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This work describes the control of a pneumatic soft robotic actuator via eye movements. The soft robot is actuated using two supply sources: a vacuum pump and an air supply pump for both negative and positive air supply sources respectively. Two controlled states are presented: the actuation of the vacuum and air pump. Through eye positioning and tracking on the graphical user interface to actuate either pump, a control command is directed to inflate or deflate the pneumatic actuator. The potential of this application is in rehabilitation, whereby eye movements are used to control a rehabilitation-based assistive soft actuator rather than ON/OFF electronics. This is demonstrated in this work using an elbow based rehabilitation soft actuator., Biomedical Engineering; Electrical Engineering; Data Visualization; Computer Engineering; Robotics; Materials Characterization; Structural Analysis; pneumatic actuator; soft robot; pleated network design; corrugated actuator; eye tracker controlled actuator; eye gaze system for control; tobii tracker 4c with soft actuator

Using body wave to swing forward is the most common propulsion method for underwater creatures, which can achieve flexible swing and multi-directional steering. Related robotics researches in recent years employed rigid structure and hinge connection to build the entire swing body, which were driven by conventional motors. However, soft robotics provides an alternative to make the robot behave like a bionic fish in terms of swing flexibility, actuation and efficiency. In this article, the authors develop the entire eel-like fish with a fully soft flexible body, which is composed of four fiber-reinforced, bidirectionally bending, fluidic elastomer actuators (FEAs) as its tail. This robotic fish tail has multiple segments connected in series with different waveforms to make the overall fish tail exhibit fluctuating forward performances. The FEA uses the typical dual-chamber configuration, and PWM sine wave signals are utilized to effectively control such a hydraulic mechanism. With experiments, the swing of each tail section is tested, and various factors with respect to the swing frequency and amplitude are studied. The results illustrate the feasibility and superiority of the proposed body wave control method.

Soft actuators have played an indispensable role in generating compliant motions of soft robots. Among the various soft actuators explored for soft robotic applications, dielectric elastomer actuators (DEAs) have caught the eye with their intriguing attributes similar to biological muscles. However, the control challenge of DEAs due to their strong nonlinear behaviors has hindered the development of DEA-based soft robots. To overcome the control challenge, this paper proposes a bioinspired control approach of DEAs. A three-dimensional muscle-like DEA, capable of large forces and giant deformation, is fabricated and adopted as the control platform. To facilitate the controller design, the dynamic model of the DEA is developed through experimental analysis, which takes electromechanical coupling, viscoelastic effects and dynamics uncertainties into consideration. Motivated by the proprioception of the biological muscles, the self-sensing capability of the actuator is explored and exhibits good accuracy. Thus the self-sensing of the actuator is utilized to provide the sensory feedback in the control loop without the need of additional external sensors. Inspired from the role of the cerebellum in motor learning, a cerebellum model articulation nonlinear controller is proposed to compensate the dynamics uncertainties and to provide motion correction. Finally, the effectiveness of the proposed control approach is verified by both the simulation and the experiments.

The improvement of the load capacity of soft robotic grippers has always been a challenge. The load improvement methods of existing soft robotic grippers mainly include the development of soft actuators with high output force and the creation of closed gripping structures. Inspired by winding behaviors of animals and plants, we propose a high-load soft robotic gripper driven by pneumatic artificial muscles (PAMs) that combines the advantages of a high force soft actuator and a closed gripping structure. Most existing model formulations focus on characterizing the end force generated to the length contraction and applied pressure of PAMs. However, the focus of this work is to build the force model of PAMs in winding shape to analyze the tightening force of the high-load soft gripper, and the model is validated by a tightening force test. An experimental work is carried out to characterize the load capacity and multi-object gripping capacity of the high-load soft gripper. We experimentally prove that it can lift heavy objects that weigh up to 35.5 kg, which is more than 47 times its weight. This work contributes to the load improvement of soft robotic grippers, and the mathematical modeling of engineering systems with winding structures. The developed high-load soft gripper is expected to enter the practical application field from the laboratory.

Soft robots have been a buzz word in the field of robotics. However, there are several definitions of soft robots in literature with their varying degrees of usefulness to providing guidelines for designing and constructing a soft robot. This paper attempts to provide a comprehensive definition of soft robots with the goals that the definition should be of (i) generality and (ii) practicality. By generality, it is meant that the definition gives the signature to soft robots and is all inclusive, and by practicality, it is meant that the definition provides a general guideline for practicing designers to design and construct soft robots to particular applications. A salient point in our definition of soft robots is the concept of softness, which is defined from the perspective of a receiving object - particularly as the stress and other damage quantities (e.g., deflection) created in the receiving object when the receiving object interacts with the soft system per se. A further contribution is the provision of definitions to soft sensor, soft actuator along with power generator, soft controller, and soft mechanism. Finally, research issues on soft robots are outlined.

