Observer-Based Path Tracking Controller Design for Autonomous Ground Vehicles With Input Saturation

This paper investigates the problem of path tracking control for autonomous ground vehicles(AGVs),where the input saturation,system nonlinearities and uncertainties are considered.Firstly,the nonlinear path tracking system is formulated as a linear parameter varying(LPV)model where the variation of vehicle velocity is taken into account.Secondly,considering the noise effects on the measurement of lateral offset and heading angle,an observer-based control strategy is proposed,and by analyzing the frequency domain characteristics of the derivative of desired heading angle,a finite frequency H_∞index is proposed to attenuate the effects of the derivative of desired heading angle on path tracking error.Thirdly,sufficient conditions are derived to guarantee robust H_∞performance of the path tracking system,and the calculation of observer and controller gains is converted into solving a convex optimization problem.Finally,simulation examples verify the advantages of the control method proposed in this paper.

Adaptive Coordinated Path Tracking Control Strategy for Autonomous Vehicles with Direct Yaw Moment Control

It is a striking fact that the path tracking accuracy of autonomous vehicles based on active front wheel steering is poor under high-speed and large-curvature conditions.In this study,an adaptive path tracking control strategy that coordinates active front wheel steering and direct yaw moment is proposed based on model predictive control algorithm.The recursive least square method with a forgetting factor is used to identify the rear tire cornering stiffness and update the path tracking system prediction model.To adaptively adjust the priorities of path tracking accuracy and vehicle stability,an adaptive strategy based on fuzzy rules is applied to change the weight coefficients in the cost function.An adaptive control strategy for coordinating active front steering and direct yaw moment is proposed to improve the path tracking accuracy under high-speed and large-curvature conditions.To ensure vehicle stability,the sideslip angle,yaw rate and zero moment methods are used to construct optimization constraints based on the model predictive control frame.It is verified through simulation experiments that the proposed adaptive coordinated control strategy can improve the path tracking accuracy and ensure vehicle stability under high-speed and largecurvature conditions.

Particle path tracking method in two- and three-dimensional continuously rotating detonation engines

The particle path tracking method is proposed and used in two-dimensional（2D） and three-dimensional（3D） numerical simulations of continuously rotating detonation engines（CRDEs）. This method is used to analyze the combustion and expansion processes of the fresh particles, and the thermodynamic cycle process of CRDE. In a 3D CRDE flow field, as the radius of the annulus increases, the no-injection area proportion increases, the non-detonation proportion decreases, and the detonation height decreases. The flow field parameters on the 3D mid annulus are different from in the 2D flow field under the same chamber size. The non-detonation proportion in the 3D flow field is less than in the 2D flow field. In the 2D and 3D CRDE, the paths of the flow particles have only a small fluctuation in the circumferential direction. The numerical thermodynamic cycle processes are qualitatively consistent with the three ideal cycle models, and they are right in between the ideal F–J cycle and ideal ZND cycle. The net mechanical work and thermal efficiency are slightly smaller in the 2D simulation than in the 3D simulation. In the 3D CRDE, as the radius of the annulus increases, the net mechanical work is almost constant, and the thermal efficiency increases. The numerical thermal efficiencies are larger than F–J cycle, and much smaller than ZND cycle.

Path tracking for vehicle parallel parking based on ADRC controller

A novel path tracking controller for parallel parking based on active disturbance rejection control （ADRC） was presented in this paper. A second order ADRC controller was used to solve the path tracking robustness, which can estimate and compensate model uncertainty caused by steering kinematics and disturbances caused by parking speed and steering system delay. Collision-free path planning technology was adopted to generate the reference path. The simulation results validate that the performance of the proposed path tracking controller is better than the conventional PID controller. The actual vehicle tests show that the proposed path tracking controller is effective and robust to model uncertainty and disturbances.

