All-wheel Drive Research Articles

Optimization Control of Multi-Mode Coupling All-Wheel Drive System for Hybrid Vehicle

The all-wheel drive (AWD) hybrid system is a research focus on high-performance new energy vehicles that can meet the demands of dynamic performance and passing ability. Simultaneous optimization of the power and economy of hybrid vehicles becomes an issue. A unique multi-mode coupling (MMC) AWD hybrid system is presented to realize the distributed and centralized driving of the front and rear axles to achieve vectored distribution and full utilization of the system power between the axles of vehicles. Based on the parameters of the benchmarking model of a hybrid vehicle, the best model-predictive control-based energy management strategy is proposed. First, the drive system model was built after the analysis of the MMC-AWD's drive modes. Next, three fundamental strategies were established to address power distribution adjustment and battery SOC maintenance when the SOC changed, which was followed by the design of a road driving force observer. Then, the energy consumption rate in the average time domain was processed before designing the minimum fuel consumption controller based on the equivalent fuel consumption coefficient. Finally, the advantage of the MMC-AWD was confirmed by comparison with the dynamic performance and economy of the BYD Song PLUS DMI-AWD. The findings indicate that, in comparison to the comparative hybrid system at road adhesion coefficients of 0.8 and 0.6, the MMC-AWD's capacity to accelerate increases by 5.26% and 7.92%, respectively. When the road adhesion coefficient is 0.8, 0.6, and 0.4, the maximum climbing ability increases by 14.22%, 12.88%, and 4.55%, respectively. As a result, the dynamic performance is greatly enhanced, and the fuel savings rate per 100 km of mileage reaches 12.06%, which is also very economical. The proposed control strategies for the new hybrid AWD vehicle can optimize the power and economy simultaneously.

Effect of Soil Properties and Powertrain Configuration on the Energy Consumption of Wheeled Electric Agricultural Robots

Agricultural emissions can be significantly reduced with smart farming, which includes moving away from large conventional tractors to fleets of compact wheeled electric robots. This paper presents a novel simulation modeling approach for an ATV-sized wheeled electric agricultural robot pulling an implement on deformable terrain. The 2D model features a semiempirical tire–soil interaction model as well as a powertrain model. Rear-wheel drive (RWD), front-wheel drive (FWD), and all-wheel drive (AWD) versions were developed. Simulations were carried out on two different soils to examine the energy consumption and tractive performance of the powertrain options. The results showed that energy consumption varies the least with AWD. However, RWD could provide lower energy consumption than AWD with light workloads due to lower curb weight. However, with the heaviest workload, AWD had 7.5% lower energy consumption than RWD. FWD was also found to be capable of lower energy consumption than AWD on light workloads, but it was unsuited for heavy workloads due to traction limitations. Overall, the results demonstrated the importance of taking the terrain characteristics and workload into account when designing electric agricultural robots. The developed modeling approach can prove useful for designing such machines and their fleet management.

Planning Speed Mode of All-Wheel Drive Autonomous Vehicles Considering Complete Constraint Set

The study aims to improve the technique of motion planning for all-wheel drive (AWD) autonomous vehicles (AVs) by including torque vectoring (TV) models and extended physical constraints. Four schemes for realizing the TV drive were considered: with braking internal wheels, using a rear-axle sport differential (SD), with braking front internal wheel and rear-axle SD, and with SDs on both axles. The mathematical model combines 2.5D vehicle dynamics model and a simplified drivetrain model with the self-locking central differential. The inverse approach implies optimizing the distribution of kinematic parameters by imposing a set of constraints. The optimization procedure uses the sequential quadratic programming (SQP) technique for the nonlinear constrained minimization. The Gaussian N-point quadrature scheme provides numerical integration. The distribution of control parameters (torque, braking moments, SDs’ friction moment) is performed by evaluating linear and nonlinear algebraic equations inside of optimization. The technique proposed demonstrates an essential difference between forecasts built with a pure kinematic model and those considering the vehicle’s drive/control features. Therefore, this approach contributes to the predictive accuracy and widening model properties by increasing the number of references, including for actuators and mechanisms.