Successful soft robot modeling approaches appearing in the recent literature have been based on a variety of distinct theories, including traditional robotic theory, continuum mechanics, and machine learning. Though specific modeling techniques have been developed for and validated against already realized systems, their strengths and weaknesses have not been explicitly compared against each other. In this article, we show how three distinct model structures, a lumped-parameter model, a continuum mechanical model, and a neural network, compare in capturing the gross trends and specific features of the force generation of soft robotic actuators. In particular, we study models for fiber-reinforced elastomeric enclosures (FREEs), which are a popular choice of soft actuator and that are used in several soft articulated systems, including soft manipulators, exoskeletons, grippers, and locomoting soft robots. We generated benchmark data by testing eight FREE samples that spanned broad design and kinematic spaces and compared the models on their ability to predict the loading–deformation relationships of these samples. This comparison shows the predictive capabilities of each model on individual actuators and each model’s generalizability across the design space. While the neural net achieved the highest peak performance, the first principles-based models generalized best across all actuator design parameters tested. The results highlight the essential roles of mathematical structure and experimental parameter determination in building high-performing, generalizable soft actuator models with varying effort invested in system identification.

Robotic devices with soft actuators have been developed to realize the effective rehabilitation of patients with motor paralysis by enabling soft and safe interaction. However, the control of such robots is challenging, especially owing to the difference in the individual deformability occurring in manual fabrication of soft actuators. Furthermore, soft actuators used in wearable rehabilitation devices involve a large response delay which hinders the application of such devices for at-home rehabilitation. In this paper, a feed-forward control method for soft actuators with a large response delay, comprising a simple feed-forward neural network (FNN) and an iterative learning controller (ILC), is proposed. The proposed method facilitates the effective learning and acquisition of the inverse model (i.e. the model that can generate control input to the soft actuator from a target trajectory) of soft actuators. First, the ILC controls a soft actuator and iteratively learns the actuator deformability. Subsequently, the FNN is trained to obtain the inverse model of the soft actuator. The control results of the ILC are used as training datasets for supervised learning of the FNN to ensure that it can efficiently acquire the inverse model of the soft actuator, including the deformability and the response delay. Experiments with fiber-reinforced soft bending hydraulic actuators are conducted to evaluate the proposed method. The results show that the ILC can learn and compensate for the actuator deformability. Moreover, the iterative learning-based FNN serves to achieve a precise tracking performance on various generalized trajectories. These facts suggest that the proposed method can contribute to the development of robotic rehabilitation devices with soft actuators and the field of soft robotics.

Beyond a traditional stimuli-responsive soft actuator that shows a single motion by a stimulus, multidirectional actuation reversal with a single stimulus is highly required in applications such as shape morphing sensors and soft robotics. Liquid crystal elastomers (LCEs) are one of the most attractive candidates for the soft actuator due to their capability of stimuli-responsive shape changing in 3D, which is programmable with local orientation of LC mesogens. Here, a simple but effective method to fabricate monolithic LCE actuators that are capable of reversible curvature reversal in bending and twisting deformation by a single stimulus-heat-is reported. The curvature reversal of the LCE film can be programmed by means of asymmetric crosslinking density along the thickness and the orientation of the LC mesogens. The curvature reversal of the monolithic LCE film exhibits highly reversible (more than 100 times) and fast actuation (≈3-5 s) by heating and cooling, allowing new concept of a practical application using LCE material: a self-regulated smart valve that is capable of qualitatively sorting liquids by temperature. It is believed that this system is potentially applied to a self-regulated sorting platform for various endothermic and exothermic chemical or biological reactions.

