Vehicle Control Algorithm Research Articles

Collision-Free Model Predictive Trajectory Tracking Control for UAVs in Obstacle Environment

In this paper, we propose a collision-free model predictive trajectory tracking control algorithm for unmanned aerial vehicles (UAVs) in environments with both static obstacles and dynamic obstacles. Collision avoidance is ensured by obtaining outer polyhedral approximations of each interval of the dynamic obstacles trajectories based on MINVO basis, and then optimizing a plane to separate the polyhedra and the trajectory of the UAV. By incorporating the resulting computationally efficient collisionfree constraints and divers physical constraints, a model predictive control (MPC) optimization problem is formulated with a tailored terminal constraint set, which can be solved by a standard nonlinear programming solver. Moreover, the control theoretic properties are established, including recursive feasibility, the guarantee of collision avoidance, as well as closed-loop stability. Finally, the efficacy of the proposed algorithm is successfully evaluated by a simulation in a multi-obstacle environment.

Integrated vehicle control using adaptive control allocation

SummaryThe focus of this paper is an integrated, fault‐tolerant vehicle control algorithm for the overall stability of ground vehicles. The proposed scheme comprises a high‐level controller that creates a virtual control input and a low‐level adaptive control allocator that distributes the virtual control effort among redundant actuators. The proposed control framework distinguishes itself from earlier results in the literature by its ability to blend active suspension, steering and traction control channels, in the presence of uncertainties and time‐varying dynamics, without the need for fault identification. The control structure is validated in the simulation environment using a fourteen‐degree‐of‐freedom non‐linear vehicle model. The integrated controller is compared to the case of a conventional control approach where each control problem is solved separately. Our results show that, compared to the conventional approach, the proposed method ensures that the vehicle follows driver inputs with up to % higher longitudinal maneuver velocity, despite the presence of actuator failures and slippery road conditions. Furthermore, to demonstrate the benefit of integrating active suspension control to the overall control scheme, we replaced the suspension control of the proposed approach with an independent suspension control system for comparison purposes. We then showed that the integrated case provided % lower roll angle deviation, and % lower pitch angle deviation, in the presence of actuator effectiveness loss and adverse road conditions.

Adaptive asymptotic tracking control for autonomous underwater vehicles with non-vanishing uncertainties and input saturation

This article provides an adaptive asymptotic backstepping-based tracking control algorithm for autonomous underwater vehicles (AUVs) under the situation of non-vanishing uncertainties and input saturation. Firstly, the adaptive asymptotic control algorithm is developed to compensate the non-vanishing uncertainties. And then, virtual control stabilizing functions are reconstructed to improve the system performance by virtue of variable substitutions. Next, the hyperbolic tangent function is applied to handle the unknown input saturation problem. Based on the Lyapunov stability theory, the asymptotic stability of AUVs is proven. Finally, simulation results illustrate that the control strategy can effectively inhibit the effect of unknown model dynamics on the system.

Comparative Assessment of Supervisory Control Algorithms for a Plug-In Hybrid Electric Vehicle

The study examines alternative on-board energy management system (EMS) supervisory control algorithms for plug-in hybrid electric vehicles. The optimum fuel consumption was sought between an equivalent consumption minimization strategy (ECMS) algorithm and a back-engineered commercial rule-based (RB) one, under different operating conditions. The RB algorithm was first validated with experimental data. A method to assess different algorithms under identical states of charge variations, vehicle distance travelled, and wheel power demand criteria is first demonstrated. Implementing this method to evaluate the two algorithms leads to fuel consumption corrections of up to 8%, compared to applying no correction. We argue that such a correction should always be used in relevant studies. Overall, results show that the ECMS algorithm leads to lower fuel consumption than the RB one in most driving conditions. The difference maximizes at low average speeds (<40 km/h), where the RB leads to more frequent low load engine operation. The two algorithms lead to fuel consumption differences of 3.4% over the WLTC, while the maximum difference of 24.2% was observed for a driving cycle with low average speed (18.4 km/h). Further to fuel consumption performance optimization, the ECMS algorithm also appears superior in terms of adaptability to different driving cycles.

