Claims: 1. A processor implemented method, comprising: inputting (302), via the one or more hardware processors, one or more time synchronized command input signals to a moving differential drive vehicle; determining (304), based on the one or more time synchronized command input signals, a corresponding change in one or more motion parameters of the moving differential drive vehicle; training (306) a non-linear model using the one or more time synchronized command input signals and the determined corresponding change in the one or more motion parameters; generating (308), using the trained non-linear model, an experimental model by computing a mapping function between the one or more time synchronized command input signals and the one or more motion parameters of the moving differential drive vehicle; estimating (310), using the generated experimental model, value of the one or more motion parameters for one or more incoming time synchronized command input signals; performing (312), using a plurality of data obtained from one or more sensors, a validation of the estimated value of the one or more motion parameters; and adaptively updating (314) the experimental model to estimate an updated value of the one or more motion parameters based on the validation. 2. The processor implemented method as claimed in claim 1, wherein the one or more sensors used for validation include position sensors and velocity sensors. 3. The processor implemented method as claimed in claim 1, wherein the validation is performed by computing an error between the plurality of data obtained from the one or more sensors and measurements of one or more motion parameters obtained from a ground truth system. 4. The processor implemented method as claimed in claim 1, wherein the step of adaptively updating the experimental model is performed using the computed error and a gain parameter. 5. The processor implemented method as claimed in claim 4, wherein the step of adaptively updating the experimental model reduces inaccuracy introduced due to one or more factors. 6. A system (104), comprising: a memory (202); one or more communication interfaces (204); and one or more hardware processors (206) coupled to said memory (202) through said one or more communication interfaces (204), wherein said one or more hardware processors (206) are configured to: input one or more time synchronized command input signals to a moving differential drive vehicle; determine, based on the one or more time synchronized command input signals, a corresponding change in one or more motion parameters of the moving differential drive vehicle; train a non-linear model using the one or more time synchronized command input signals and the determined corresponding change in the one or more motion parameters; generate, using the trained non-linear model, an experimental model by computing a mapping function between the one or more time synchronized command input signals and the one or more motion parameters of the moving differential drive vehicle; estimate, using the generated experimental model, value of the one or more motion parameters for one or more incoming time synchronized command input signals; perform, using a plurality of data obtained from one or more sensors, a validation of the estimated value of the one or more motion parameters; and adaptively update the experimental model to estimate an updated value of the one or more motion parameters based on the validation. 7. The system as claimed in claim 6, wherein the one or more sensors used for validation include position sensors and velocity sensors. 8. The system as claimed in claim 6, wherein the validation is performed by computing an error between the plurality of data obtained from the one or more sensors and measurements of one or more motion parameters obtained from a ground truth system. 9. The system as claimed in claim 6, wherein the step of adaptively updating the experimental model is performed using the computed error and a gain parameter. 10. The system as claimed in claim 9, wherein the step of adaptively updating the experimental model reduces inaccuracy introduced due to one or more factors. , Description:FORM 2 THE PATENTS ACT, 1970 (39 of 1970) & THE PATENT RULES, 2003 COMPLETE SPECIFICATION (See Section 10 and Rule 13) Title of invention: CONTROL COMMAND BASED ADAPTIVE SYSTEM AND METHOD FOR ESTIMATING MOTION PARAMETERS OF DIFFERENTIAL DRIVE VEHICLES Applicant: Tata Consultancy Services Limited A company Incorporated in India under the Companies Act, 1956 Having address: Nirmal Building, 9th Floor, Nariman Point, Mumbai 400021, Maharashtra, India The following specification particularly describes the invention and the manner in which it is to be performed. TECHNICAL FIELD The disclosure herein generally relates to field of estimating motion parameters, more particularly, to control command based adaptive system and method for estimating motion parameters of differential drive vehicles. BACKGROUND Differential drive vehicles as autonomous mobile robots have gained significant interests due to their applicability to various scenarios, such as