Data Analysis and Traction Performance Evaluation of an Electric All-Wheel Drive Tractor During Agricultural Operation

Highlights The study evaluated the feasibility of using a new type of platform, the electric AWD tractor, for agricultural operations. The data for key components of the electric AWD tractor during plowing were analyzed. The traction performance of the electric AWD tractor was evaluated via tractive efficiency and dynamic ratio. The electric AWD tractor was expected to replace conventional tractors in terms of traction performance. Abstract. Owing to the increasing need for research on electric all-wheel drive (AWD) systems, an investigation on the AWD systems applicable to tractors was conducted in this study. The experimental platform consisted of an electric power transmission system including four sets of electric motors, helical and planetary reducers, wheels, and an electrical system including chargers, batteries (LiFePO4), converters, and inverters. The drive control system of the electric AWD tractor was divided into the AWD and the motor controller. The data measurement system consisted of analog (current and traction force) and digital (battery voltage, current, motor rotational speed, state of charge (SOC) level, and travel speed) components that communicate using a controller area network (CAN) bus. Data measured during plowing were used to calculate motor power, motor torque, traction power, and driving power to analyze the power consumption of an electric AWD tractor. The traction performance of the electric AWD tractor was evaluated using tractive efficiency, calculated values (traction power and driving power), and dynamic ratio. The maximum and minimum average voltage of the electric AWD tractor during plowing were 72.2 and 64.7 V, respectively, and the average value was measured as 68.2 V. The average value of the maximum current applied to the inverter was 362.4 A, and the mean value was measured as 187.5 A. The average value of the maximum rotational speed of the four motors was 2111.5 rpm, while the mean rotational speed during the operation was 1131.9 rpm. The average value of motor torque was 79.4 Nm. The decrease rate of battery SOC level per minute was calculated as an average of 1.4%/min. The maximum values of the traction and driving power during plowing were 42.0 and 73.7 kW. The maximum tractive efficiency (TE) was 0.77, with an average TE of 0.47. The maximum dynamic ratio (DR) was 0.28, with an average DR of 0.18. The TE of a conventional tractor was found to be a maximum of 0.61, with averages of 0.41, respectively. The average TE of the electric AWD tractor was approximately 14% higher than that of the conventional tractor. Data analysis indicates that the electric AWD tractor performing speed control is vulnerable to slip, and further research is necessary to improve AWD system performance through speed and slip control. Keywords: Data analysis, Data measurement system, Electric AWD tractor, Plowing, Traction performance.

Using Inverse Dynamics Technique in Planning Autonomous Vehicle Speed Mode Considering Physical Constraints

The study aims at improving the technique of planning the autonomous vehicles’ (AV) speed mode based on a kinematic model with physical restrictions. A mathematical model relates the derivatives of kinematic parameters with ones of the trajectory’s curvature. The inverse approach uses an expanded vehicle model considering the distribution of vertical reactions, wheels’ longitudinal reactions according to a drive type, and lateral forces ensuring motion stability. For analysis of the drive type, four options are proposed: front-wheel drive (FWD), rear-wheel drive (RWD), permanent engaged all-wheel drive (AWD), and 4-wheel drive with torque vectoring (4WD-TV). The optimization model is also built by the inverse scheme. The longitudinal speed’s higher derivatives are modeled by the finite element (FE) functions with nodal unknowns. The sequential integrations ensure the optimality and smoothness of the third derivative. The kinematic restrictions are supplemented by the tire-road critical slip states. Sequential quadratic programming (SQP) and the Gaussian N-point scheme for quadrature integration are used to minimize the objective function. The simulation results show a significant difference in the mode forecasts between four types of AV drives at the same initial conditions. This technique allows redistributing the traction forces strictly according to the wheels’ adhesion potentials and increases the optimization performance by about 40% compared to using the kinematic model based on the same technique without physical constrains.

Parallel Operation of Integrated Battery Chargers for All Wheel Drive Electric Vehicles

In this paper, an integrated charging scheme for All Wheel Drive (AWD) Electric Vehicles (EVs) is proposed. The proposed scheme utilizes the existing driving hardware for charging the battery pack with minimum external components. As the driving hardware of AWD EVs include two motors and two inverters, an Integrated Battery Charger (IBC) with parallel configuration is proposed. In the existing topology of three phase IBC using split phase machine, the grid currents were not balanced. But in the proposed parallel IBC topology, the two split phase machines are proposed with different phase configurations, namely R-Y-B and Y-R-B, which will cancel the unbalance caused by each IBC, and make the grid currents balanced. The mathematical proof for the same is also included in this paper.The proposed parallel topology of IBCs has higher power rating and hence provides faster charging compared to conventional single IBCs. Circulating current mitigation for parallel IBCs is also presented in this paper. Exhaustive simulation and experimental results are included to validate the proposed scheme.