The design of robots that are light, soft, powerful is a grand challenge. Since they can easily adapt to dynamic environments, soft robotic systems have the potential of changing the status-quo of bulky robotics. A crucial component of soft robotics is a soft actuator that is activated by external stimuli to generate desired motions. Unfortunately, there is a lack of powerful soft actuators that operate through lightweight power sources. To that end, we recently designed a highly scalable, flexible, biocompatible Electromagnetic Soft Actuator (ESA). With ESAs, artificial muscles can be designed by integrating a network of ESAs. The main research gap addressed in this work is in the absence of system-theoretic understanding of the impact of the realtime control and actuator selection algorithms on the performance of networked soft-body actuators and ESAs. The objective of this paper is to establish a framework that guides the analysis and robust control of networked ESAs. A novel ESA is described, and a configuration of soft actuator matrix to resemble artificial muscle fiber is presented. A mathematical model which depicts the physical network is derived, considering the disturbances due to external forces and linearization errors as an integral part of this model. Then, a robust control and minimal actuator selection problem with logistic constraints and control input bounds is formulated, and tractable computational routines are proposed with numerical case studies., To appear at the 2018 International Conference on Robotics and Automation (ICRA), Brisbane, Australia, May 21, 2018--May 25, 2018

Given that mobile soft robots are adaptable to the environment, they are always tethered with slow locomotion speed. Compared with other types of mobile robots, mobile soft robots may be more suitable for rescuing tasks, accompanying elderly people, and being used as a safe toy for children. However, the infinite freedom of soft robots increases the difficulty of precision control. In addition, the large volume and long tube of the conventional soft actuator structure limit the range of motion of current mobile soft robots. In this article, a newly designed innovative untethered-bioinspired quadrupedal robot based on double-chamber pre-charged pneumatic (DCPCP) soft actuators with highly flexible trunk is proposed. Asymmetrical cross-tendons actuated by servo motors are used to drive the DCPCP soft legs so that buckling can be avoided and mimic the gait of quadruped animals with the simplest drive and control strategy. In addition, the proposed design greatly improves energy efficiency and exhibits superior performance of variable stiffness. The bioinspired highly flexible trunk is designed with the supporting spine structure and tendon driven muscle to deform, which can constantly adjust to the contact situation between the foot and the ground to adjust the center of gravity of the soft quadruped robot and increase stability when walking and turning. The proposed soft quadruped robot does not require any air compressors, valves, and hoses. The characteristics of untethered, high-energy efficiency, linear control, and stability make the soft quadruped robot suitable for many applications.

Fluid actuated soft robots, or fluidic elastomer actuators, have shown great potential in robotic applications where large compliance and safe interaction are dominant concerns. They have been widely studied in wearable robotics, prosthetics, and rehabilitations in recent years. However, such soft robots and actuators are tethered to a bulky pump and controlled by various valves, limiting their applications to a small confined space. In this study, we report a new and effective approach to fluidic power actuation that is untethered, easy to design, fabricate, control, and allows various modes of actuation. In the proposed approach, a sealed elastic tube filled with fluid (gas or liquid) is segmented by adaptors. When twisting a segment, two major effects could be observed: (1) the twisted segment exhibits a contraction force and (2) other segments inflate or deform according to their constraint patterns. Utilizing such effects, various actuation modes could be realized. In this research, four modes of actuation are illustrated: (1) soft actuator and pump actuation, (2) serial actuation, (3) parallel actuation, and (4) agonist and antagonist actuation. Theoretic analysis and experimental studies for the basic actuation principle have been conducted. A case study on an anthropomorphic forearm based on the proposed twisting tube actuation has been developed to showcase the effectiveness of the actuation modes. The studies suggest that the proposed approach has a great potential in both soft and compliant robotics.

Soft robotics is a trending area of research that can revolutionize the use of robotics in industry 4.0 and cyber-physical systems including intelligent industrial systems and their interactions with the human. These robots have notable adaptability to objects and can facilitate many tasks in everyday life. One potential use of these robots is in medical applications. Due to the soft body of these robots, they are a suitable replacement for applications like rehabilitation and exoskeletons. In this paper, we present the neuro-fuzzy modeling of a soft pneumatic finger-like actuator. This actuator is a fiber-reinforced soft robot with the shape and dimensions of a real finger and moves in planar motion. A bending sensor is used as a feedback for curvature motion of this actuator. In order to model this actuator, an adaptive neuro-fuzzy inference system is utilized to overcome the hardship in the modeling of the nonlinear performance of the soft materials. An experimental setup is designed to obtain suitable input–output data needed for modeling. The results show the applicability of the utilized method in the modeling of the soft actuator.