Target Path Tracking Method of Intelligent Vehicle Based on Competitive Cooperative Game

To improve intelligent vehicle drive performance and avoid vehicle side-slip during target path tracking,a linearized four-wheel vehicle model is adopted as a predictive control model,and an intelligent ve-hicle target path tracking method based on a competitive cooperative game is proposed.The design variables are divided into different strategic spaces owned by each player by calculating the affecting factors of the design variables with objective functions and fuzzy clustering.Based on the competitive cooperative game model,each game player takes its payoff as a mono-objective to optimize its own strategic space and obtain the best strategy to deal with others.The best strategies were combined into the game strategy set.Considering the front wheel angle and side slip angle increment constraint,tire side-slip angle,and tire side slip deflection dynamics,it took the path tracking state model was used as the objective,function and the calculation was validated by competitive cooperative game theory.The results demonstrated the effectiveness of the proposed algorithm.The experimental results show that this method can track an intelligent vehicle quickly and steadily and has good real-time per-formance.

Cascade Optimization Control of Unmanned Vehicle Path Tracking Under Harsh Driving Conditions

Under ultra-high-speed and harsh conditions,conventional control methods struggle to ensure the path tracking accuracy and driving stability of unmanned vehicles during the turning process.Therefore,this study proposes a cascade control to solve this problem.Based on the new vehicle error model that considers vehicle tire sideslip and road curvature,the feedforward-parametric adaptive linear quadratic regulator(LQR)and proportional integral control-based speed-keeping controllers are used to compose the path-tracking cascade optimization controller for unmanned vehicles.To improve the adaptability of the unmanned vehicle path-tracking control under harsh driving conditions,the LQR controller parameters are automatically adjusted using a back-propagation neural network,in which the initial weights and thresholds are optimized using the improved grey wolf optimization algorithm according to the driving conditions.The speed-keeping controller reduces the impact on the curve-tracking accuracy under nonlinear vehicle speed variations.Finally,a joint model of MATLAB/Simulink and CarSim was established,and simulations show that the proposed control method can achieve stable entry and exit curves at ultra-high speeds for unmanned vehicles.Under strong wind and ice road conditions,the method exhibits a higher tracking accuracy and is more adaptive and robust to external interference in driving and variable curvature roads than methods such as the feedforward-LQR,preview and pure pursuit controls.

Path Tracking Control for Autonomous Truck with Dual Modular Chassis

This study focuses on enhancing the agility and path tracking capabilities of autonomous trucks equipped with dual 4WIS-4WID modular chassis.To address the challenges associated with these versatile vehicles,a comprehensive approach is presented.Firstly,a communication framework is devised,utilizing a hierarchical combination of two fieldbus systems.This framework facilitates adaptive marshalling,allowing effective communication and coordination among the various modular components of the autonomous truck.Secondly,a reference path generation strategy is proposed.This strategy relates the motion paths of the truck's body to its modular chassis.Reference paths for the modular chassis are derived based on the center of mass,effectively resolving the issue of differing motion paths.To tackle the path tracking problem for dual modular chassis,a cooperative path tracking controller is developed.This controller is designed using the kinematic model of the autonomous truck,enabling adaptive control through online adjustments of controller parameters based on measured input–output data.Simulation and real vehicle testing validate the proposed path tracking controller.In the dual modular chassis path tracking simulation,the maximum lateral position error and the maximum yaw angle error of truck body at different speeds are 0.082 m and 0.007 rad,respectively.In the real vehicle test,the maximum lateral position error is 0.194 m,and the maximum yaw angle error is 0.071 rad.These results demonstrate the practicality and effectiveness of the controller in real-world applications.

