## 1 Introduction  
Natural mobility hindrance for human beings is a critical problem as it causes dependency and poor quality of life. This hindrance is caused by physiological (muscular or skeletal) or neurological disorders or disabilities (stroke, spinal cord injuries etc.) [1]. In recent years, the use of robotic exoskeletons in the medical field for lower limb rehabilitation has gained much importance throughout the world. This is mainly because of two reasons: firstly, unlike passive conventional mobility aids (such as crutches, manual wheelchairs, and walkers etc. ), exoskeletons are a source of active anthropomorphic mobility. Secondly, exoskeletons support rehabilitation in the form of locomotion assistance and gait training. In both forms, exoskeletons are proven to be more beneficial and effective in terms of comfort and results, as compared to conventional assistance and manual training [2; 3]. For these reasons, more research has been carried out in the field of rehabilitation exoskeletons and numerous multidisciplinary (electrically, hydraulically, or pneumatically powered) exoskeletons have been designed [1; 4; 5]. Whether designed for active or passive rehabilitation, a state-of-the-art mechanical design requires an effective control strategy to provide desired performance. Active rehabilitation refers to repetitive movements/exercises through exoskeletons with assist-when-required algorithms, either integrated with trajectory tracking or repetitive learning. This ensures active participation of the patient, whereas passive rehabilitation involves reference/trajectory tracking control of the robotic exoskeleton, i.e., the control law is designed to ensure proper tracking of a pre-defined reference/trajectory. The patient forcibly receives training. Passive rehabilitation training mostly uses trajectory tracking, whereas the active rehabilitation training employs impedance control with assist-when-required algorithms. This research focuses on passive rehabilitation through exoskeletons and provides control strategies that ensure trajectory tracking over a predefined gait pattern. The reference trajectory tracking controllers are broadly categorized as model-based (MB) and model-free (MF) controllers. A model-based control strategy requires mathematical modeling of the system; however, a model-free controller does not need an exact mathematical model, but a system function is approximated. The lower limb exoskeletons are modeled as a two link [6; 7] or three link [8; 9] manipulator. In most of the previous research, the Lagrange method is used for development of the mathematical model of system dynamics. This method uses the difference between the kinetic and potential energies acting on the links, and derives a dynamics model of the system in the form of a relationship between the associated force/torque and the generalized coordinates. The standard form of dynamics of a mechanism is expressed as Equation (1):  
\[\tau=M(q)\ddot{q}+C\big{(}q,\dot{q}\big{)}+G(q) \tag{1}\]  
where \(\tau\in R^{n}\) is the applied torque, \(M(q)\in R^{n}\) is the inertia matrix, \(C\big{(}q,\dot{q}\big{)}\in R^{n}\) represents Coriolis and centrifugal force, \(G(q)\in R^{n}\) is the gravitational force, and \(q\in R^{n}\) denotes the angle. Equation (1) can be modified with addition of external disturbances 'd' (refers to the interference that becomes part of the system exogenously) at the system input [10] and model uncertainties '\(\Delta\)' (refers to difference between the actual system and the modeled system) [11; 12]. The resulting modification is shown in Equation (2):  
\[\tau=M(q)\ddot{q}+C\big{(}q,\dot{q}\big{)}+G(q)+\Delta+d \tag{2}\]  
The Lagrangian approach in the case of systems having complex dynamic characteristics requires more computations. An alternate approach for modeling the manipulators is the bond graph. The bond graph technique provides a model of the system with less complexity and more flexibility [13; 14], as compared to the conventional Lagrangian method [15; 16].