Lateral Vehicle Trajectory Planning Using a Model Predictive Control Scheme for an Automated Perpendicular Parking System

This article proposes a lateral vehicle trajectory planning and control algorithm using a model predictive control (MPC) scheme for an automated perpendicular parking system. Previous work showed that approximated clothoid-based local path planning using a virtual towing distance provides low computational time and the stability of lateral vehicle motion in a parking system. However, this approach cannot function in certain parking situations and may cause undesirable steering maneuvers due to state-dependent planning. We present a new approximated clothoid path based on lateral vehicle kinematics to cope with these problems. Using the proposed kinematic model, we formulate an MPC problem for path planning. Then, the proposed lateral vehicle trajectory planning becomes an MPC problem under constraints. Besides, we show that lateral vehicle motion for vertical parking can be implemented using simple kinematic lateral motion control. Experimental results show that the vehicle with the proposed method moves smoothly and completes the given parking mission under constraints, even under tight parking space conditions.

Modeling of a brushless dc motor driven electric vehicle and its pid-fuzzy control with dSPACE

In this study, a high power (75 kW) original driver and control algorithm has been developed for an electric passenger vehicle whose features can be used practically. It has been observed that some problems occur in the operation of the developed control algorithms in traction systems operating at high power. In this study, the solution methods of these problems are included. Firstly, the simulation model of an electrical vehicle was obtained by determining the basic parameters for a passenger electric vehicle. Then a brushless DC motor and drive system was determined for the electric vehicle and an original 75 kW DC-AC Converter (Inverter) in accordance with automotive standards has been designed and tested for the Brushless DC motor. Also, in the design and implementation phase, PID and Fuzzy control-based vehicle control software was developed in MATLAB/Simulink environment on the purpose of rapid prototyping and loaded on the DS1401 dSPACE based control system. It has been seen that through rapid prototyping, the appropriate controller development cycle time for the vehicle drastically reduced, which significantly has reduced the research and development costs. In the vehicle control algorithm, speed information is used as a reference input and brake information is used as feedback. The control signal generated by the controller is converted into PWM pulses for each phase and applied to the IGBT driver. These PWM pulses were used to switch the six IGBT power components used in the three-phase full-bridge DC-AC converter. Driving performance at the design stage has been studied for cases of starting, speed, reversal and load failure. Simulation and experimental results demonstrated the effectiveness of the driver and drive control system that were originally developed. When the system response was examined, it was revealed that the fuzzy logic control algorithm presented much better results than the PI and then the PID control algorithm. Simulation results and application results were consistent with each other and the system performance was successfully tested. Many protection circuits have been designed and configured in the system, with the control algorithms developed according to the problems arising in the operation of high-power systems, hardware add-ons for the operation of the high power (75kW) power-train. Safety and security infrastructures have been developed in both hardware and software for the appropriate certification in automotive standards.

A Beta-less Approach for Vehicle Cornering Stiffness Estimation

With an increasing number of driver assistance functions and the upcoming trend of autonomous driving, knowledge of the current state and changeable parameters of the vehicle becomes increasingly essential. Particularly significant parameters with regard to vehicle lateral dynamics are the cornering stiffnesses, which describe the tire lateral forces depending on the current slip angles. Thus, the cornering stiffness characterizes the current lateral force of a tire, which heavily varies over different tires and decreases by tire wear. However, its knowledge is crucial for describing the vehicle dynamics behavior. Therefore, vehicle control algorithms and driver assistance functions would highly benefit from knowledge of the vehicle cornering stiffness values, which could increase driving comfort and safety. For that purpose, this paper introduces an online estimation algorithm for the tire cornering stiffness values. Only typical vehicle series sensors are required for the algorithm, which ensures its applicability for series vehicles. Finally, vehicle measurements are conducted in order to validate the proposed estimation algorithm by means of the resulting cornering stiffnesses.

A Novel 3D Path Following Scheme for a Class of Underactuated Underwater Vehicles with Unconventional Actuation

A 3D path following algorithm involving a novel motion control methodology is investigated for use on a new class of autonomous underwater vehicle (AUV). Unlike standard path following AUV control algorithms for underactuated vehicles, where roll perturbations are avoided, this unconventional algorithm utilizes active roll control to achieve a desired heading. The desired heading is calculated using traditional look-ahead techniques, and is achieved by performing a roll rotation to align the body-fixed yaw plane with the desired heading, and then a yaw rotation is performed to achieve that desired heading. It is demonstrated that this new path following algorithm achieves good tracking results, but experiences complications for increased forward velocities. It is also demonstrated that the complications at higher velocities can be overcome with adaptive adjustment of the look-ahead algorithm parameters.