Biomimicry of the stomach's peristaltic contractions can be challenging in the design, modeling, and control of a soft actuator. The mimicking of organ contractions advances our knowledge of the digestive system and analyzes the biological behavior by testing with a physical robot. This article proposes a ring-shaped soft pneumatic actuator (RiSPA) as a segment of the digestive tract. RiSPA is made of a ring frame with embedded bellow actuators that generate contractive motions. An embedded sensory system measures the contraction using range sensors. The kinematics and dynamics of RiSPA's contraction are modeled and simulated, while a state feedback algorithm is applied to them. The simulation results are validated experimentally by comparing the RiSPA measurements with desired applied signals. The proposed actuator provides controllable symmetrical and asymmetrical contractions analog to the human stomach. The results of RiSPA validate the prediction performance of the simulation and controller with applied sinusoidal signals as a peristaltic wave. RiSPA contractions can be applied to a broad range of applications, such as imitating the esophagus and intestine contractions.

Interest in emulating the properties of biological muscles that allow for fast adaptability and control in unstructured environments has motivated researchers to develop new soft actuators, often referred to as ‘artificial muscles’. The field of soft robotics is evolving rapidly as new soft actuator designs are published every year. In parallel, recent studies have also provided new insights for understanding biological muscles as ‘active’ materials whose tunable properties allow them to adapt rapidly to external perturbations. This work presents a comparative study of biological muscles and soft actuators, focusing on those properties that make biological muscles highly adaptable systems. In doing so, we briefly review the latest soft actuation technologies, their actuation mechanisms, and advantages and disadvantages from an operational perspective. Next, we review the latest advances in understanding biological muscles. This presents insight into muscle architecture, the actuation mechanism, and modeling, but more importantly, it provides an understanding of the properties that contribute to adaptability and control. Finally, we conduct a comparative study of biological muscles and soft actuators. Here, we present the accomplishments of each soft actuation technology, the remaining challenges, and future directions. Additionally, this comparative study contributes to providing further insight on soft robotic terms, such as biomimetic actuators, artificial muscles, and conceptualizing a higher level of performance actuator named artificial supermuscle. In conclusion, while soft actuators often have performance metrics such as specific power, efficiency, response time, and others similar to those in muscles, significant challenges remain when finding suitable substitutes for biological muscles, in terms of other factors such as control strategies, onboard energy integration, and thermoregulation.

Purpose Soft robotics is a burgeoning field owing to its high adaptability and safety in human–machine interaction and unstructured environments. However, the feedback control of soft actuators with flexible sensors is still a challenge. Design/methodology/approach To address this issue, this study proposes an optical fibre-based sensing membrane for the posture measurement of soft pneumatic bending actuators. The major contribution is the development of a flexible sensing membrane with a high sensitivity and repeatability for the feedback control of soft actuators. The characteristics of sensing membrane were analysed. The relationship between wavelength shift and bending curvature was derived. The curvatures of soft actuator were measured at four bending status, and the postures were reconstructed. Findings The results indicate that the measurement error is less than 2.1% of the actual bending curvature. The sensitivity is up to 212.8 pm/m−1, and the signal fluctuation in repeated measurements is negligible. This approach has broad application prospects in soft robotics, because it makes the optical fibre achieve more strength and compatible with soft actuators, thus improving the sensing accuracy, sensitivity and reliability of fibre sensors. Originality/value Different from previous approaches, an optical fibre with FBGs is embedded into a multilayered polyimide film to form a flexible sensing membrane, and the membrane is embedded into a soft pneumatic bending actuator as the smart strain limited layer which is able to measure the posture in real time. This approach makes the optical fibre stronger and compatible with the soft pneumatic bending actuator, and the sensing accuracy, sensitivity and reliability are improved. The proposed sensing configuration is effective for the feedback control of the soft pneumatic bending actuators.

Various robotic devices have been developed for home rehabilitation and support of therapists. Special attention has been focused on soft actuators due to their high viscoelasticity and flexibility, which can contribute to the safety and affinity with the users. However, most of them aimed at the assist of finger flexion, and few have been designed to support extension actively. Moreover, for most soft actuator based mechanisms, the individual-adaptability have not been considered nor appropriately evaluated. Consequently, the effect of individual difference on the assistance using soft robotic devices is unclear, and for the purpose of dealing with the individual difference, the whole mechanism should be designed and fabricated for each individual user, which is ineffective for the rehabilitation support. In this study, we proposed a hybrid soft mechanism with modularized fiber-reinforced elastomer actuators for joint-dependent flexion support and McKibben actuators for finger extension support. Without further changing the design of the elastomer actuator, the hybrid mechanism could be adapted to individual hand difference: proportions of hand segments, range of motion (ROM) and torque characteristics of the joints. A prototype of the mechanism was fabricated and evaluated. The results showed that the mechanism could meet the requirement of finger function assist. Moreover, the mechanism could be fine-tuned towards the individual hand by changing the fiber-reinforcement and adjusting the fittings of the actuators.