surveillance and reconnaissance, search and rescue, disaster management, and industrial warehouses. Consequently, various technological aspects such as indoor/outdoor localization, motion planning and control of such autonomous robots are required to be analyzed. Localization which refers to position estimation with respect to some inertial frame and local pose tracking are among various key issues in mobile robotics. Conventional localization methods fuse measurements from exteroceptive sensors such as GPS, camera, LIDAR with measurements from interoceptive sensors such as wheel encoder, accelerometer, and gyroscopes to get position and orientation of autonomous mobile-wheeled robots. However, conventional methods depending on exteroceptive sensors only for localization suffer degradation of localization accuracy or even loss of localization. For example, frequent disruption may occur when robot moves in GPS denied areas. Few conventional methods use an early-extended Kalman filter based localization framework, which uses odometer for position estimation and differential GPS for measurement update. Further, other conventional methods use low-cost localization scheme using a Kalman filter (KF) that fuses measurements from GPS, low cost inertial sensors, and wheel encoders. However, during GPS outages, the KF uses velocity updates from wheel encoders for reducing localization errors. SUMMARY Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one aspect, there is provided a processor implemented method, comprising: inputting, via the one or more hardware processors, one or more time synchronized command input signals to a moving differential drive vehicle; determining, based on the one or more time synchronized command input signals, a corresponding change in one or more motion parameters of the moving differential drive vehicle; training a non-linear model using the one or more time synchronized command input signals and the determined corresponding change in the one or more motion parameters; generating, using the trained non-linear model, an experimental model by computing a mapping function between the one or more time synchronized command input signals and the one or more motion parameters of the moving differential drive vehicle; estimating, using the generated experimental model, value of the one or more motion parameters for one or more incoming time synchronized command input signals; performing, using a plurality of data obtained from one or more sensors, a validation of the estimated value of the one or more motion parameters. In an embodiment, the one or more sensors used for validation include position sensors and velocity sensors. In an embodiment, the validation is performed by computing an error between the plurality of data obtained from the one or more sensors and measurements of one or more motion parameters obtained from a ground truth system. In an embodiment, the method further comprising adaptively updating the experimental model to estimate an updated value of the one or more motion parameters based on the validation. In an embodiment, the step of adaptively updating the experimental model is performed using the computed error and a gain parameter and reduces inaccuracy introduced due to one or more factors. In another aspect, there is provided a system comprising: a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory through the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to: input, via the one or more hardware processors, one or more time synchronized command input signals to a moving differential drive vehicle; determine, based on the one or more time synchronized command input signals, a corresponding change in one or more motion parameters of the moving differential drive vehicle; train a non-linear model using the one or more time synchronized command input signals and the determined corresponding change in the one or more motion parameters; generate, using the trained non-linear model, an experimental model by computing a mapping function between the one or more time synchronized command input signals and the one or more motion parameters of the moving differential drive vehicle; estimate, using the generated experimental model, value of the one or more motion parameters for one or more incoming time synchronized command input signals; perform, using a plurality of data obtained from one or more sensors, a validation of the estimated value of the one or more motion parameters. In an embodiment, the one or more sensors used for validation include position sensors and velocity sensors. In an embodiment, the validation is performed by computing an error between the plurality of data obtained from the one or more sensors and measurements of one or more motion parameters obtained from a ground truth system. In an embodiment, the one or more hardware processors are further configured by the instructions to adaptively update the experimental model to estimate an updated value of the one or more motion parameters based on the validation. In an embodiment, the step of adaptively updating the experimental model is performed using the