# Active Gait Retraining with Lower Limb Exoskeleton Based on Robust Force Control
## 1 Introduction  
People have the fundamental ability to walk to get from one place to another. However, losing this ability due to physical or psychological health problems significantly affects human lives [1]. According to the World Health Organization (WHO), spinal cord injuries, neuromuscular diseases, and muscle aging will continue to increase [2]. Traditional rehabilitation methods mainly focus on passive exercises, assisted stretching, and activities supervised by a therapist. Despite the good results obtained, traditional rehabilitation has several disadvantages, including inconsistent intensity, high physical effort required from both patients and therapists, and a lack of adaptability. As a result, research has shifted toward active rehabilitation methods using robotic devices like exoskeletons, for example, systems for assistance and gait retraining, designed to progressively improve therapy outcomes [3]. Gait rehabilitation has been revolutionized by the development of devices such as exoskeletons and advanced rehabilitation systems. These innovations have transformed traditional rehabilitation techniques, making them more comprehensive and effective. This new approach incorporates the use of exoskeletons, enabling patients to actively participate in their rehabilitation process [4]. Commercial exoskeletons like the EXO-H3 [5], Atalante, Ekso [6], and Hank have achieved important advances in gait rehabilitation and have also demonstrated their potential by integrating advanced control algorithms that allow for customized trajectories and speeds based on patient needs. Pattern recognition techniques further enhance theadaptation of the exoskeleton to the patient [7; 8; 9; 10], making rehabilitation more effective for conditions such as hemiplegia and quadriplegia. Understanding movement intention is crucial for making accurate trajectory predictions. This challenge has been effectively addressed using robust control mechanisms. For example, reference [11] discusses the implementation of adaptive sliding mode variable admittance control. Meanwhile, references [12; 13] explore the use of adaptive backstepping sliding mode techniques and hybrid approaches integrating neural networks. Two strategies have shown particular promise in enhancing control stability and adaptability in gait exoskeletons. The first strategy proposes using sliding mode-based observers and neural networks to estimate unmeasured variables, such as joint velocities, and employing iterative learning control (ILC) to progressively adjust trajectories based on previous repetitions. The approach proposed in [14] demonstrates that accurate trajectory tracking can be achieved without velocity measurements using only a neural network-based observer and sliding mode control to estimate and compensate for uncertainties in the system. Applying this strategy to exoskeletons for rehabilitation and gait retraining would reduce the dependence on additional sensors, thereby improving the robustness of the control to external perturbations and variations in patient characteristics. Another strategy is to use iterative learning control, such as that presented in [15], which offers a way to optimize the adaptation of the exoskeleton to the patient. By repeating movements, the system can adjust its control parameters with the objectives of compensating for repetitive perturbations and improving trajectory tracking accuracy. This progressive learning capability is particularly beneficial for rehabilitation with this type of exoskeleton. Using and implementing the control strategies described above in an exoskeleton allows for a more efficient and adaptable system capable of predicting and correcting trajectories without the need for direct speed measurements while simultaneously improving its performance with everyday use. Such control strategies can open new possibilities and opportunities for the personalization of rehabilitation by automatically adjusting gait patterns based on user progress without manual intervention from the therapist.