A processor implemented method for modeling a neuronal controller exhibiting human postural sway, the method comprising the steps of: modeling, by one or more hardware processors, the neuronal controller in the form of a Reinforcement Learning agent based on an inverted pendulum with 1 Degree Of Freedom representing a first mechanical model of a human body in the form of a dynamical system, wherein the RL agent is trained using a Q-learning technique to learn an optimal state-action value (Q-value) function for a tuneable Markov Decision Process model, wherein the modeled neuronal controller is configured to reproduce Center Of Pressure characteristics in the human body either along a sagittal plane or along a frontal plane separately; deriving, by the one or more hardware processors, dynamical equations of a Spherical Inverted Pendulum with respect to a global coordinate system, wherein the SIP represents a second mechanical model of the human body that exhibits postural sway along both the frontal plane and the sagittal plane together, wherein the dynamical equations are derived by using Lagrange's equations with two independent state variables ( θx and θy) being angular deviation of the SIP about a pivot joint and along x and y axes respectively of the global coordinate system, wherein the pivot j oint characterizes an ankle joint of the human body; and modeling, by the one or more hardware processors, the human postural sway both along the sagittal plane and along the frontal plane together using the modeled neuronal controller and the derived dynamical equations of the SIP by tuning (i) a reward function comprised in the modeled neuronal controller and (ii) a set of parameters to balance the SIP such that the postural sway characteristics generated by the modeled neuronal controller match the postural sway characteristics of one or more control subjects, wherein the set of parameters include parameters of the MDP model and parameters associated with physiology of the human body, wherein the step of modeling a neuronal controller comprises reproducing the Center Of Pressure characteristics in the human body either along the sagittal or along the frontal plane separately in accordance with a dynamical equation based on overall mass of the human body being concentrated at the point of Center Of Mass located at the 2 nd lumbar vertebrae of the human body, an approximate height of the 2 nd lumbar vertebrae, moment of inertia of the inverted pendulum, angle of the inverted pendulum with respect to the direction of gravity, gravitational acceleration, a stiffness constant and a damping constant, wherein the stiffness constant and the damping constant denote pivot joint properties of the inverted pendulum. modeling, by one or more hardware processors, the neuronal controller in the form of a Reinforcement Learning agent based on an inverted pendulum with 1 Degree Of Freedom representing a first mechanical model of a human body in the form of a dynamical system, wherein the RL agent is trained using a Q-learning technique to learn an optimal state-action value (Q-value) function for a tuneable Markov Decision Process model, wherein the modeled neuronal controller is configured to reproduce Center Of Pressure characteristics in the human body either along a sagittal plane or along a frontal plane separately; deriving, by the one or more hardware processors, dynamical equations of a Spherical Inverted Pendulum with respect to a global coordinate system, wherein the SIP represents a second mechanical model of the human body that exhibits postural sway along both the frontal plane and the sagittal plane together, wherein the dynamical equations are derived by using Lagrange's equations with two independent state variables ( θx and θy) being angular deviation of the SIP about a pivot joint and along x and y axes respectively of the global coordinate system, wherein the pivot j oint characterizes an ankle joint of the human body; and modeling, by the one or more hardware processors, the human postural sway both along the sagittal plane and along the frontal plane together using the modeled neuronal controller and the derived dynamical equations of the SIP by tuning (i) a reward function comprised in the modeled neuronal controller and (ii) a set of parameters to balance the SIP such that the postural sway characteristics generated by the modeled neuronal controller match the postural sway characteristics of one or more control subjects, wherein the set of parameters include parameters of the MDP model and parameters associated with physiology of the human body, wherein the step of modeling a neuronal controller comprises reproducing the Center Of Pressure characteristics in the human body either along the sagittal or along the frontal plane separately in accordance with a dynamical equation based on overall mass of the human body being concentrated at the point of Center Of Mass located at the 2 nd lumbar vertebrae of the human body, an approximate height of the 2 nd lumbar vertebrae, moment of inertia of the inverted pendulum, angle of the inverted pendulum with respect to the direction of gravity, gravitational acceleration, a stiffness constant and a damping constant, wherein the stiffness constant and the damping constant denote pivot joint properties of the inverted pendulum.