Wireless positioning and path tracking for a mobile platform in greenhouse

In order to realize intelligent greenhouse,an automatic navigation method for a mobile platform based on ultra-wideband(UWB)positioning technology was proposed and validated in this study.The time difference of arrival(TDOA)approach was used to monitor and track the UWB positioning to obtain the localization information of the mobile platform working in a greenhouse.After applying polynomial fitting for positioning error correction,the system accuracy was within 5 mm.A fuzzy controller model was constructed by incorporating the lateral and heading deviations as input variables and the steering angle of front wheel as the output variable.A fuzzy rule was established based on domain knowledge,as well as the steering angle of front wheel offline query table,which was applied to alleviate the calculative load of the controller.Experimental results confirmed that the automatic navigation method proposed in this study performed satisfactorily,with a steady-state error ranging from 41 mm to 79 mm when tracking straight line,and an average error of 185 mm and an average maximum error of 532 mm when tracking polygon.In addition,the maximum error occurred at the polygonal corner which could meet the needs of driving on the narrow road in the greenhouse.The method proposed in this study provides a new systematic approach for the research of greenhouse automatic navigation.

Implementation of MPC-Based Path Tracking for Autonomous Vehicles Considering Three Vehicle Dynamics Models with Different Fidelities

Model predictive control(MPC)algorithm is established based on a mathematical model of a plant to forecast the system behavior and optimize the current control move,thus producing the best future performance.Hence,models are core to every form of MPC.An MPC-based controller for path tracking is implemented using a lower-fidelity vehicle model to control a higher-fidelity vehicle model.The vehicle models include a bicycle model,an 8-DOF model,and a 14-DOF model,and the reference paths include a straight line and a circle.In the MPC-based controller,the model is linearized and discretized for state prediction;the tracking is conducted to obtain the heading angle and the lateral position of the vehicle center of mass in inertial coordinates.The output responses are discussed and compared between the developed vehicle dynamics models and the CarSim model with three different steering input signals.The simulation results exhibit good path-tracking performance of the proposed MPC-based controller for different complexity vehicle models,and the controller with high-fidelity model performs better than that with low-fidelity model during trajectory tracking.

Tractor path tracking control based on binocular vision

In the process of field operation management,determining how to accurately realize crop row identification and path tracking control is an essential part of tractor automatic navigation.According to the linear operation in the process of cotton field management,the tractor path tracking control system was designed based on binocular vision and the pure pursuit model.A new crop row detection method based on the Census transform and the PID control algorithm with dead zone were used.First,the upper computer software was developed by C++with the functions of parameter setting and image acquisition and processing.Second,an automatic steering controller was developed based on microprocessor MC9S12XS128 of Freescale.The control program was developed based on modular design using CodeWarrior during development of the PID-based automatic steering control strategy.Finally,a field experiment platform of tractor path tracking control was built,and field experiments under the actual cotton were conducted.The optimal visibility distance was determined by several previous experiments.When the tractor tracks the path with the optimal visibility distance in the growth environment of actual cotton crops,the mean absolute deviation of course angle was 0.95°,and the standard deviation was 1.26°;the mean absolute deviation of lateral position was 4.00 cm,and the standard deviation was 4.97 cm;the mean absolute deviation of front wheel angle was 2.99°,and the standard deviation was 3.67°.The experimental results show that(1)the crop row detection method based on Census transform can identify the crop line and plan the navigation path well,and(2)the tractor path tracking control system based on binocular vision has good stability and high control precision;thus,the control systemcan realize the automatic path tracking control of cotton line operation and meets the agricultural requirements of cotton field operation management.

Robust H_(∞)path tracking control of autonomous vehicles with delay and actuator saturation

The path tracking control problem is investigated in this paper for autonomous vehicles(AVs)with time-varying input delay and actuator saturation.Based on the Lyapunov-Krasovskii function and the characteristic of the saturation nonlinearity,a robust H_(∞)state-feedback path tracking controller is presented,and the corresponding control gain can be obtained by solving the linear matrix inequalities(LMIs).The asymptotic stability and prescribed H_(∞)performance conditions are studied for the closed-loop control system.To reduce the cost of control system,a static robust H_(∞)output-feedback controller is also proposed.In addition,the uncertainty effects of the cornering stiffness and external disturbances are included to improve the robustness of the control scheme.Simulation results are given to verify the effectiveness of the proposed theoretical results.