Trajectory Planning and Control of Autonomous Vehicles for Static Vehicle Avoidance in Dynamic Traffic Environments

This paper presents a trajectory planning and control algorithm of autonomous vehicles for static traffic agent avoidance in multi vehicle urban environments. In urban autonomous driving, the subject vehicle encounters diverse traffic scenes including lane changing, intersection driving, and illegally parked static vehicle avoidance. Among these, dealing with illegally parked static target vehicle is a major challenge to urban autonomous driving due to large velocity difference between ego and target vehicles and interactions with surrounding vehicles. In order to tackle this problem, we introduce a decision making and motion planning framework for static vehicle avoidance considering both the preceding static vehicles and surrounding vehicles. Among the surrounding vehicles, the set of objects with potential collision risk is selected based on the lane boundaries and road geometry. Then, the driving status of the selected target vehicles are classified as normal driving vehicles or parked vehicles based on their longitudinal speed, lateral position and lateral space occupancy. For the preceding parked vehicles, the motion planner generates lateral and longitudinal evasive motion, by taking side lane traffic flow and risk into account. The desired motion is executed by applying optimized control inputs computed by lateral and longitudinal model predictive controllers. The performance validation of the proposed algorithm has been conducted with actual autonomous test vehicles. The test results confirmed that the proposed algorithm can successfully perform evasive maneuvers on urban roads to ensure safety and mitigate collision risk with the surrounding traffic agents.

A Literature Review of Energy Optimal Adaptive Cruise Control Algorithms

Transportation sector and its impacts on climate change have received much attention over the last decade. Energy-optimal vehicle control algorithms such as adaptive cruise control can potentially reduce fuel consumption and the consequential environmental impacts. Adaptive cruise control algorithms optimize vehicles’ speed to lower energy consumption considering several constraints, including safety, stability, and comfortability. A wide range of algorithms with different objectives and optimization mechanisms have been reported in the literature. Due to the high diversity of constraints and objectives, a comprehensive study of these algorithms is required. In recent literature reviews, there is no comprehensive summary of the adaptive cruise control algorithms’ features, classes, and objectives. This paper presents a holistic literature review of energy-optimal adaptive cruise control algorithms. We gathered relevant publications from well-eminent journals. Information on diversity and the number of publications will be presented to provide data on used references. For each paper, the objectives, features, and relevant classes of algorithms were extracted. Then a classification based on objectives is suggested, and mathematical formulas of representative studies are summarized to provide the required knowledge regarding mathematical modeling and optimization in this field. The study provides a useful insight into the development of the cruise control systems research field, revealing those scientific actors (i.e., authors, developers, and institutions) that have made the biggest research contribution to its development.

An Open-Source Benchmark Simulator: Control of a BlueROV2 Underwater Robot

This paper presents a simulation model environment for the popular and low-cost remotely operated vehicle (ROV) BlueROV2 implemented in Simulink™ which has been designed and experimentally validated for benchmark control algorithms for underwater vehicles. The BlueROV2 model is based on Fossen’s equations and includes a kinematic model of the vehicle, the hydrodynamics of vehicle and water interaction, a dynamic model of the thrusters, and, lastly, the gravitational/buoyant forces. The hydrodynamic parameters and thruster model have been validated in a test facility. The benchmark model also includes the ocean current, modeled as constant velocity. The tether connecting the ROV to the top-site facility has been modeled using the lumped mass method and is implemented as a force input to the ROV model. At last, to show the usefulness of the benchmark model, a case study is presented where a BlueROV2 is deployed to inspect an offshore monopile structure. The case study uses a sliding mode controller designed for the BlueROV2. The controller fulfills the design criteria defined for the case study by following the provided trajectory with a low error. It is concluded that the simulator establishes a benchmark for future control schemes for position control and trajectory tracking under the influence of environmental disturbances.

Completely Distributed Affine Formation Maneuvering of Networked Marine Surface Vehicles With Cooperation Localization

This paper develops a completely distributed affine formation maneuver control (CDAFC) algorithm for networked marine surface vehicles (NMSVs). The desired trajectories are generated by the affine image. Accordingly, NMSVs are capable to achieve arbitrary preset configuration transformations, while only a small part of the formation members called leaders obtain the configuration information. Then, a synthetical constraint is designed, which can concern position and attitude configuration rigidities simultaneously. This feature enables NMSVs to achieve formation maneuvers with time-varying formation speeds and orientations. On these bases, a novel CDAFC scheme is developed. In contrast to the conventional fully distributed control schemes, the implementation requirements are completely limited to the local reference frame, i.e., not only is CDAFC designed in a node-based way, but the CDAFC implementation just depends on the local relative poses and self velocities. Thus, we call it <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">completely distributed</i> . The semi-physical simulations are conducted to illustrate the effectiveness.

