Control Method For Vehicle Research Articles

A Survey on Fault Diagnosis and Fault-Tolerant Control Methods for Unmanned Aerial Vehicles

The continuous evolution of modern technology has led to the creation of increasingly complex and advanced systems. This has been also reflected in the technology of Unmanned Aerial Vehicles (UAVs), where the growing demand for more reliable performance necessitates the development of sophisticated techniques that provide fault diagnosis and fault tolerance in a timely and accurate manner. Typically, a UAV consists of three types of subsystems: actuators, main structure and sensors. Therefore, a fault-monitoring system must be specifically designed to supervise and debug each of these subsystems, so that any faults can be addressed before they lead to disastrous consequences. In this survey article, we provide a detailed overview of recent advances and studies regarding fault diagnosis, Fault-Tolerant Control (FTC) and anomaly detection for UAVs. Concerning fault diagnosis, our interest is mainly focused on sensors and actuators, as these subsystems are mostly prone to faults, while their healthy operation usually ensures the smooth and reliable performance of the aerial vehicle.

Extended-state-observer-based distributed model predictive formation control of under-actuated unmanned surface vehicles with collision avoidance

In this paper, distributed formation tracking control with collision avoidance is addressed for a group of under-actuated unmanned surface vehicles subject to physical constraints and dynamical uncertainties. An extended-state-observer-based distributed model predictive control method is proposed for achieving a safe formation. Specifically, the vehicle dynamics is firstly transformed into an almost spherical form consisting of a position motion subsystem and an angular motion subsystem. Next, an extended state observer is used to estimate unknown model uncertainties and external disturbances in each subsystem. After that, by taking physical constraints and collision avoidance requirements into account, a distributed model predictive position tracking controller and a model predictive angular motion controller are designed based on the recovered model information through the extended state observers. The distributed formation control with collision avoidance problem is formulated as a constrained quadratic programming problem, which can be locally solved in a decentralized manner. Finally, the simulation results of five under-actuated unmanned surface vehicles substantiate the effectiveness of the proposed extended-state-observer-based distributed model predictive control method for multiple under-actuated unmanned surface vehicles.

Voltage-Based Droop Control of Electric Vehicles in Distribution Grids under Different Charging Power Levels

If left uncontrolled, electric vehicle charging poses severe challenges to distribution grid operation. Resulting issues are expected to be mitigated by charging control. In particular, voltage-based charging control, by relying only on the local measurements of voltage at the point of connection, provides an autonomous communication-free solution. The controller, attached to the charging equipment, compares the measured voltage to a reference voltage and adapts the charging power using a droop control characteristic. We present a systematic study of the voltage-based droop control method for electric vehicles to establish the usability of the method for all the currently available residential electric vehicle charging possibilities considering a wide range of electric vehicle penetrations. Voltage limits are evaluated according to the international standard EN50160, using long-term load flow simulations based on a real distribution grid topology and real load profiles. The results achieved show that the voltage-based droop controller is able to mitigate the under voltage problems completely in distribution grids in cases either deploying low charging power levels or exhibiting low penetration rates. For high charging rates and high penetrations, the control mechanism improves the overall voltage profile, but it does not remedy the under voltage problems completely. The evaluation also shows the controller’s ability to reduce the peak power at the transformer and indicates the impact it has on users due to the reduction in the average charging rates. The outcomes of the paper provide the distribution grid operators an insight on the voltage-based droop control mechanism for the future grid planning and investments.

Toward human-vehicle collaboration: Review and perspectives on human-centered collaborative automated driving

The last decade witnessed a great development of automated driving vehicles (ADVs) and vehicle intelligence. The significant increment of machine intelligence poses a new challenge to the community, which is the collaboration between human drivers and vehicle autonomy. As vehicle autonomy is gaining more control authority, the roles that human drivers can play in the future need to be further clarified. In this study, literature review and perspectives on the human behaviors and cognition (HBC) for ADVs toward human-autonomy (H-A) collaboration are proposed. First, the H-A collaboration basics and key factors are reviewed. Then, the HBC issues in driver behavior modeling and understanding are discussed. Specifically, two key factors are reviewed, which are human trust and situation awareness (SA). Next, HBC in two H-A collaboration-enabled vehicle control methods, namely, shared control and take-over control, are analyzed. It is shown that the human-centered collaboration strategy that integrates driver states will largely increase the acceptance of the ADVs. Then, the HBC issues in the design of human-machine-interface (HMI) for future autonomous and collaboration-enabled ADVs are discussed. Last, challenges and future works for H-A collaboration on ADVs are analyzed to contribute to the development of understandable, trustable, and acceptable ADVs.

Fault-Tolerant Adaptive Learning Control for Quadrotor UAVs With the Time-Varying CoG and Full-State Constraints.