A Concept for Using Road Wetness Information in an All-Wheel-Drive Control

This paper presents a concept for using road wetness information in an all-wheel-drive (AWD) control that distributes drive torques in the longitudinal direction. Driving on wet roads requires special attention. Not only does the road surface friction coefficient decrease, but driving dynamics targets must be adjusted to prevent vehicle instability under wet conditions. As an exemplary application, the otherwise generic control concept is implemented on an AWD vehicle with a torque-on-demand transfer case. Therefore, the AWD topology of a drive train with a torque-on-demand transfer case is analysed in advance in terms of occurring torques and rotational speeds. In the following, the vehicle dynamics goals for driving in wet road conditions are described—divided into primary and secondary goals. Starting from a state-of-the art AWD control, an adaptive control strategy is derived by superimposing a wetness coordination unit. With the knowledge of occurring road wetness, this unit adapts newly introduced parameters in order to meet the target driving behaviour under wet conditions. Lastly, the derived AWD control is implemented into a 14-DOF, non-linear vehicle model in Matlab/Simulink, which is used as a virtual plant. The performance of the developed concept is assessed by the driving maneuver “Power On Cornering“ (PON), which means an acceleration out of steady-state circular motion. As its essential benefit, the AWD control enables a maximum spread between driving stability, agility and traction under combined dynamics when using wetness information. The newly introduced wetness coordination unit uses only a few additional and physically interpretable key parameters for this purpose, without significantly increasing the controller complexity.

Traction Performance Evaluation of the Electric All-Wheel-Drive Tractor.

This study aims to design, develop, and evaluate the traction performance of an electric all-wheel-drive (AWD) tractor based on the power transmission and electric systems. The power transmission system includes the electric motor, helical gear reducer, planetary gear reducer, and tires. The electric system consists of a battery pack and charging system. An engine-generator and charger are installed to supply electric energy in emergency situations. The load measurement system consists of analog (current) and digital (battery voltage and rotational speed of the electric motor) components using a controller area network (CAN) bus. A traction test of the electric AWD tractor was performed towing a test vehicle. The output torques of the tractor motors during the traction test were calculated using the current and torque curves provided by the motor manufacturer. The agricultural work performance is verified by comparing the torque and rpm (T–N) curve of the motor with the reduction ratio applied. The traction is calculated using torque and specifications of the wheel, and traction performance is evaluated using tractive efficiency (TE) and dynamic ratio (DR). The results suggest a direction for the improvement of the electric drive system in agricultural research by comparison with the conventional tractor through the analysis of the agricultural performance and traction performance of the electric AWD tractor.

Modeling and Reinforcement Learning Control of an Autonomous Vehicle to Get Unstuck From a Ditch

AbstractAutonomous vehicle control approaches are rapidly being developed for everyday street-driving scenarios. This article considers autonomous vehicle control in a less common, albeit important, situation “a vehicle stuck in a ditch.” In this scenario, a solution is typically obtained by either using a tow-truck or by humans rocking the vehicle to build momentum and push the vehicle out. However, it would be much more safe and convenient if a vehicle was able to exit the ditch autonomously without human intervention. In exploration of this idea, this article derives the governing equations for a vehicle moving along an arbitrary ditch profile with torques applied to front and rear wheels and the consideration of four regions of wheel-slip. A reward function was designed to minimize wheel-slip, and the model was used to train control agents using Probabilistic Inference for Learning COntrol (PILCO) and deep deterministic policy gradient (DDPG) reinforcement learning (RL) algorithms. Both rear-wheel-drive (RWD) and all-wheel-drive (AWD) results were compared, showing the capability of the agents to achieve escape from a ditch while minimizing wheel-slip for several ditch profiles. The policy results from applying RL to this problem intuitively increased the momentum of the vehicle and applied “braking” to the wheels when slip was detected so as to achieve a safe exit from the ditch. The conclusions show a pathway to apply aspects of this article to specific vehicles.