Distributed Cooperative Control of Multiple UAVs in the Presence of Actuator Faults and Input Constraints

A distributed cooperative control algorithm of multiple unmanned aerial vehicles (UAVs) with directed topology is investigated in this brief. To overcome the actuator faults and external disturbance, incremental sliding mode formation controller is developed to realize the distributed coordination of the UAVs system, where an adaptive mechanism is utilized to deal with the chattering problem. Moreover, to reduce the adverse influence of input saturation, an auxiliary system is introduced to regulate the control signals. It is shown by Lyapunov method that the stability of the proposed algorithm can be guaranteed. A numerical example is given to illustrate that all follower UAVs can track the leader UAV.

A Platoon-Based Hierarchical Merging Control for On-Ramp Vehicles Under Connected Environment

Connected autonomous vehicle technology is conductive to promoting the transition from traditional merging control (e.g., ramp metering) to automated merging control. This paper proposes a platoon-based hierarchical merging control algorithm for on-ramp vehicles to achieve automated merging control under connected traffic environment. The proposed algorithm optimizes merging maneuvers of on-ramp vehicles to smooth their merging trajectories without frequent decelerations or stops at the end of the ramp, and to minimize disruption to the mainline traffic in the merging zone. A tactical layer controller is designed to select pre-target merging gaps for on-ramp vehicles, in which the future motion (i.e., acceleration and deceleration) of mainline vehicles is considered through the grey prediction model. An operational layer controller is constructed based on model predictive control to adjust the speed of on-ramp vehicles in advance, and controls on-ramp vehicles to merge into the pre-target merging gaps under state constraints (i.e., safe headway, maximum speed and so on). Through numerical simulation, the effectiveness of the proposed algorithm is validated under different merging scenarios. It is shown that on-ramp vehicles smoothly merge into the mainline within the pre-target merging gap at the same speed as adjacent mainline vehicles. Compared with the baseline merging control algorithm, the proposed algorithm significantly reduces both fuel consumption and travel time of on-ramp vehicles, and improves passenger comfort.

An Intelligent Predictive Algorithm for the Anti-Rollover Prevention of Heavy Vehicles for Off-Road Applications

Rollover detection and prevention are among the most critical aspects affecting the stability and safety assessment of heavy vehicles, especially for off-road driving applications. This topic has been studied in the past and analyzed in depth in terms of vehicle modelling and control algorithms design able to prevent the rollover risk. However, it still represents a serious problem for automotive carmakers due to the huge counts among the main causes for traffic accidents. The risk also becomes more challenging to predict for off-road heavy vehicles, for which the incipient rollover might be triggered by external factors, i.e., road irregularities, bank angles as well as by aggressive input from the driver. The recent advances in road profile measurement and estimation systems make road-preview-based algorithms a viable solution for the rollover detection. This paper describes a model-based formulation to analytically evaluate the load transfer dynamics and its variation due to the presence of road perturbations, i.e., road bank angle and irregularities. An algorithm to detect and predict the rollover risk for heavy vehicles is also presented, even in presence of irregular road profiles, with the calculation of the ISO-LTR Predictive Time through the Phase-Plane analysis. Furthermore, the artificial intelligence techniques, based on the recurrent neural network approach, is also presented as a preliminary solution for a realistic implementation of the methodology. The paper finally assess the efficacy of the proposed rollover predictive algorithm by providing numerical results from the simulation of the most severe maneuvers in realistic off-road driving scenarios, also demonstrating its promising predictive capabilities.

Virtual Training System for Unmanned Aerial Vehicle Control Teaching–Learning Processes

The present work is focused on the development of a Virtual Environment as a test system for new advanced control algorithms for an Unmanned Aerial Vehicles. The virtualized environment allows us to visualize the behavior of the UAV by including the mathematical model of it. The mathematical structure of the kinematic and dynamic models is represented in a matrix form in order to be used in different control algorithms proposals. For the dynamic model, the constants are obtained experimentally, using a DJI Matrice 600 Pro UAV. All of this is conducted with the purpose of using the virtualized environment in educational processes in which, due to the excessive cost of the materials, it is not possible to acquire physical equipment; moreover, is it desired to avoid damaging them. Finally, the stability and robustness of the proposed controllers are determined to ensure analytically the compliance with the control criteria and its correct operation.