Most existing control methods for quadrotor unmanned aerial vehicles (UAVs) are based on the primary assumption that the center of gravity (CoG) is fixed and is in the same position as the centroid, which is not necessarily true with swing load as continuously making CoG vary with the swing angle and substantially complicating the dynamic model of UAV. This article presents an adaptive learning and fault-tolerant control scheme for quadrotor UAVs with varying CoG and unknown moment of inertia. First, we establish the dynamic model of quadrotor UAVs in the presence of time-varying CoG, input saturation, and actuator fault. Then, we design a fault-tolerant adaptive learning controller for the quadrotor UAVs and show that both linear and angular velocity tracking errors are ensured to converge to a residual set around zero in the presence of full-state constraints. Furthermore, all signals in the closed-loop system are uniformly ultimately bounded. Simulation studies also confirm the effectiveness of the proposed control method.

A flight control method for unmanned aerial vehicles based on vibration suppression

AbstractThe development of unmanned aerial vehicle (UAVs) has gained importance in recent years for various applications like photography, inspection of infrastructure and various agricultural applications. Various researchers have carried out numerous attempts at optimisation control of UAVs and their flight range maximisation. However, a few attempts have addressed structural deformation effects on the reliability and safety of UAVs. In order to study the effect of structural deformation on the reliability and safety of UAVs under normal flight conditions, a certain type of UAV as a research object is discussed and the augmented tracking model based on the original object system by using the Lyapunov function is built up. The performance is observed in terms of rise time and overshoot and it is observed from the simulation outcomes that the system is stable at all design points in the range of 0–9000 m, providing a rise time of 10 s and zero overshoot. From this study, it is revealed that the designed controller can effectively suppress the vibration of the flexible mode while ensuring the stability and tracking performance of the UAV.

Flow Control Mechanism Based on Magnetic Induced Vortex for Reentry Vehicles

A novel magnetohydrodynamic (MHD) flow control mechanism based on the magnetic induced vortex is proposed to improve the thermal protection performance and aerodynamic performance of reentry vehicles. The magnetic field based on the polygonal coil grid is designed to obtain different vortex forms. Five Lorentz force models are constructed to reveal the MHD interaction mechanisms of the magnetic induced vortex. By numerically solving the low-magneto-Reynolds-number equations, the flowfields of the two-dimensional cylinder and three-dimensional sphere with the different vortex forms are obtained. Two vortex forms are found in the side and head regions of the cylinder, respectively. Under the influence of the vortexes, the high heat flux, which may be higher than the original one, is restricted in the regions near the coil centers in both the two- and three-dimensional cases. About 40% mitigation of total wall heat flux and nearly 60% drag increment are observed in the three-dimensional cases. By changing the switch states of the coils in the different regions, an additional aerodynamic force can be generated for the adjustment of flight attitude and trajectory. Hence, the magnetic induced vortex provides an effective MHD flow control method for reentry vehicles.

Nonlinear lateral motion stability control method for electric vehicle based on the combination of dual extended state observer and domination approach via sampled-data output feedback

This paper studies lateral motion stability control method for an electric vehicle (EV) with uncertain disturbances. Aiming to suppress the influence of uncertain disturbances, a novel method based on the combination of dual extended state observer (dESO) and domination approach is proposed. The proposed method enables the designed controller just to adopt a smaller scaling gain than the classic domination method, which is more practical for the controller in engineering. Firstly, a dESO is proposed to estimate the uncertain disturbances of the system. Also, a sampled-data output feedback domination approach is designed to dominate the disturbances by using a scaling gain. Then, the sampled-data output feedback control law is constructed based on the dESO and domination approach called dESOD. Furthermore, the stability analysis is presented to show that the proposed sampled-data controller can guarantee the closed-loop system globally asymptotically stable. Finally, the simulations and experiment results show the effectiveness of the proposed method.

Are Self-Driving Vehicles Ready to Launch? An Insight into Steering Control in Autonomous Self-Driving Vehicles

In the last couple of years, academia-industry collaboration has demonstrated rapid advancements in the development of self-driving vehicles. Since it is evident that self-driving vehicles are going to reshape the traditional transportation systems in near future through enhancement in safe and smart mobility, motion control in self-driving vehicles while performing driving tasks in a dynamic road environment is still a challenging task. In this regard, we present a comprehensive study considering the evolution of steering control methods for self-driving vehicles. Initially, we discussed an insight into the traditional steering systems of the vehicles. To the best of our knowledge, currently, there is no taxonomy available, which elaborates steering control methods for self-driving vehicles. In this paper, we present a novel taxonomy including different steering control methods which are categorized into deterministic and heuristic steering control methods. Concurrently, the abovementioned techniques are critically reviewed elaborating their strengths and limitations. Based on the analysis, key challenges/research gaps in existing steering control methods along with the possible solutions have been briefly discussed to improve the effectiveness of the steering system of self-driving vehicles.

