## 1 Introduction  
Population aging impacts mobility and functional abilities, highlighting the need for assistive technologies [1]. Neurological conditions like muscle weakness, spinal cord injury, and stroke further contribute to mobility loss and reduced autonomy [2]. Physical therapy is effective for rehabilitation but is time-consuming and costly, requiring constant medical supervision [3, 4]. Actuated exoskeleton robotics offer a promising alternative to traditional rehabilitation, reducing the need for continuous supervision while effectively aiding patient recovery. Medical and healthcare robots, particularly exoskeletons, represent one of the most promising categories of robotic systems in recent years [2]. Exoskeletons, wearable devices designed to enhance physical capabilities, have shown significant potential in rehabilitation, physical therapy, and assisting individuals with limited mobility,including the elderly [2; 5; 6; 7; 8]. These devices have provided hope in addressing mobility challenges faced by the aging population. Exoskeletons can improve mobility, balance [9], and independence in daily activities. When combined with personalized healthcare, they have the potential to transform elderly care and enhance support for individuals with mobility impairments [10]. A crucial area of focus in the exoskeleton field is the development of specialized exoskeletons for rehabilitation [11; 12], aiming to assist individuals with disabilities [13] and older individuals in terms of mobility and walking improvement [14; 15]. Control strategies are essential to guaranteeing the effective and safe functioning of exoskeletons. Researchers have investigated different control methods, such as classical approaches, model-based control [16], adaptive controller [17], and neural networks, to enhance exoskeleton functionality, adaptability, stability, and user safety [18]. In the context of lower-limb exoskeletons [19], numerous studies have investigated different control strategies [20; 21] for enhancing walking assistance and gait rehabilitation [22]. Among the methods investigated are fuzzy logic, neural networks [23], impedance control, robust control [24], trajectory tracking, and neural networks [25; 26; 27]. The goal is to achieve accurate joint movement, adaptability to user needs, and overall user safety. These diverse control methodologies highlight the importance of creating adaptable and personalized exoskeleton systems that cater to individual user requirements. [PERSON] et al. [28] implemented a sliding mode controller to address the nonlinearities of an electro-hydraulic actuator powering a polycentric one-Degree-of-Freedom (1-DoF) knee exoskeleton for efficient torque transfer and energy use. [PERSON] et al. [29] developed a unilateral knee exoskeleton with a finite state machine-based gait control strategy using IMU and knee angle sensors to detect intentions, applying negative damping for assistive torque. However, they have hardly discussed how to handle system uncertainties. In [30], the authors investigated an affine state-feedback control strategy to ensure robust position control of a knee exoskeleton system. They considered its nonlinear dynamics, accounting for viscous and solid frictions. Conversely, in [31], the authors focused on knee-joint control of the exoskeleton, addressing external disturbances through a combination of linear state-feedback control and nonlinear control law. In another work by [PERSON] et al. [32], an anthropomorphic exoskeleton with a variable instantaneous center of rotation using a 2-DoF five-bar mechanism was controlled using a PID controller, and experiments showed minimal errors and improved comfort for rehabilitation training. This paper focuses on achieving precise position control, with a particular emphasis on the knee joint [30; 31] within a powered lower limb exoskeleton. Due to its critical role in walking and weight-bearing activities, the knee joint holds significant importance.