Soft materials and compliant actuation concepts have generated new design and control approaches in areas from robotics to wearable devices. Despite the potential of soft robotic systems, most designs currently use hard pumps, valves, and electromagnetic actuators. In this work, we take a step towards fully soft robots by developing a new compliant electromagnetic actuator architecture using gallium-indium liquid metal conductors, as well as compliant permanent magnetic and compliant iron composites. Properties of the new materials are first characterized and then co-fabricated to create an exemplary biologically-inspired soft actuator with pulsing or grasping motions, similar to Xenia soft corals. As current is applied to the liquid metal coil, the compliant permanent magnetic tips on passive silicone arms are attracted or repelled. The dynamics of the robotic actuator are characterized using stochastic system identification techniques and then operated at the resonant frequency of 7 Hz to generate high-stroke (>6 mm) motions.

Soft actuators producing large motion in a short time are mostly based on stretchable polymers actuated by pneumatic pressure; they consist of bulky components, including a motor, pump/compressor, tubes, and valves. In this study, we develop a fast-responding large-amplitude soft actuator, based on a liquid-gas phase transition, which produces a compact system. The required pressure is generated solely by the electrically induced phase transition of a fluid in a cavity, mimicking the thigmonastic movements found in plants. We discuss the critical design variables to improve the performance and propose a new design for the electrodes, which are the most critical components. Our bending actuator produces large motion in7 s, using a low-voltage source (50 V) that allows a much faster response than the soft actuators based on phase transition currently available.

Soft robotic systems offer benefits over traditional rigid systems through reduced contact trauma with soft tissues and by enabling access through tortuous paths in minimally invasive surgery. However, the inherent deformability of soft robots places both a greater onus on accurate modelling of their shape, and greater challenges in realising intraoperative shape sensing. Herein we present a proprioceptive (self-sensing) soft actuator, with an electrically conductive working fluid. Electrical impedance measurements from up to six electrodes enabled tomographic reconstructions using Electrical Impedance Tomography (EIT). A new Frequency Division Multiplexed (FDM) EIT system was developed capable of measurements of 66 dB SNR with 20 ms temporal resolution. The concept was examined in two two-degree-of-freedom designs: a hydraulic hinged actuator and a pneumatic finger actuator with hydraulic beams. Both cases demonstrated that impedance measurements could be used to infer shape changes, and EIT images reconstructed during actuation showed distinct patterns with respect to each degree of freedom (DOF). Whilst there was some mechanical hysteresis observed, the repeatability of the measurements and resultant images was high. The results show the potential of FDM-EIT as a low-cost, low profile shape sensor in soft robots., Comment: Fixed PDF conversion error. Now published in ICRA 2019, IEEE International Conference on Robotics and Automation 2019

Soft robotic systems generally require both soft actuators and soft sensors to perform complex functions. Separate actuators and sensors are often combined into one composite device when proprioception (self-sensing) is required. In this article, we introduce the concept of using a conductive liquid to perform both the sensing and actuation functions of a proprioceptive soft actuator. The working fluid drives actuator deformation while simultaneously acting as a strain-sensing component for detecting actuator deformation. The concept is examined and demonstrated in two proprioceptive flexible fluidic actuators (FFAs) that use conductive liquids as their working fluids: a linear actuator and a bending actuator. In both cases, we show that resistance can be used to infer strain. Some hysteresis and nonlinearity are present, but repeatability is high. The bandwidth of resistance as a sensing variable in the bending FFA is tested and found to be ∼3.665 Hz. Resistance is demonstrated as a feedback variable in a control loop, and the proprioceptive bending FFA is controlled to respond to step input and sinusoidal target functions. The effect of temperature on resistance–strain behavior is also examined, and we demonstrate how measurement of volume and resistance can be used to detect when the actuator is constrained. Biocompatible proprioceptive soft actuators such as those presented in this article are ideal for use in low-cost bionic healthcare components such as orthotics, prosthetics, or even replacement muscles.

