# The Energy Consumption and Robust Case Torque Control of a Rehabilitation Hip Exoskeleton
## 1 Introduction  
An urgent need for robotic exoskeletons has been widely observed in the military, industry, medicine and hazardous situations such as disasters and pandemics over the last few decades [1, 2]. Some exoskeleton robots augment human mobility or endurance, while others are used for rehabilitation and ambulatory assistance [3]. In addition, some are used for paediatric recovery, while others serve the elderly, soldiers or athletes [4, 5, 6]. According to a 2019 United Nations' report [7, 8], the number of strokes and the percentage of mobility accidents are the highest among people older than 65 years and the 250,000-500,000 patients suffering from spinal cord injury (SCI) every year, increase research interest in robotic exoskeletons [9]. Additionally, more than 17% of work-related disorders are associated with lower limb activity, which also increased the need for robotic exoskeletons for mobility assistance and rehabilitation [9, 10]. Thus, a lower limb exoskeleton, specifically, the rehabilitation hip exoskeleton, is the focus of this paper because 45% of the mechanical energy of lower limb movements comes from the hip joint [8, 11]. Robotic exoskeletons can contribute in repetitive training of human extremities to enable patients to pursue rehabilitation sessions at home and enable therapists to focus on more specialised rehabilitation sessions [12]. Among the different rehabilitation exoskeletons are those used for sit-to-stand and walking movements. Therefore, we focus more on these activities because of their importance in daily life [13]. The rehabilitation hip exoskeleton is a mechanical structure worn to enhance gait mobility but can also be used, for instance, after hip replacement surgery or a stroke [14]. Since, in the very early stages of rehabilitation sessions, patients' limbs are usually trained passively to track predefined trajectory motions, which vary according to the patients'needs or limb disorders [15], different controller strategies have been used to track and control the trajectories because human extremities are intolerant of any unexpected harmful movement errors; in particular, rehabilitation exoskeletons serve people who already have some sort of limb disorder or mobility deficiency [16; 17]. Some control strategies have used fuzzy hybrid controller, while others utilised Bayesian optimisation to minimise the metabolic cost of walking by adjusting various control parameters for different individuals [18; 19]. Some applications were required to apply different parameters based on assistance onset timing, assistance magnitude and duration, as used in [20]. However, building a seamless controller integrated with all applications and human biological systems is still challenging. Therefore, some researchers used reinforcement learning for optimal controllers or to perform a normative range of motion and gait patterns during locomotion [21; 22]. While others applied real-time optimisation control strategies based on measurements of the user, machine learning to implement various dynamic controllers, EMG-based controllers or observer-based robust controllers [23; 24; 25; 26]. However, the most used controller is the proportional integral derivative (PID), which showed its insufficient performance in tracking a predefined trajectory and overcoming changes to model parameters and uncertainties [16; 27; 28]; therefore, an optimal controller, a linear quadratic regulator (LQR), is proposed to be used in this work. However, the hip exoskeleton, like other active exoskeleton devices, contains sensors, actuators, links, batteries and electronic circuits that operate in close contact with patients; for this reason, it should operate safely and reliably.