# Probabilistic Sensitivity Amplification Control for Lower Extremity Exoskeleton
## 1 Introduction  
In recent decades, with the dramatic progress in computing, control and sensing, significant types of prostheses, orthoses and exoskeletons have been designed and developed to assist and support human limbs for various tasks. In order to increase the operator's strength and endurance, the exoskeleton is a typical class of wearable mechatronic robotic systems, which are designed to meet this demand. A particular feature of this kind of exoskeleton is that they are fully driven by the pilot intention and follow the pilot movements in synchrony. The application feasibility includes functional rehabilitation for the injured, physical assistance for the disabled and elderly and fatigue relief for heavy-duty workers. To recognize human intention, two main groups of sensors, i.e., cognitive-based sensors, as well as physical-based sensors, are usually applied to capture the information of the human interaction or the muscle signals. By measuring the electric signals from the musculoskeletal system or the nervous system, the operator's intention read by the cognitive-based sensors can be interpreted as the input to the exoskeleton system controller. A successful practical application of these kinds of exoskeletons isthe hybrid assistive limb (Tsukuba University and Cyberdyne, Japan) [1; 2], which is structured for whole-body assistance. The muscular activity signal is collected using surface electromyography (sEMG) sensors. In addition, the exploration for brain-machine interface (BMI) control is implemented at the University of Tubingen in Germany [3]. The hybrid BMI system consists of an EMG continuous decoder, as well as an electroencephalogram (EEG) binary classifier. Consequently, the crucial advantage of cognitive-based sensors is that the pilot or the patient intention can still be read, even if the subject cannot provide necessary joint torques. Thus, these types of the sensor systems are widely utilized in rehabilitation and medical application. An alternative sensor system recognizes the human intentions by applying physical-based sensors. The Body Extender [4] is designed to handle and transport heavy loads up to 100 kg. Consequently, the human intentions are read by the force sensors mounted on the interaction part between the pilot and exoskeleton. The velocity input to the controller is converted by the impedance control algorithm [5; 6]. Moreover, for low impedance, back drivability and precise control, a compact rotary series elastic actuator is designed in [7] to fulfill these requirements. The input to the system controller is derived from the deformation of the series elastic module. Also note that an improvement of system stability is introduced in [8], which addresses the time-delay effect of the series elastic module. Apart from the sensor-based human intention recognition, the sensitivity amplification control is proposed in [9; 10] for the Berkeley Lower Extremity Exoskeleton. The design conception of control algorithm aims to enable a pilot's locomotion with little effort on any type of terrain. In addition, the control scheme demands on interaction sensors between the pilot, as well as the exoskeleton. Nevertheless, the controller has little robustness to model uncertainties or parameter variations. Although the sensors mounted between the pilot and the exoskeleton can simplify the control algorithm, a crucial issue that should be addressed is that the time-delay effect cannot be avoided. Consequently, the control performance is limited due to this issue, even if the robustness can be enhanced by the compensation of the disturbance observer [11] or the internal model control [12]. The problem of time-delay effect may be solved by the cognitive-based sensors [13; 14], since the human intention can be acquired before the movement primitives. Nevertheless, these signals derived from the sensors suffer from unwanted noise, as well as the overlap of the spectrum with other signals.