computed error and a gain parameter and reduces inaccuracy introduced due to one or more factors. In yet another aspect, there are provided one or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause comprising: inputting, via the one or more hardware processors, one or more time synchronized command input signals to a moving differential drive vehicle; determining, based on the one or more time synchronized command input signals, a corresponding change in one or more motion parameters of the moving differential drive vehicle; training a non-linear model using the one or more time synchronized command input signals and the determined corresponding change in the one or more motion parameters; generating, using the trained non-linear model, an experimental model by computing a mapping function between the one or more time synchronized command input signals and the one or more motion parameters of the moving differential drive vehicle; estimating, using the generated experimental model, value of the one or more motion parameters for one or more incoming time synchronized command input signals; performing, using a plurality of data obtained from one or more sensors, a validation of the estimated value of the one or more motion parameters. In an embodiment, the one or more sensors used for validation include position sensors and velocity sensors. In an embodiment, the validation is performed by computing an error between the plurality of data obtained from the one or more sensors and measurements of one or more motion parameters obtained from a ground truth system. In an embodiment, the instructions may further cause adaptively updating the experimental model to estimate an updated value of the one or more motion parameters based on the validation. In an embodiment, the step of adaptively updating the experimental model is performed using the computed error and a gain parameter and reduces inaccuracy introduced due to one or more factors. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles: FIG. 1 illustrates an exemplary robot network environment for implementing control command based adaptive system and method for estimating motion parameters of differential drive vehicles, in accordance with an embodiment of present disclosure. FIG. 2 illustrates a block diagram of the system of FIG. 1 for implementing control command based adaptive method for estimating motion parameters of differential drive vehicles, according to some embodiments of the present disclosure. FIG. 3 illustrates an exemplary flow diagram of a processor implemented control command based adaptive method for estimating motion parameters of differential drive vehicles, in accordance with some embodiments of the present disclosure. FIG. 4 illustrates a diagram characterizing motion of the differential drive vehicle in 2D space, in accordance with some embodiments of the present disclosure. FIG. 5 illustrates a functional block diagram representing the Hammerstein-Wiener model for estimating motion parameter of the differential drive vehicle using control command, in accordance with some embodiments of the present disclosure. FIGS. 6A through 6C show graphs depicting outputs of different blocks of the Hammerstein-Weiner model for different terrains, in accordance with some embodiments of the present disclosure. FIG. 7 illustrates a functional block diagram of an extended kalman filter (EKF) framework for estimation of the one or motion parameters in real time, in accordance with some embodiments of the present disclosure. FIGS. 8A through 8R show graphs illustrating experimental results for control command based adaptive system and method for estimating motion parameters of differential drive vehicles, in accordance with some embodiments of the present disclosure. DETAILED DESCRIPTION OF EMBODIMENTS Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope being indicated by the following claims. The embodiments herein provide control command based adaptive system and method for estimating motion parameters of differential drive vehicles. The typical interpretation of results obtained from conventional motion parameter estimation methods has been modified to solve a problem of accurate localization of vehicles and/or robots using only interceptive sensors and adapting to challenging scenarios such as presence of different terrains, change in payload of the differential vehicle, friction, and the like. The method of present disclosure utilizes a plurality of time synchronized command input signals provided to mobile robot of the differential drive vehicle for estimation of velocity thereof. Further, an experimental model has been generated, which provides relationship between the plurality of time synchronized command input signals and the velocity of the differential drive vehicle. The generated experimental model was adaptively updated to compute one or more motion parameters of the differential drive vehicle in real time, by fusing information from a plurality of sensors, the plurality of time synchronized command input signals and orientation information received from an onboard Inertial Measurement