# Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton using Electromyography
## 1 Introduction  
Exoskeletons are wearable mobile robots that combine various technologies to enable limb movement with greater strength and endurance. These robots are used in several application areas, such as industry [1] and medicine [2]. Industry uses exoskeletons to increase the productivity and safety of workers, enabling them to lift, carry, and operate heavy objects and tools [3], thus reducing risks arising from this handling [4; 5]. In the medical field, exoskeletons are used to rehabilitate patients by assisting the execution of repetitive movements, thus reducing the therapist's workload. The use of the exoskeleton increases the efficiency of treatments for the recovery of neuromuscular functions [6]. Among the different applications in rehabilitation, we can cite the work of [PERSON] et al. [7], who developed an exoskeleton to assist people with muscle weakness. In [6; 8], the authors built exoskeletons for the rehabilitation of arms. [PERSON] et al. [9] focused on helping patients to rehabilitate their hands. These and other applications require techniques and technologies to assess muscle function for subsequent activation of the exoskeleton. Electromyography (EMG) is one of the available techniques used to monitor muscle function. This technique captures the energy generated by the muscles and provides electrical signals that indicate muscle activation. This type of signal is called electromyographic signal [10], and its intensity is directly related to the amount of force exerted by the skeletal muscles [11; 12; 13], which are responsible for the movement of the joints and the human posture [10; 14]. With the correct interpretation, it is possible to find the user's movement intentions indicated by the electromyographic signals. Several works combine EMG with other techniques to control exoskeletons, including neuro-fuzzy [8; 15], classifiers and neural networks [16], proportional closed-loop control [4], machine learning [17], cinematics [18], and Kalman filter and Hill model [19]. The use of classifiers enables identifying movements with accuracy above 90% [19]. Neural networks can adapt; however, the complexity grows exponentially with the number of inputs [20]. Fuzzy logic offers simplicity and is tolerant to a certain degree of contradiction in data [20]. Although these works indicate that techniques based on Artificial Intelligence (AI) have the potential to be used in the control of exoskeletons, they require training and have a high computational cost. AI-based systems also tend not to behave well in untrained/modeled scenarios [16; 21]. Other problems with artificial intelligence techniques lie in insufficient data acquisition that tends to increase the modeling error. An alternative to AI techniques relies on the use of control techniques analogous to biomechanics and allows changing the rigidity of the robot so that it behaves as human musculature. Among these techniques, we can highlight the impedance and admittance controls, which perform position control through the forces/torques applied to the mechanical structure. At the impedance level, the actuator reacts to the environment, changes its position, and corrects the error generated by the interaction with the environment [22]. Admittance control acts in favor of this change of position, facilitating the user's interaction with the robotic structure. This method softens the stiffness of the structure [23], usually taking position or speed into account as control parameters. This research is inserted in the context above and presents a hybrid control system that combines impedance and admittance controls for an exoskeleton of upper limbs to lift large loads. The admittance control defines the position (posture) that the exoskeleton must assume, while the impedance control corrects changes caused by interactions with loads. In addition to the electromyography signals, the system considers the inclination the limb is in, the degree of inclination of the joints, and the load that the structure is carrying.