Design of all-wheel-drive power-split hybrid configuration schemes based on hierarchical topology graph theory

Power-split hybrid electric vehicles (PS-HEVs) can decouple the rotation speed and torque of the engine and power output shaft to ensure that the engine can work at efficient operating points as much as possible. All-wheel-drive (AWD) PS-HEVs can recover braking energy and have good trafficability, giving them good market application prospects. In this paper, we propose a powertrain configuration scheme design method based on hierarchical topological graph theory. Through kinematics and dynamics modeling, configuration mode classification, and scheme synthesis of PS-HEV powertrains, AWD PS-HEV configuration schemes are designed. The dynamic programming (DP) and rapid DP (Rapid-DP) algorithms are used to optimize the configuration schemes. Simulation results show that the AWD PS-HEVs designed by this method will have better fuel economy and acceleration performance than existing designs.

Tribological Behavior and Microstructural Analysis of Atmospheric Plasma Spray Deposited Thin Coatings on Cardan Cross Spindles.

Cardan joints are used in transmissions between misaligned shafts, as in all-wheel-drive (AWD) cars and railway applications. Their functioning is accompanied by heavy cyclical loads, with the cardan cross spindles subjected to intensive abrasive wear and pitting. In this paper, a solution to the mentioned issue is proposed, thin anti-wear coatings of Metco 32 and Metco 72 metallic powders deposited by atmospheric plasma spray (APS) on cylindrical samples cut from spindles of two cardan crosses made of 40Cr10 and RUL2 steel. The morphological analysis of the coated surfaces was realized by scanning electron microscopy (SEM), and the elemental composition of the tested samples was elaborated by energy-dispersive X-ray spectroscopy (EDS). To investigate the wear resistance of the coated samples in dry and grease-lubricated conditions, tests at constant load and constant speed were carried out using an AMSLER tribometer. The results of greased tests proved that the expulsion of the lubricant from the tribological contact occurred no matter the combination of coated or uncoated samples. During grease-lubricated tests of ten minutes, the least coefficient of friction was measured for uncoated specimens with better surface finishing; but in dry friction tests, the lowest values of the mean friction coefficients were obtained for the Metco 72 coatings. The porous coatings may act as lubricant reservoirs in long-lasting tests, providing a solution to the expulsion phenomenon of the lubricant to the boundary outside the area of the larger-diameter roller.

A sensorless traction strategy for all-wheel drive electric motorcycles

Motorcycle designers aim to enhance vehicle performance, stability, and safety, especially when the vehicle is approaching the tyres' friction limit. A strategy to achieve these purposes is to design All-Wheel Drive (AWD) vehicles with active torque distribution. Nowadays, effective technical solutions for powering the front wheel of a motorcycle have been proposed. The use of traction on both front and rear wheel is convenient when tyre friction is low, enhancing vehicle safety and performance. In this context, this paper presents a sensorless, open-loop traction strategy for AWD motorcycles that improves performance and stability both in straight running and in cornering, without requiring the knowledge of road-tyre adherence. This novel traction distribution strategy is based on the idea of maximising the area of the motorcycle performance envelope, also known as g-g diagram. A simplified mathematical model is used to find closed-form solution to this problem, then converted into a control algorithm for the front and rear driving torques. The performance of the proposed method is assessed with simulations: a detailed model of a light electric motorcycle is used for calculating the non-linear vehicle performance envelope, the acceleration response in straight running and cornering, and the response to road irregularities. Moreover, a comparison with feedback slip-based traction control system is conducted. Simulation results indicate that the AWD motorcycle outperforms the conventional rear wheel drive motorbike almost in all situations, especially when road-tyre adherence is low, and that no counter-indication for the adoption of the AWD architecture exists.

LQR Control of an All-Wheel Drive Vehicle Considering Variable Input Constraint

This article suggests a model-based controller for an all-wheel drive (AWD) vehicle using a novel vehicle dynamics model. Recently, an active type of AWD system that automatically controls the clutch engagement force of the transfer case has become popular. However, its performance has been limited by its rule-based controller, which necessitates the development of a model-based controller. Although the bicycle model has been most widely adopted for upper level controller of vehicle control systems, it is not appropriate for AWD controllers because the model does not have a direct relationship between the states and control inputs. In this sense, this study adopted a tire force-based full car model for the AWD controller. The clutch engagement force limit, which varies depending on the current engagement state, was calculated systematically by considering changeable dynamic behavior that is inherent in AWD vehicle and was used as a valid value for input limit in linear quadratic regulator (LQR) control algorithm. For the real-time application of the designed controller, only data obtained from the controller area network (CAN) of production vehicles were used. Then, the performance of the proposed controller was validated through vehicle experiments that included scenarios of both longitudinal and lateral movements.