The emerging field of soft robotics offers the prospect of applying soft actuators as artificial muscles in the robots, replacing traditional actuators based on hard materials, such as electric motors, piezoceramic actuators, etc. Dielectric elastomers are one class of soft actuators, which can deform in response to voltage and can resemble biological muscles in the aspects of large deformation, high energy density and fast response. Recent research into dielectric elastomers has mainly focused on issues regarding mechanics, physics, material designs and mechanical designs, whereas less importance is given to the control of these soft actuators. Strong nonlinearities due to large deformation and electromechanical coupling make control of the dielectric elastomer actuators challenging. This paper investigates feed-forward control of a dielectric elastomer actuator by using a nonlinear dynamic model. The material and physical parameters in the model are identified by quasi-static and dynamic experiments. A feed-forward controller is developed based on this nonlinear dynamic model. Experimental evidence shows that this controller can control the soft actuator to track the desired trajectories effectively. The present study confirms that dielectric elastomer actuators are capable of being precisely controlled with the nonlinear dynamic model despite the presence of material nonlinearity and electromechanical coupling. It is expected that the reported results can promote the applications of dielectric elastomer actuators to soft robots or biomimetic robots.

This paper presents the design and development of a pneumatic soft-and-rigid hybrid actuator system that consists of half-bellow shaped soft sections in-between block shape rigid sections. The hybrid actuator architecture allows for selective actuation of each soft section (acting as a joint) with precise control over its bending motion. The soft half-bellow section is designed as a series of hollow ridges extending straight to a flat base. This geometry provides forward and backward bending motion when subjected to positive and negative pressure, respectively. Bending occurs as the ridges of the soft section expand and contract more than the flat base due to pressure variations. The rigid sections serve as connections between soft actuator sections and enhance force transfer. As a case study, a hybrid actuator system was designed as a soft robotic digit with three soft joints and four rigid connecting sections. Finite element analysis was performed to evaluate the design parameters such as number of ridges and materials for the robotic finger. The joints (from proximal to distal) were designed to have four, three, and two ridges, respectively, to generate the desired range of angular motion. Fabrication of the finger was done with silicone rubber RTV-4234-T4 and PMC polyurethane rubber using a combination of compression molding and overmolding processes. The angular and translational displacements of the robotic finger were experimentally and numerically evaluated at different pressures. The trajectory of the fingertip is comparable to those reported in literature for continuous soft actuators with a similar length. The significance of this actuator system is that both range of angular and translational motions are achieved at low pressure, less than 70kPa, as opposed to reported pressures of greater than 100kPa. The presented results show the great potential of the soft robotic finger for use in robotic, rehabilitation, and assistive device applications.Copyright © 2015 by ASME

We introduce a soft actuator for grippers using DEA capable of bending actuation. The actuator is also able to generate the electro-adhesion by the fringe field formed at the edges of the electrodes. The adhesion improves the holding force and ensures the conformation of the structure to the object. After the characterization of the actuator, we develop a 2-finger soft gripper capable of holding various objects. The gripper has a mass of around 1 g, and consists of a few cm long actuation parts, realizing simple open-close movement. The compliance of the gripper leads to conformation of the structure against the object surface, which is proven by successful handling of objects with different geometries such as a toothbrush, a flat paper, and a ping pong ball. The effect of the electro-adhesion is visible when the paper is held with its flat shape meaning that an adhesion force against gravity exists. Also, by the fact that the conformed structure increases the contact area, the holding force is improved while avoiding damaging the object, which is highlighted by the ability to hold a raw egg weighing around 60 g. This soft gripper, combining both actuation and electro-adhesion, illustrates the potential use of DEA for soft robotics.

Soft actuators using pressurized air are being widely used due to their inherent compliance, conformability, and customizability. These actuators are powered and controlled by pneumatic supply systems (PSSs) consisting of components such as compressors, valves, tubing, and reservoirs. Regardless of the choice of actuator, the PSS critically affects overall performance of soft robots because it governs the soft actuator pressure dynamics, and thereby, the general dynamic behavior. While selecting and controlling PSS components for meeting desired soft actuator performance, specifications such as PSS mass, volume, and duration of operation must also be considered. Currently, there is no comprehensive study on PSS optimization for meeting dynamic performance and PSS specifications, due to limited understanding of soft actuator pressure dynamics, large solution space for PSSs, and variability in soft actuators. By considering critical parameters of PSS and soft actuators, we introduce and demonstrate PSS parameter optimization. We propose a normalized model for soft actuator pressure dynamics and quantify the relationship between PSS parameters, soft actuator design parameters, and dynamic performance metrics of rise time, fall time, and actuation frequency. After experimental validation, we applied these results and optimally select and control PSS components to meet desired soft actuator performance for a soft exosuit, while minimizing mass of selected components. The measured pressure response with this prototype agrees well with simulations, with root mean square errors