Implementation of MPC-Based Trajectory Tracking Considering Different Fidelity Vehicle Models

In order to investigate how model fidelity in the formulation of model predictive control(MPC)algorithm affects the path tracking performance,a bicycle model and an 8 degrees of freedom(DOF)vehicle model,as well as a 14-DOF vehicle model were employed to implement the MPC-based path tracking controller considering the constraints of input limit and output admissibility by using a lower fidelity vehicle model to control a higher fidelity vehicle model.In the MPC controller,the nonlinear vehicle model was linearized and discretized for state prediction and vehicle heading angle,lateral position and longitudinal position were chosen as objectives in the cost function.The wheel step steering and sine wave steering responses between the developed vehicle models and the Carsim model were compared for validation before implementing the model predictive path tracking control.The simulation results of trajectory tracking considering an 8-shaped curved reference path were presented and compared when the prediction model and the plant were changed.The results show that the trajectory tracking errors are small and the tracking performances of the proposed controller considering different complexity vehicle models are good in the curved road environment.Additionally,the MPC-based controller formulated with a high-fidelity model performs better than that with a low-fidelity model in the trajectory tracking.

Rollover Prevention and Motion Planning for an Intelligent Heavy Truck

It is very necessary for an intelligent heavy truck to have the ability to prevent rollover independently.However,it was rarely considered in intelligent vehicle motion planning.To improve rollover stability,a motion planning strategy with autonomous anti rollover ability for an intelligent heavy truck is put forward in this paper.Considering the influence of unsprung mass in the front axle and the rear axle and the body roll stiffness on vehicle rollover stability,a rollover dynamics model is built for the intelligent heavy truck.From the model,a novel rollover index is derived to evaluate vehicle rollover risk accurately,and a model predictive control algorithm is applicated to design the motion planning strategy for the intelligent heavy truck,which integrates the vehicle rollover stability,the artificial potential field for the obstacle avoidance,the path tracking and vehicle dynamics constrains.Then,the optimal path is obtained to meet the requirements that the intelligent heavy truck can avoid obstacles and drive stably without rollover.In addition,three typical scenarios are designed to numerically simulate the dynamic performance of the intelligent heavy truck.The results show that the proposed motion planning strategy can avoid collisions and improve vehicle rollover stability effectively even under the worst driving scenarios.

Gravity-driven powder flow and the influence of external vibration on flow characteristics

The controlled and homogeneous flow of dry granular powders through hoppers is essential for applications,namely,packaging of food grains,fertilizers and additive manufacturing processes such as directed energy deposition for better product quality.One of the major issues encountered in the granular flows through hoppers is flow stagnation due to the well-known arching phenomenon.Vibration-assisted granular flow through hoppers is one of the mechanisms used for better mass flow control.In this work,the influence of external mechanical vibration on the powder flow is investigated experimentally and using discrete element simulations.First,the mass flow rate through the hopper increases with an increase in vibration amplitude and then decreases,signifying the existence of an optimal amplitude of vibration.The DEM simulations explained the underlying mechanisms for the existence of an optimal amplitude of vibration corresponding to the maximum mass flow rate.A range of vibration amplitudes from 0 mm to 3.5 mm is used to study the flow behaviour;the maximum flow of around 33 g/s to 35 g/s is observed for 0.75 mm to 1.25 mm vibration amplitude for the hopper-particle combination studied in this work.The work also reports the influence of vibration frequency,hopper,and particle dimension on the flow characteristics.The research facilitates the effective use of mechanical vibration to enhance powder flow that can further be extended to non-spherical and multi-material particles.