Unit (IMU). The system and method of proposed disclosure accurately estimate one or more motion parameters of the differential drive vehicle for localization with high accuracy and reduced dependency on extroceptive sensors. Referring now to the drawings, and more particularly to FIG. 1 through FIG. 8R, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method. FIG. 1 illustrates an exemplary robot network environment 100 with a system 104 for implementing control command based adaptive system and method for estimating motion parameters of differential drive vehicles, in accordance with an embodiment of present disclosure. The robot network environment 100 utilizes a plurality of sensors 106 and the system 104 for estimating motion parameters of differential drive vehicle 102. The differential drive vehicle 102 is an autonomous four wheeled mobile robot which moves in the robot network environment. The robot network environment includes an indoor network area as well as outdoor network area where the differential drive vehicle moves on different surface terrains. In an embodiment, the system 104 can be implemented as a unit within the differential drive vehicle 102. In an embodiment, the system 104 can be implemented as a stand-alone unit separated from the differential drive vehicle 102. In an embodiment, the plurality of sensors 106 may reside in the system 104 and/or may act as standalone unit. The system 104 is configured to process and analyze the data received from the plurality of sensors 106, and differential drive vehicle 102 for estimating motion parameters using control command input based adaptive methods. The system 104 is configured to process and analyze the received data in accordance with a plurality of models, further explained in conjunction with FIG. 2 through FIG. 5, and FIG. 7. FIG. 2 illustrates a block diagram of the system 104 of FIG. 1 for control command based adaptive system and method for estimating motion parameters of differential drive vehicles in the robot network environment 100 of FIG. 1, according to some embodiments of the present disclosure. In an embodiment, the system 104 includes or is otherwise in communication with one or more hardware processors such as a processor 206, an Input/Output interface 204, and at least one memory such as a memory 202. The processor 206, the I/O interface 204, and the memory 202, may be coupled by a system bus (not shown in FIG. 2). The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The interfaces 204 may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, a camera device, and a printer. The interfaces 204 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. For the purpose, the interfaces 204 may include one or more ports for connecting a number of computing systems with one another or to another server computer. The I/O interface 204 may include one or more ports for connecting a number of devices to one another or to another server. The hardware processor 206 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, combinational circuits, application specific integrated circuits, semiconductor devices, logic circuitries including switches, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the hardware processor 206 is configured to fetch and execute computer-readable instructions stored in the memory 202. The memory 202 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, the memory 202 includes a data repository 210 for storing data processed, received, and generated as output(s) by the system 104. The memory 202 stores a plurality of models such as known non-linear model, generated experimental model (alternatively referred as offline models) and adaptively updated models (alternatively referred as online models) which are further used for estimating motion parameters of differential drive vehicles in the robot network environment 100. The plurality of models stored in the memory 202 may include routines, programs, objects, components, data structures, and so on, which perform particular tasks or implement particular (abstract) data types. The data repository 210, amongst other things, includes a system database and other data. The system database may store information but are not limited to, a plurality of parameters obtained from the plurality of sensors, wherein the parameters are specific to an entity (e.g., machine such as robot, vehicle, and the like). The plurality of parameters may comprise sensor data captured through the plurality of sensors connected to the machine. Further, the database 208 stores information pertaining to inputs fed to the system 104 and/or outputs generated by the system (e.g., at each stage), specific to the methodology described herein. More specifically, the system database stores information being processed at each step of the proposed methodology. The other data may include, data generated as a result of the execution of the plurality of models stored in the memory 202. The generated data may be further learnt to provide improved learning of the plurality of models in the next iterations to output desired results with improved accuracy. In an