## 1 Introduction  
Industrial robots for mass production and medical robots using advanced technology have been transforming to expand human living spaces and improve the quality of life. Robots have been developed in various forms to coexist with humans, such as exoskeleton robots. These exoskeleton robots are essential production work elements in the future on industrial or construction sites where workers repeatedly carry heavy objects. Even if the object is not heavy, moving objects repeatedly throughout the day can accumulate fatigue and reduce productivity. Additionally, according to one survey, the proportion of elderly persons in 2050 is expected to double compared to 2015 [1]. Due to the increasing aging society, the demand for assistance robots that can support daily life activities for the elderly is expected to increase. Therefore, an exoskeleton robot can help to increase worker productivity and reduce the risk of injury and fatigue and can also assist with human movement, such as medical applications. An exoskeleton robot has been defined as nonmedical applications and medical applications [2]. In other words, the robot goes by various names, such as a powered exoskeleton for military and industrial purposes [3; 4; 5] and power-assist suit for rehabilitation and assistive purposes [6; 7; 8]. Depending on whether the target of the application involves medical or nonmedical purposes, the actuation technologies [9; 10; 11], the exoskeleton mechanism [12; 13], the sensor type [14; 15], and the wearer intention algorithm should be chosen appropriately. As such, exoskeleton robots will be able to help with our daily lives across various fields in the future. It is necessary to identify a wearer's intention to control exoskeleton robots based on the wearer's intention algorithm. However, misalignment occurs if the link lengths and joint positions between the human and robot are different. This misalignment is one of the factors that adversely affects the wearer's intent. Additionally, it is difficult to grasp a wearer's intention owing to the mathematical relationship between a human lower muscular system and a lower-limb exoskeleton system. To overcome these difficulties, various algorithms based on torque control have been applied to a lower-limb exoskeleton robot. Fuzzy logic is one of the most effective methods for managing the complexity and uncertainties, because the system tolerates inaccurate information, is flexible, and can easily modify the rules used. For instance, the fuzzy logic method and proportional-based fuzzy (PBF) algorithm are used to identify the wearer's intention in real time, and an algorithm is applied to the exoskeleton robot [16; 17; 18]. In addition, the terrain feature estimation method using a contact point between the limb exoskeleton and ground is used to estimate the slope and elevation [19]. However, in order to measure a force between a wearer and a robot, there is a disadvantage of minimizing the contact point, because an expensive force sensor must be attached to every contact point. Therefore, the purpose of the study is to estimate and control the wearer's motion intention while overcoming these limitations. Due to the misalignment and the complex mathematical relationship between the human lower muscular system and a lower-limb exoskeleton system, it is difficult to grasp the intention of the wearer, resulting in an inefficient system performance. In this study, we propose a design for the motion profiles to control an exoskeleton robot's knees based on a weighted fuzzy logic algorithm using the hip joint angles. To design the proposed algorithm, preliminary work on the gait phases was required to analyze the knee of an exoskeleton robot. One leg joint was considered to have four degrees of freedom (4 DoFs), the hip and ankle joint angles of the exoskeleton robot were designed to be passive joints, and the knee joint angle worked as an active joint.