Compound control method for Anti-Unmanned Aerial Vehicle (UAV) vertical launch missile

A compound control method with aerodynamic canard and reaction jet is proposed for anti-Unmanned Aerial Vehicle (UAV) vertical launch missile. The purpose of this method is to solve the problem of vertical launch with high angle of attack and low dynamic pressure. This paper first introduces the actuator layout and ignition strategy of reaction jet. Then the model of compound control system is established and compared with aerodynamic control in detail. Simulation studies demonstrate the effectiveness of the proposed method.

Optimal lateral control for autonomous vehicle based on multi-stage handling inverse dynamics

In this paper, an optimal lateral control method for autonomous vehicle based on multi-stage handling inverse dynamics is proposed. By applying this method, the vehicle can be controlled to complete the corresponding driving task without tracking a certain ideal state variable trajectory. Firstly, a separate-stage handling inverse dynamics problem is described; and by setting the connections between each stage, a multi-stage handling inverse dynamics model is developed; then we convert the inverse dynamics problem into an optimal control problem (OCP). Secondly, an improved hp-adaptive Radau pseudospectral method ( hp-RPM) is applied to discrete the continuous OCP; and then the sequential quadratic programming is used to solve the problem. In addition, the results of contrast verification show that, when solving non-smooth problem, the improved hp-RPM has higher computational efficiency than hp-RPM. Finally, simulations under three typical cases and bench tests illustrate the effectiveness of the proposed method.

Model Predictive Control Method for Autonomous Vehicles Using Time-Varying and Non-Uniformly Spaced Horizon

This paper proposes an algorithm for path-following and collision avoidance of an autonomous vehicle based on model predictive control (MPC) using time-varying and non-uniformly spaced horizon. The MPC based on non-uniformly spaced horizon approach uses the time intervals that are small for the near future, and time intervals that are large for the distant future, to extend the length of the whole prediction horizon with a fixed number of prediction steps. This MPC has the advantage of being able to detect obstacles in advance because it can see the distant future. However, the presence of longer time interval samples may lead to poor path-following performance, especially for paths with high curvature. The proposed algorithm performs proper adjustment of the prediction interval according to a given situation. For sections with large curvature, it uses the short prediction intervals to increase the path-following performance; further, to consider obstacles over a wider range, it uses the long prediction intervals. This technique allows simultaneous improvement of the path-following performance and the range of obstacle avoidance with fixed computational complexity. The effectiveness of the proposed method is verified through an open-source simulator, CARLA and real-time experiments.

Adaptive Backstepping Sliding Mode Tracking Control for Underactuated Unmanned Surface Vehicle With Disturbances and Input Saturation

This paper presents an adaptive backstepping sliding mode tracking control method for underactuated unmanned surface vehicle. The tracking controller is designed by combining backstepping and adaptive sliding mode control technology. The virtual control law is used to replace the control strategy of position error, and the position tracking control is transformed into velocity control, which can effectively avoid the input saturation and singular value problem existing in the design of traditional backstepping control law. The neural shunt model method is used to solve the differential explosion problem caused by virtual control law. The adaptive technology based on backstepping sliding mode theory is introduced to compensate the model uncertainty and time-varying disturbances of the system, and the robustness of the underactuated USV is enhanced by this method in the unknown environment. Based on the Lyapunov stability theory, it is proved that the control system error is ultimately uniformly bounded. The simulation results show that the proposed adaptive backstepping sliding mode tracking control method can achieve stable tracking in the case of system parameter uncertainty and time-varying disturbances, so it can be concluded that this method is reasonable and effective.

Zero-Error Tracking for Autonomous Vehicles Through Epsilon-Trajectory Generation

This letter presents a control method for vehicles with first-order nonholonomic constraints that guarantees asymptotic convergence to a time-indexed trajectory. To overcome the nonholonomic constraint, a fixed point in front of the vehicle can be controlled to track a desired trajectory, albeit with a steady-state error. To eliminate steady state error, a sufficiently smooth trajectory is reformulated for the new reference point such that, when tracking the new trajectory, the vehicle asymptotically converges to the original trajectory. The results of the control method are evaluated to view the effects of errors due to estimation and initial conditions.

Research on Starting Control Method of New-Energy Vehicle Based on State Machine

In order to improve the starting smoothness of new-energy vehicles under multiple working conditions and meet the driving intention better, and to make the control strategy have high portability and integration, a starting control method for vehicle based on state machine is designed. Based on inclination, starting of vehicle is divided into three working conditions: flat road, slight slope and steep slope. The method of vehicle starting control is designed, which includes five control states: default state control, torque pre-loading control, anti-rollback control, pedal control and PI (Proportion-Intergral) creep control. The simulation is carried out under the conditions of flat road, slight slope and steep slope. In terms of flat road and light slope, the vehicle travels below 3 km/h according to the driver’s intention, the speed is stable at 8 km/h during the creeping control phase and the jerk is lower than 5 m/s3. In terms of steep slope, the speed is controlled at 0 km/h basically and the 10 s-rollback distance is less than 0.04 m. The results show that the strategy can fully meet the driver’s intention with lower jerk, better dynamic and stability, and the method can achieve the demand of new-energy vehicle starting control.