A Robust Traction Control Platform for AWD Vehicles With AWD TMU and eLSD

This paper addresses the traction control of All-Wheel-Drive (AWD) vehicle equipped with AWD Torque Management Unit (TMU), Traction Control System (TCS) and electronic Limited Slip Differential (eLSD). In the existing schemes, they are separately designed, ineffectiveness of control coupling exists, and the integration of them is time consuming and hard to achieve at a good driving performance such as good acceleration and high driving energy efficiency. To avoid such problems, a novel, effective and robust traction control platform is proposed. A decoupling method is adopted, which is achieved by presetting slip ratio thresholds and axle differential speed difference. State machines are designed according to preset high and low thresholds to select among integral increasing control, integral decreasing control, or keeping control. It has no vehicle parameters, no variants hard to measure, and no road identification. The design only counts tire slip ratios, wheel speeds and vehicle speed. So, it's one-platform-fits-all, including the vehicle designs, the road types, and so on. This design has been shown by results of high-fidelity simulations that the proposed design improves energy-economy, driving, handling and stability. Also, the design is shown to be effective, stable and robust.

Efficient Flywheel-Based All-Wheel-Drive Electric Powertrain

All-wheel-drive (AWD) multimotor electric powertrains offer greater potential for system performance, efficiency, and reliability improvements. In this article, a new AWD electric powertrain that can increase the overall powertrain efficiency and battery lifetime is introduced. The proposed powertrain is based on a compact and efficient flywheel-based kinetic energy recovery system (KERS) that overcomes most of the shortcomings of the conventional electric KERS. Here, a significant part of the mechanical power transfer takes place without conversion to electrical power. Therefore, a selectable fraction of the recoverable kinetic energy is processed by power electronics. Thus, no energy exchange is necessary between the battery and the powertrain during acceleration and deceleration, resulting in a reduced battery power rating. This article compares the proposed powertrain with a conventional AWD electric powertrain. Mathematical modeling and simulations based on the space vector method were used for both powertrains that show the advantage of the proposed powertrain over the conventional one. The experimental data are also presented from our proof of the concept laboratory setup.

Development and Verification of a Simulation Model for 120 kW Class Electric AWD (All-Wheel-Drive) Tractor during Driving Operation

This study was conducted to develop a simulation model of a 120 kW class electric all-wheel-drive (AWD) tractor and verify the model by comparing the measurement and simulation results. The platform was developed based on the power transmission system, including batteries, electric motors, reducers, wheels, and a charging system composed of a generator, an AC/DC converter, and chargers on each axle. The data measurement system was installed on the platform, consisting of an analog (current) and a digital part (rotational speed of electric motors and voltage and SOC (state of charge) level of batteries) by a CAN (controller area network) bus. The axle torque was calculated using the current and torque curves of the electric motor. The simulation model was developed by 1D simulation software and used axle torque and vehicle velocity data to create the simulation conditions. To compare the results of the simulation, a driving test using the platform was performed at a ground speed of 10 km/h in off- and on-road conditions. The similarities between the results were analyzed using statistical software and we found no significant difference in axle torque data. The simulation model was considered to be highly reliable given the change rate and average value of the SOC level. Using the simulation model, the workable time of driving operation was estimated to be about six hours and the workable time of plow tillage was estimated to be about 2.4 h. The results showed that the capacity of the battery is slightly low for plow tillage. However, in future studies, the electric AWD tractor performance could be improved through battery optimization through simulation under various conditions.