embodiment, the one or more hardware processors 206 can be configured to perform control command based adaptive method for estimating motion parameters of differential drive vehicles which can be carried out by using methodology, described in conjunction with FIG. 3, FIG. 4, and use case examples. FIG. 3 illustrate an exemplary flow diagram of a processor implemented method 300, implemented by the system 104 of FIG. 1 and FIG. 2 for implementing control command based adaptive method for estimating motion parameters of differential drive vehicles, in accordance with some embodiments of the present disclosure. Referring to FIG. 3, at step 302 of the present disclosure, the one or more hardware processors 206 input one or more time synchronized command input signals to a moving differential drive vehicle. Here, the one or more time synchronized command input signals correspond to giving control commands for different movements of the moving autonomous vehicle. For example, the time synchronized command input signals could be but not limited to a command for forward motion of vehicle, command for rotation of vehicle and the like. In an embodiment, the time synchronized command input signals could be but not limited to a step signal, a ramp signal, an impulse signal, and the like. In an embodiment, the differential drive vehicle is an autonomous robot having four wheels in which rotation is achieved by a differential push on wheel pairs at opposite sides. FIG. 4 illustrates a diagram characterizing motion of the differential drive vehicle in 2D space, in accordance with some embodiments of the present disclosure. To characterize the differential drive vehicle’s (hereby referred as vehicle throughout the description) motion in 2D space, a reference frame (alternatively referred as inertial frame) and a vehicle’s body frame are defined. As can be seen in FIG. 4, the reference frame is defined as {F_(1 )= X_1,Y_(1 ) } the vehicle’s body frame is defined as {F_(B )= X_B,Y_(B ) }. In an embodiment, origin of the vehicle’s body frame is fixed to vehicle’s centre of mass, and it’s axes (X_B,Y_(B )) are always aligned to vehicle’s front and left directions. Thus, in space, the reference frame is fixed, whereas the vehicle’s body frame moves around the reference frame. Further, using a plurality of defined reference frames, unicycle kinematics for the vehicle can be expressed as provided below: [¦((p_x ) ?@(p_y ) ?@? ? )]= [¦(cos??&0@sin??&0@0&1) ][¦(v@?)]= [¦(v_x@v_y@?)] (1) Here, [p_x p_y ]^Tand ? represent translation and rotation of the vehicle’s body frame F_(B )with respect to the reference frame F_(1 )expressed in F_(1 )respectively. Further, v represents forward velocity of the vehicle’s body frame F_(B ) with respect to the reference frame F_(1 ) expressed in F_(B ). Also, [v_x v_y ]^Tand ? represent linear and angular velocities of the vehicle’s body frame F_(B )with respect to the reference frame F_(1 )expressed in F_(1 )respectively. In an embodiment, equation (i) helps in determining how vehicle states represented by ([p_(x ) p_y ?]^T ) evolves over time when linear and angular velocities ([v_x v_y ]^T ) are known. Further, ? and ? are estimated using a low cost 9-Degrees of freedom (Dof) inertial measurement unit (IMU) which provides information about linear accelerations, angular rates and magnetometer readings. Further, as depicted in step 304 of FIG. 3, the one or more hardware processors 206 determine a corresponding change in one or more motion parameters of the moving differential drive vehicle based on the one or more time synchronized command input signals. In an embodiment, the one or more motion parameters could be but not limited to forward velocity, angle of rotation, change in position of the vehicle, and the like. For example, initially, a first command input signal c_1 is applied as step signal for forward motion of the vehicle, and a second command input signal for rotation of the vehicle is considered zero. Then, in such a case, a corresponding change in the value of forward velocity and position is determined. In an embodiment, the forward velocity v is computed by sequentially differentiating position estimates of the vehicle obtained using a known in the art ground truth system. In an embodiment, the ground truth system is created to store a range of values specific to the one or more motion parameters of the differential vehicle, wherein the range of values specific to the one or more motion parameters are obtained by using a set of cameras device in real time. In an embodiment, the set of cameras may include but not limited to a monocular camera, a RGB-depth camera, an infrared camera, and a stereo-vision camera. In an embodiment, there exists some infrared/optical markers on the vehicle and the ground truth system provides real time estimates of actual value of the one or more motion parameters based on the infrared/optical markers. Further, at step 306 of FIG. 3, the one or more hardware processors train a non-linear model by using the one or more time synchronized command input signals and the determined corresponding change in the one or