Autonomous vehicle trajectory tracking lateral control based on the terminal sliding mode control with radial basis function neural network and fuzzy logic algorithm

Abstract. This paper will study a trajectory tracking control algorithm for electric vehicles based on a terminal sliding mode controller. First, a 3 degrees of freedom nonlinear vehicle model and a controller-oriented 2 degrees of freedom vehicle model are established. The preview time is adaptively adjusted based on the preview model. Then, the vehicle trajectory tracking controller, which uses the terminal sliding mode algorithm, is designed. The radial basis function (RBF) neural network algorithm is used to approximate the system variable parameters in the control model online. At the same time, fuzzy logic is used to control the gain parameters of the controller to reduce the chattering of the control system. Finally, the designed controller is verified by simulation. The maximum deviation of path tracking under different speeds is 0.6 m, and the target path can also be well followed under different road friction coefficients. The simulation results show that the controller designed in this paper can effectively carry out the vehicle trajectory tracking and lateral control and reduce the chattering to a certain extent.

Time headway distribution analysis of naturalistic road users based on aerial datasets

Purpose Time headway (THW) is an essential parameter in traffic safety and is used as a typical control variable by many vehicle control algorithms, especially in safety-critical ADAS and automated driving systems. However, due to the randomness of human drivers, THW cannot be accurately represented, affecting scholars’ more profound research. Design/methodology/approach In this work, two data sets are used as the experimental data to calculate the goodness-of-fit of 18 commonly used distribution models of THW to select the best distribution model. Subsequently, the characteristic parameters of traffic flow are extracted from the data set, and three variables with higher importance are extracted using the random forest model. Combining the best distribution model parameters of the data set, this study obtained a distribution model with adaptive parameters, and its performance and applicability are verified. Findings In this work, two data sets are used as the experimental data to calculate the goodness-of-fit of 18 commonly used distribution models of THW to select the best distribution model. Subsequently, the characteristic parameters of traffic flow are extracted from the data set, and three variables with higher importance are extracted using the random forest model. Combining the best distribution model parameters of the data set, this study obtained a distribution model with adaptive parameters, and its performance and applicability are verified. Originality/value The results show that the proposed model has a 62.7% performance improvement over the distribution model with fixed parameters. Moreover, the parameter function of the distribution model can be regarded as a quantitative analysis of the degree of influence of the traffic flow state on THW.

Design, Fabrication, and Control Algorithm of Self-Reconfigurable Modular Intelligent Vehicles

Self-reconfigurable vehicles are able to transform into new formations on the basis of mutual communication and positioning so as to adapt to a new environment and new tasks. Compared with fixed-formation vehicles, reconfigurable vehicles feature multifunction and high reliability. First, a modular vehicle with a Mecanum wheel is designed, and a motion control method of a modular vehicle is derived through the establishment and analysis of its kinematics model. Afterwards, a visual positioning system based on AprilTag is designed, and the reconfiguration experiment of modular vehicles is completed in order to validate the feasibility of the proposed control method. A hardware control platform is built, which is able to realize the omnidirectional movement of the vehicle in the plane. The proposed modular vehicles with the corresponding control method successfully completes centralized perception, trajectory planning, and self-reconfiguration, which can be potentially applied in various scenarios, such as orientation guidance and logistics handling.

Synchronization Tracking Of Uncertain Nonidentical Multi-USV system

We propose a distributed adaptive synchronous tracking control algorithm for the heterogeneous multi-unmanned surface vehicles described by the Euler-Lagrange system. In multi-unmanned surface vehicles system, the inter-communication state can be represented by a directed topology graph, and only part of unmanned surface vehicles can directly acquire the accurate information of time-varying reference trajectory. The proposed algorithm can implement synchronous tracking for multiple unmanned surface vehicles without knowing global communication information by elaborately selecting Lyapunov functions. We conduct theoretical analysis of the stability of closed-loop systems according to the Lyapunov stability theory. And we also verify the effectiveness of proposed scheme through numerical simulation.