increase operational duration times for the vehicle during real-time missions.

A Neural Network Method for Miniature Unmanned Helicopter Heading Control

A neural network control method for heading control of miniature unmanned helicopter is proposed. For the complexity of miniature helicopter aerodynamics, it is difficult to identify the unknown parameters of yaw dynamics model. To design heading controller of miniature helicopter without modelling yaw dynamics, BP neural network is designed as heading controller, which is trained by collected flight data. By training, the neural network controller can learn the artificial operation strategy and realize the heading control of miniature unmanned helicopter. Simulation results demonstrate the validity of the proposed neural network control method.

Nonlinear robust control of integrated vehicle dynamics

A new methodology to design the vehicle GCC (global chassis control) nonlinear controller is developed in this paper. Firstly, to handle the nonlinear coupling between sprung and unsprung masses, the vehicle is treated as a mechanical system of two-rigid-bodies which has 6 DOF (degree of freedom), including longitudinal, lateral, yaw, vertical, roll and pitch dynamics. The system equation is built in the yaw frame based on Lagrange's method, and it has been proved that the derived system remains the important physical properties of the general mechanical system. Then the GCC design problem is formulated as the trajectory tracking problem for a cascade system, with a Lagrange's system interconnecting with a linear system. The nonlinear robust control design problem of this cascade interconnected system is divided into two H ∞ control problems with respect to the two sub-systems. The parameter uncertainties in the system are tackled by adaptive theory, while the external uncertainties and disturbances are dealt with the H ∞ control theory. And the passivity of the mechanical system is applied to construct the solution of nonlinear H ∞ control problem. Finally, the effectiveness of the proposed controller is validated by simulation results even during the emergency manoeuvre.

Neuro-Fuzzy Control with a Novel Training Method Based-on Sliding Mode Control Theory: Application to Tractor Dynamics

Accurate automatic guidance of agricultural vehicles is essential for gaining the ultimate benefit in agriculture. As an agricultural vehicle, tractors have more than one subsystem interacting each other, e.g. yaw dynamics, longitudinal dynamics, implement dynamics, etc. Instead of modeling the subsystem interaction prior to model-based control, we have developed a control algorithm which learns the interactions by using the measured feedback error. In this study, two PD controllers and two fuzzy neural networks are combined for controlling the yaw and traction dynamics. While the former ensures the stability of the related subsystem, the latter learns the system dynamics and becomes the leading controller. The interactions between both subsystems are not taken into account explicitly, but are considered to be disturbances in the control of each individual subsystem. A novel sliding mode control theory-based learning algorithm is used to train the fuzzy neural networks, and the convergence of the parameters is shown using a Lyapunov function.

A Local Control Network Autopilot for an Unmanned Surface Vehicle

Although marine craft are renowned for their nonlinear dynamic behaviour, autopilot system designs are still being developed based on linear control methodologies. Thus to overcome any limitations in the performance capabilities of such systems, it is appropriate to consider designing nonlinear autopilots. This paper reports an investigation into a novel approach to marine control systems design based on local control network (LCN) techniques. The approach is used to design an autopilot for controlling the nonlinear yaw dynamics of an unmanned surface vehicle and is benchmarked against another based on a standard proportional-integral-derivative controller. Simulation results are presented and the performances of the autopilots are compared using standard system performance criteria. From the results it can be observed that the LCN is more efficient compared to the PID controller. Also it out performs its PID counterpart in terms of the selected criteria. Copyright © 2012 IFAC

Optimal Reconfiguration Control of the Yaw Stability of the Tractor‐Semitrailer Vehicle

An optimal reconfiguration control scheme based on control allocation (CA) is proposed to stabilize the yaw dynamics of the tractor‐semitrailer vehicle. The proposed control scheme is a two‐level structure consisting of an upper level of sliding mode yaw moment controller (SMYC) and a lower optimal brake force distributor (BFD). The upper SMYC is designed to follow the tractor yaw rate and the combination of the hitch angle and trailer slip angle and outputs the corrective yaw moment, respectively, for the tractor and the trailer. The optimal brake force allocation and reconfigurable control problem is transformed to a problem of error minimization and control minimization combination formulated by constrained weighted least squares (CWLS) optimization and further solved with active set (AS) algorithm. Simulation results reveal that the CA technique‐based optimal reconfigurable control is rather effective for the tractor‐semitrailer vehicle to enhance the yaw stability performance and the reliability in case of actuator failure thanks to the multiple‐axle structure enriching the alternatives of possible actuator combinations in CA optimization.

Minimal modelling approach to describe turbulent rocket roll dynamics in a vertical wind tunnel

The roll dynamics of a 5-kg, 1.3 -m high sounding rocket are analysed in a vertical wind tunnel. Significant turbulence in the tunnel makes the system identification of the effective inertia, damping, and asymmetry with respect to roll challenging. A novel method is developed, which decouples the disturbance from the rocket frame's intrinsic roll dynamics and allows accurate prediction of roll rate and angle. The parameter identification method is integral-based, and treats wind disturbances as equivalent to a movement in the actuator fins. The method is robust, requires minimal computation, and gave realistic disturbance distributions reflecting the randomness of the turbulent wind flow. Two models, one with constant damping and one with fin angle-dependant damping were considered. The mean absolute roll rate of the rocket frame observed in experiments was 16.4°/s and both models predicted the roll rate with a mean absolute error less than 0.10°/s with a standard deviation less than 0.08°/s. The roll angle (measured by an encoder), was tracked by the model with a mean absolute error less than 0.20° and a standard deviation less than 0.15°. These results prove the concept of this minimal modelling approach which will be extended to varying wind speed, and pitch, and yaw dynamics in the future.

The influence of sensory delay on the yaw dynamics of a flapping insect

In closed-loop systems, sensor feedback delays may have disastrous implications for performance and stability. Flies have evolved multiple specializations to reduce this latency, but the fastest feedback during flight involves a delay that is still significant on the timescale of body dynamics. We explored the effect of sensor delay on flight stability and performance for yaw turns using a dynamically scaled robotic model of the fruitfly, Drosophila. The robot was equipped with a real-time feedback system that performed active turns in response to measured torque about the functional yaw axis. We performed system response experiments for a proportional controller in yaw velocity for a range of feedback delays, similar in dimensionless timescale to those experienced by a fly. The results show a fundamental trade-off between sensor delay and permissible feedback gain, and suggest that fast mechanosensory feedback in flies, and most probably in other insects, provide a source of active damping which compliments that contributed by passive effects. Presented in the context of these findings, a control architecture whereby a haltere-mediated inner-loop proportional controller provides damping for slower visually mediated feedback is consistent with tethered-flight measurements, free-flight observations and engineering design principles.

Modeling the yaw dynamics of an unmanned helicopter through modes partition method

The main difficulties in modeling yaw dynamics of a helicopter arise from the high nonlinearities, cross-couplings and dynamic uncertainties of these aerocraft. This paper proposes a new identification approach for yaw dynamics modeling through modes partition method (MPM) with a concentrated search space limited by implicit human factors. Working from first principles and basic aerodynamics, the nonlinear equations of motion for yaw dynamics is derived. The model is linearized and transformed into a combination of dynamic modes, whose coefficients are identified from real-flight data through distributed genetic algorithm (DGA). The effectiveness of the approach is validated in terms of the identified model which can accurately capture the dynamic characters of the helicopter. Time- and frequency-domain results clearly demonstrate the potential of MPM in modeling such complex systems.