## 1 Introduction
Hence, the bond graph technique was used in this study for the modeling of exoskeleton for rehabilitation. Similar studies in which a bond graph is used to model a powered exoskeleton for use as an industrial backpack and a knee joint exoskeleton are presented in [17; 18]. It is worth mentioning that rehabilitation in the form of locomotion assistance and gait training through robotic exoskeletons requires both precision and accuracy. The essential challenge for this is to ensure robust tracking of the desired trajectory of locomotion. This desired trajectory is basically a clinical gait pattern provided by the physician, and the exoskeleton as a rehabilitation equipment is used and controlled to ensure that the patients repetitively follow the pattern to train or exercise the muscles. The control strategies used in the literature for trajectory tracking are designed either using the model of the system [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33], the approximated function [34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44], or the combination of both [6]. Toensure tracking, various types of controllers are used. In some cases, a single control action is enough to fulfil the required task but, in other cases, a combination of one or more controllers is required. Trajectory tracking through impedance/admittance control is also carried out [19; 20; 21; 22] where the impedance between the exoskeleton and the human leg is controlled by changing the force applied by the actuator. MB and MF trajectory tracking control strategies are also used to achieve reference signal tracking in exoskeletons, with high stability and performance. In [23], a MB sliding mode controller is designed. The results show that all three joints reach the desired set angles in simulation but in experimentation the hip and knee joints do not reach the final value. Similarly, a pneumatic muscle-based two-DOF ES is controlled by proportional-derivative (PD) and adaptive control strategies separately in [7]. The results show that the output is slightly out of phase for PD control but for adaptive control the tracking results show adaptation within 1.5 s of operation. There exists an initial error of 1 for both the joint angles. Trajectory tracking through adaptive sliding mode impedance control is implemented in [24]. The results show better tracking for the knee joint but for the hip joint there is a delay in phase. The work presented in [45] focuses on achieving system robustness and dynamic performance similar to that of this research, for a 2-DOF LLE. An MB robust sliding mode controller is designed to achieve the desired output. A new classical PD and PD-particle swarm optimization augmented force control (PSOAFC) approach is implemented in [25]. There exists phase delay in joint angles in the case of a time-varying signal. However, for a simple model with an external disturbance torque vector, PD-PSOAFC shows better results than classical PD. In other research works for passive rehabilitation, the MB controllers designed for trajectory tracking include an adaptive admittance control for unstructured uncertainty compensation [26], a sliding mode controller for robustness [28; 31], and an intelligent adaptive fuzzy approach [33]. In a model-free control strategy, however, the mathematical model is not derived, and the system is presented in the form of a function. This function is approximated using techniques such as system identification and artificial intelligence. Model-free control strategies for efficient tracking performance have been designed in various studies, most of which used neural networks to approximate the function of the system and the control law design. The MF controllers designed for passive rehabilitation include an intelligent adaptive controller [34; 41; 42], an intelligent robust controller [35; 40], and a second-order robust sliding mode controller [39]. These MB and MF trajectory tracking control strategies perform well.