more motion parameters. Further, as depicted in step 308 of FIG. 3, the one or more hardware processors 206 generate, using the trained non-linear model, an experimental model by computing a mapping function between the one or more time synchronized command input signals and the one or more motion parameters of the differential drive vehicle. In an embodiment, the non-linear model (alternatively referred as an offline model and may be interchangeably used herein after) could be but not limited to a Hammerstein wiener model. FIG. 5 illustrates a functional block diagram representing the Hammerstein-Wiener model for estimating motion parameters of the differential drive vehicle using control command, in accordance with some embodiments of the present disclosure. The Hammerstein-Wiener model describes a dynamic system using dynamic linear block in series with static nonlinear blocks. As can be seen in FIG. 5, the Hammerstein-Wiener model comprises an input non-linearity block, a linear block, and an output non-linearity block. In an embodiment, the input non-linearity block of the Hammerstein-Wiener model captures nonlinearity present in input data which is referred as input nonlinearity and represented by a non-linear function f. Here, the input data is a unit step time synchronized command input signal represented as u(n)=c_1. The non-linearity function f maps the input data u(n)=c_1 as w(n)=f(c_1 ) where w(n) is an inner variable which represents output of the input non-linearity block and has same dimension as u(n). FIGS. 6A through 6C show graphs depicting outputs of different blocks of the Hammerstein-Weiner model for different terrains, in accordance with some embodiments of the present disclosure. As shown in FIG. 6A, the non-linearity in command input signal c_1 is captured by a piece-wise linear function. Further, using least square method, this piece-wise linear function can be closely expressed by polynomial shown in equation (2) and equation (3) provided below as: f(c_1 )=a_2 c_1 (n^2 )+ a_1 c_1 (n) (2) w(n)=a_2 c_1 (n^2 )+ a_1 c_1 (n) (3) Here, c_1 (n) represents n^thinstant of commanded input c_1 and coefficients a_2, a_1 and a_0 ? R define non-linearity behavior that depend on factors such as terrain, payload, and the like. Further, output of the input non-linearity block is provided as input to the linear block of the Hammerstein-Weiner model. In an embodiment, the linear block of the Hammerstein-Weiner model is a discrete transfer function that represents dynamic component of the experimental model. In the method of present disclosure, the linear block is configured as third order system represented by equation (4) provided below as: T(z)= (b_2 z^(-2)+ b_3 z^(-3))/(d_0+ d_1 z^(-1)+ d_2 z^(-2)+ d_3 z^(-3) ) (4) Here, T(z) represents transfer function expressed in z domain. The transfer function T(z) transforms W(z) to X(z) as shown in equation (5) and (6) provided below as: X(z)= T(z) W(z) (5) X(z)= (b_2 z^(-2)+ b_3 z^(-3))/(d_0+ d_1 z^(-1)+ d_2 z^(-2)+ d_3 z^(-3) ) W(z) (6) Here, W(z) and X(z) represent z transforms of discrete signals w(n) and x. Further, inverse z transform is performed on both sides of equation (6) and output of the linear block, x(n) is expressed in discrete form as shown in equation (7) provided below as x(n)= 1/d_0 [b_2 w(n-2)+b_3 w(n-3)- d_1 x(n-1)- d_2 x(n-2)- d_3 x(n-3)] (7) Here, coefficients b_2, b_3, d_1, d_2, and d_3? R define dynamic response of the Hammerstein-Weiner model to input w(n). Further, values of these coefficients depend on factors such as terrain, payload, and the like. FIG. 6B shows a graph depicting variation in unit step response of the linear block with change in terrain. Further, upon substituting expression of input non-linearity shown in equation (3) in expression for the linear block output x(n), the equation (7) can be rewritten as x(n)= 1/d_0 [b_2 f(c(n-2))+b_3 f(c(n-3))- d_1 x(n-1)- d_2 x(n-2)- d_3 x(n-3)] or x(n)= 1/d_0 [b_2 ?(a?_2 ?(c_1 (n-2)?^2)+a_1 c_1 (n-2))+b_3 ?(a?_2 ?(c_1 (n-3)?^2)+a_1 c_1 (n-3))- d_1 x(n-1)- d_2 x(n-2)- d_3 x(n-3)] or x(n)= K_1 ?(c_1 (n-2))?^2+ K_2 c_1 (n-2))+ K_3 ?(c_1 (n-3))?^2+ K_4 c_1 (n-3))- K_5 x(n-1)- K_6 x(n-2)- K_7 x(n-3) (8) Here, K_1= a_(2b_2 )/d_0 , K_2= a_(1b_2 )/d_0 , K_3= a_(2b_3 )/d_0 , K_4= a_1/(b_3 d_0 ), K_5= -d_1/d_0 , K_6= -d_2/d_0 , K_7=-d_3/d_0 . In an embodiment, knowledge of these seven model parameters defines response of input non-linearity and linear block. Further, the third order system in this case is chosen over second order system due to better accuracy during experiments. In an embodiment, a non-linear function h is used to transform the output of the linear block x(n) to output of the non-linear model y(n)=v as y(n)=h(x(n)). The non-linear function h is referred as output non-linearity since it acts on the output of the linear block. This output non-linearity is configured as a saturation function in the method of proposed disclosure as shown in FIG. 6C to model velocity saturation of the vehicle. The output non-linearity is expressed by equation (9) provided below as: y_n= {¦(x(n) if v_min=x(n)=v_max @v_max if x(n)>v_max @v_min if x(n)