Longitudinal Vehicle Dynamics Modeling for AWD/4WD Vehicles to Study Torque Split between Front and Rear Axles

<div class="section abstract"><div class="htmlview paragraph">All-wheel Drive (AWD) is a mature technology and most automobile manufacturers offer this feature on their vehicles. Improved traction, enhanced vehicle stability, and better handling are some of the key characteristics of AWD vehicles which are achieved by distributing the appropriate level of torque to the front and rear axles. Accurately capturing the torque split between the two axles is essential for sizing of driveline components like gears, bearings, and shafts. Traditionally, the torque split is considered to be either 50-50%, or solely proportional to the static weight distribution between the two axles. Design decisions are made based on historical test data. In this paper a longitudinal vehicle dynamics model for AWD systems is proposed to understand the influence of various key factors such as dynamic weight transfer, compliance of driveline components, and changing tire radius on the torque split. Comparison with the test data is performed to understand the significance and limitations of the model. While more detailed models have been built and used for various purposes, to the authors’ knowledge, this is the first time a model with this level of fidelity is being used to evaluate driveline component durability.</div></div>

Model Predictive Control of an All-Wheel Drive Vehicle Considering Input and State Constraints

This paper suggests a novel model-based control scheme for an all-wheel drive (AWD) vehicle. Using transfer case, AWD system is able to transfer the engine torque to all the wheels mechanically, helping the vehicle to maintain traction on a slippery road or to climb a hill. Recently, an active type AWD system that is able to control the torque transmitted through transfer case continuously by the aid of electronic clutch actuator has become popular. In order to realize preemptive operation which is necessary property for AWD system while simultaneously considering states and input constraints which is originated from mechanical and physical characteristics, this paper adopted model predictive control (MPC) concept. The proposed MPC-based controller was designed by using planar full-car model that is based on tire force and motion states, which represents the direct relationship between these states and control input. Therefore, the planar full-car model contributed the suggested controller to follow standard MPC design process. The target states of longitudinal wheel slip and yaw rate, which have commonly been adopted in previous studies, were selected for practical concerns. The advantages of the suggested MPC-based controller that is designed especially for an AWD system were validated by conducting simulations through a SimulinkCarSim-based AWD vehicle model.

Towards an All-Wheel Drive Motorcycle: Dynamic Modeling and Simulation

Modern motorcycles are evolving more and more towards complex systems by the increasing integration of mechanical, electrical and control disciplines. All-wheel drive (AWD) vehicles have proven effective to improve vehicle's performances and rider's safety. Despite this, manufacturers have developed few AWD motorcycles and little research has been devoted to them. Obvious difficulties concern torque distribution to the front wheel because of steering system. Nowadays, the integration of technologies eases the implementation of front wheel drive opening new research perspectives. In this work, the dynamic model of an AWD motorcycle with an attached rider is proposed. It represents the first symbolic analysis investigating the effects of front wheel traction on the dynamics of a motorcycle for supporting the design of AWD motorcycles reducing trials and tests on prototypes. The proposed model is parametric with respect to the motorcycle geometry, and it allows to simulate complex operating modes of the AWD, such as cornering phenomena, taking into account coupling of lateral and longitudinal dynamics and tire-road interactions. Unlike other works, here the authors include a full tire model by exploiting theoretical slips of the brush model for tire's aligning moment too, instead of applying a totally empirical representation less suitable for a complete symbolic description. Besides, to simulate the equations of motion, the benefits and disadvantages of using AWD with torque distribution have been pointed out introducing a new handling ratio. Two verification procedures validate the model: one is performed theoretically, the other carries out a comparison with a multibody software, whose model is more sophisticated, this latter embeds all main motorcycle's dynamics. Although radically different, being the first theoretical and the second numerical-computational, both methods exhibit consistent behavior between them, and effectiveness of the former is also consistent with the results of a multibody simulator under the assumptions made.

Robust Differential Steering Control System for an Independent Four Wheel Drive Electric Vehicle

This research investigates a robust differential steering control system (DSCS) for an independent four wheel drive electric vehicle (EV). The DSCS will maneuver the independently actuated (IA) four wheel drive EV without the help of any conventional steering mechanism (CSM) via the input torque of the four wheels. The differential angular rotation speed between left and right wheels is used to generate the CSM effects. The DSCS is designed using the linear model of the vehicle with linear tire dynamics and is tested in simulations using a nonlinear vehicle model with nonlinear tire dynamics. The proposed DSCS is a combination of forward speed and yaw rate controllers, designed using the robust H∞ control methodology. The effectiveness of the proposed robust controller is analyzed by comparing the performance of the all-wheel drive (AWD), the rear wheel drive (RWD), and the front wheel drive (FWD) vehicles during simulations. The simulations results indicates that the proposed system can successfully maneuver the vehicle under different driving conditions by tracking the desired parameters without the use of any CSM.