Antisway Control of a Multirotor With Cable-Suspended Payload

This brief presents a new trajectory generation and an effective antisway tracking control (ATC) method for a multirotor unmanned aerial vehicle (UAV) with a cable-suspended payload. The UAV transportation system has been developed due to its ability to easily deliver small packages. However, existing methods lack research on transient response accompanied by aggressive rotation of the UAV and inevitable payload swing. Unfortunately, these motions become intensified when the input is dynamically unfeasible, such as step-like changes. Trajectory generation to minimize the swing motion provides a dynamically feasible trajectory between waypoints. The proposed method provides fast settling time with remarkably little payload swing. In addition, the ATC is developed on the basis of linear-quadratic (LQ) control to deal with motion coupling issues. A payload state estimator is also designed that requires no sensor for the payload. Finally, the performance of the proposed method is verified by numerical simulation and experimental results that show the possibility of safe and fast transportation with a minimal swing.

Optimised controlled charging of electric vehicles under peak power‐based electricity pricing

This study presents a practical control method for electric vehicle (EV) charging optimisation for detached and attached houses. The developed EV charging control method utilises real-time measurements to minimise charging costs of up to two EVs in a single household. Since some Finnish distribution system operators have already launched peak power-based distribution tariffs for small-scale customers and because there is a lot of discussion on this kind of tariff development, the control method considers peak power-based charges. Additionally, the proposed smart charging control method utilises charging current measurements as feedback to reallocate unused charging capacity if an EV does not utilise the whole capacity allocated for it. The control method is implemented and tested with commercial EVs. The conducted hardware-in-the-loop simulations and measurements confirm that the control method works as intended. The proposed smart charging control reduces EV charging electricity distribution costs around 60% when compared to the uncontrolled EV charging.

Multicriteria Autonomous Vehicle Control at Non-Signalized Intersections

The aim of the paper is to describe a multicriteria model predictive control method for autonomous vehicles at non-signalized intersections. The centralized controller aims to describe control action for each autonomous vehicle to guarantee collision free passage. At the same time, performances are defined for the centralized Model Predictive Controller, namely the minimization of traveling time and energy consumption. Since these control goals are often conflicting, a scheduling variable is introduced to create a balance between them. Hence, the centralized controller can be tuned based on the importance of each control goal, which can be useful in urban environment where traffic densities may vary heavily depending on the period of the day. The effectiveness of the proposed centralized multicriteria controller is demonstrated through a complex simulation example in CarSim simulation environment using different tpye of autonomous vehicles.

A Fault-Tolerant Control Method for Unmanned Aerial Vehicle (UAV) Formation with Topological Faults Considered

A fault-tolerant control method for the UAV formation that has topological fault, control surface fault, actuator fault and uncertainty is proposed. Firstly, the formation motion model and the UAV motion model are established. Then based on the topological fault detection method, the topological fault reconstruction and optimization algorithm is proposed to realize the formation topological fault reconstruction and optimization with minimum communication cost and formation reconstruction cost. In designing the backstepping fault-tolerant control method, the actuator's fault identification module and the auxiliary system module are used to estimate and compensate for the faults of the actuator and the control surface respectively so as to realize the stable flight of the UAV formation under the conditions of actuator fault, control surface fault and uncertainty. The simulation results verify the superiority of the fault-tolerant control method for the UAV formation with topological faults.

WIP Vehicle Control Method Based on Improved Artificial Potential Field Subject to Multi-obstacle Environment

This paper presents a control method for a WIP vehicle in multi-obstacle environment based on improved artificial potential field. Firstly, an improved artificial potential field (IAPF) is developed, where a safe distance is introduced to the existing repulsive potential field to solve the security issue, while the local minima can also be eliminated in the meantime. Next, an obstacle avoidance controller is designed based on the IAPF, where the nonholonomic constraint and underactuated characteristic of the WIP vehicle are fully considered, and the stability condition of the system is analyzed by means of the related control theory. Moreover, to further improve the control performance, a key parameter that play an important role in the controller is adjusted by taking advantage of fuzzy logic, and detailed analyses are given to demonstrate its necessity and effectiveness. Finally, considering a motion environment that contains dense obstacles, narrow corridor and an obstacle near the target, numerical simulations are conducted to validate the proposed method, whose results indicate that the method has a good performance to control the WIP vehicle in multi-obstacle environment.