A review on carrier aircraft dispatch path planning and control on deck

As an important part in sortie/recovery process,the dispatch of carrier aircraft not only affects the sortie/recovery efficiency and safety,but also has severe influence on the carrier's combat efficiency and the comprehensive support capability.Path planning is the key to improve the efficiency and safety during the dispatch process.The main purpose of this paper is to propose a comprehensive investigation of techniques and research progress for the carrier aircraft's dispatch path planning on the deck.Three different dispatch modes of carrier aircraft and the corresponding modeling technologies are investigated,and the aircraft's dispatch path planning techniques and algorithms have been classified into different classes.Moreover,their assumptions and drawbacks have been discussed for single aircraft and multiple aircraft.To make the research work more comprehensive,the corresponding tracking control methodologies are also discussed.Finally,due to the similarity of path planning problem between the carrier aircraft's dispatch and those in other fields,this paper provides an exploratory prospect of the knowledge or method learned from other fields.

Trajectory tracking control of agricultural vehicles based on disturbance test

To improve the trajectory tracking robust stability of agricultural vehicles,a path tracking control method combined with the characteristics of agricultural vehicles and nonlinear model predictive control was presented.Through the proposed method,the path tracking problem can be divided into two problems with speed and steering angle constraints:the trajectory planning problem,and the trajectory tracking optimization problem.Firstly,the nonlinear kinematics model of the agricultural vehicle was discretized,then the derived model was inferred and regarded as the prediction function plant for the designed controller.Second,the objective function characterizing the tracking performance was put forward based on system variables and control inputs.Therefore,the objective function optimization problem,based on the proposed prediction equation plant,can be regarded as the nonlinear constrained optimization problem.What’s more,to enhance the robust stability of the system,a real-time feedback and rolling adjustment strategy was adopted to achieve optimal control.To validate the theoretical analysis before,the Matlab simulation was performed to investigate the path tracking performance.The simulation results show that the controller can realize effective trajectory tracking and possesses good robust stability.Meanwhile,the corresponding experiments were conducted.When the test vehicle tracked the reference track with a speed of 3 m/s,the maximum lateral deviation was 13.36 cm,and the maximum longitudinal deviation was 34.61 cm.When the added horizontal deviation disturbance Yr was less than 1.5 m,the controller could adjust the vehicle quickly to make the test car return to the reference track and continue to drive.Finally,to better highlight the controller proposed in this paper,a comparison experiment with a linear model predictive controller was performed.Compared to the conventional linear model predictive controller,the horizontal off-track distance reduced by 36.8%and the longitudinal deviation reduced by 32.98%when performing circular path tracking at a speed of 3 m/s.

Equidistance target-following controller for underactuated autonomous underwater vehicles

Purpose–Autonomous Underwater Vehicles(AUVs)play a crucial role in marine biology research and oceanic natural resources exploration.Since most AUVs are underactuated they require sophisticated trajectory planning and tracking algorithms.The purpose of this paper is to develop a new method that allows an underactuated AUV to track a moving object while constraining the approach to a direction tangent to the path of the target.Furthermore,the distance at which the AUV follows the target is constrained,reducing the probability of detection and unwanted behavior change of the target.Design/methodology/approach–First,a kinematic controller that generates a trajectory tangent to the path of the moving target is designed such that the AUV maintains a prescribed distance and approaches the target from behind.Using a Lyapunov based method the stability of the kinematic controller is proven.Second,a dynamic sliding mode controller is employed to drive the vehicle on the trajectory computed in the first step.Findings–The kinematic and dynamic controllers are shown to be stable and robust against parameter uncertainty in the dynamic model of the vehicle.Results of numerical simulations for equidistant tracking of a target on both smooth and discontinuous derivatives trajectories for a variety of relative initial positions and orientations are shown.Originality/value–The contribution of this research is development of a new method for path planning and tracking of moving targets for underactuated AUVs in the horizontal plane.The method allows control of both the direction of approach and the distance from a moving object.