# Single Leg Gait Tracking of Lower Limb Exoskeleton Based on Adaptive Iterative Learning Control
## 1 Introduction  
Lower limb exoskeleton robot is a mechanical device to improve a human's \"physical strength\" [1; 2]. It can assist aged and disabled citizens, rehabilitate injured patients, and extend the capacity of military and engineering forces [3; 4; 5; 6]. As the lower limb exoskeleton should move synchronously with human legs, accurate tracking of human gait is particularly important for its performance [7; 8; 9]. Human gait is periodic and the gait trajectory tracking can be predicted within finite time intervals. The iterative learning control (ILC) algorithm is such an iterative method to predict gait pattern with prior knowledge [10; 11]. ILC takes advantage of the invariant characteristics of a system and improves control performance through historical iterations. Therefore, ILC is suitable for lower limb exoskeleton control with proper gait trajectory tracking. [PERSON] et al. proposed an ILC model to improve the performance of upper limb rehabilitation robotics and concluded that the ILC can be more accurate with repetitive rehabilitation passive training [12]. [PERSON] et al. also designed an ILC algorithm with a forgetting factor to improve the control system robustness at the cost of response speed [13].  
Although ILC has been proven effective as a control method for repetitive motions, it is difficult to be applied in practice due to unclear initial value and high tracking error [14; 15]. [PERSON] et al. proposed an adaptive fuzzy decoupling control method to approximate the unknown system coefficients and improved the existing ILC model through simplifying the learning process, reducing computing time, and coupling multiple-input multiple output (MIMO) nonlinear systems in real-time operation [16].  
[PERSON] and [PERSON] implemented adaptive law to smooth exoskeleton interaction and ensure the trajectory-based controller [17]. Other researchers also introduced electromyography (EMG) and other data sources to improve the control accuracy [18; 19]. Based on these studies, this study intends to implement an adaptive control mechanism for ILC to minimize the tracking error so that the stability and robustness can be improved in the future. The proposed adaptive iterative learning control (AILC) can not only acquire the prior system knowledge based on multiple iterations but also reduce the nonlinear uncertainty of the controller [20; 21].  
In summary, this paper contributes to the body of knowledge by introducing a novel adaptive iterative learning control mechanism, which allows a more precise and flexible controller for the lower limb exoskeleton robots. By aggregating and forming a time frame for the prior knowledge, this study utilizes iterative prediction to reduce the gait tracking error so that the nonlinear uncertainty caused by the human-machine coupling can be effectively reduced. The proposed method can further improve the feasibility and safety of the lower limb exoskeleton robots with various functionalities.  
The rest of paper is organized as follows: in Section 2, the trajectory tracking algorithm AILC is introduced. Section 3 introduces the design and process of the target trajectory for learning and a validation experiment. In Section 4, the gait tracking results are analyzed and the human-robot interaction moments on gait tracking errors are discussed. In Section 5, the application and limitations of the proposed method are discussed. Section 6 summarizes the conclusions and potential future work.