Wearable exoskeleton systems based-on pneumatic soft actuators and controlled by parallel processing

Human assistance innovation is essential in an increasingly aging society and one technology that may be applicable is exoskeletons. However, traditional rigid exoskeletons have many drawbacks. This research includes the design and implementation of upper-limb power assist and rehabilitation exoskeletons based on pneumatic soft actuators. A novel extensor-contractor pneumatic muscle has been designed and constructed. This new actuator has bidirectional action, allowing it to both extend and contract, as well as create force in both directions. A mathematical model has been developed for the new novel actuator which depicts the output force of the actuator. Another new design has been used to create a novel bending pneumatic muscle, based on an extending McKibben muscle and modelled mathematically according to its geometric parameters. This novel bending muscle design has been used to create two versions of power augmentation gloves. These exoskeletons are controlled by adaptive controllers using human intention. For finger rehabilitation a glove has been developed to bend the fingers (full bending) by using our novel bending muscles. Inspired by the zero position (straight fingers) problem for post-stroke patients, a new controllable stiffness bending actuator has been developed with a novel prototype. To control this new rehabilitation exoskeleton, online and offline controller systems have been designed for the hand exoskeleton and the results have been assessed experimentally. Another new design of variable stiffness actuator, which controls the bending segment, has been developed to create a new version of hand exoskeletons in order to achieve more rehabilitation movements in the same single glove. For Forearm rehabilitation, a rehabilitation exoskeleton has been developed for pronation and supination movements by using the novel extensor-contractor pneumatic muscle. For the Elbow rehabilitation an elbow rehabilitation exoskeleton was designed which relies on novel two-directional bending actuators with online and offline feedback controllers. Lastly for upper-limb joint is the wrist, we designed a novel all-directional bending actuator by using the moulding bladder to develop the wrist rehabilitation exoskeleton by a single all-directional bending muscle. Finally, a totally portable, power assistive and rehabilitative prototype has been developed using a parallel processing intelligent control chip

Disturbance rejecting PID-FF controller design of a non-ideal buck converter using an innovative snake optimizer with pattern search algorithm.

The optimal design of a proportional-integral-derivative controller with two cascaded first-order low-pass filters (PID-FF) for non-ideal buck converters faces significant challenges, including effective disturbance rejection, robustness to parameter variations, and the mitigation of high-frequency signal noise, with existing approaches often struggling and leading to suboptimal performance in practical applications. This study addresses these challenges by introducing a constraint on the open-loop crossover frequency to mitigate high-frequency noise and ensuring the controller prioritizes maintaining constant output voltage and robust responsiveness to input voltage and load current variations. This study also introduces an innovative metaheuristic algorithm, the opposition-based snake optimizer with pattern search (OSOPS), designed to address these limitations. OSOPS enhances the Snake Optimizer (SO) by integrating opposition-based learning (OBL) and Pattern Search (PS), thereby improving its exploration and exploitation capabilities. The proposed algorithm design includes a crossover frequency constraint aimed at counteracting high-frequency noise and ensuring robust performance under diverse disturbances. The efficacy of the OSOPS algorithm is demonstrated through rigorous statistical box plot analysis and convergence response comparisons with the original SO algorithm. Additionally, we systematically compare the performance of the OSOPS-based PID-FF-controlled non-ideal buck converter system against systems utilizing the original SO algorithm and the classical pole placement (PP) method. This evaluation encompasses transient and frequency responses, disturbance rejection, and robustness analysis. The results reveal that the OSOPS-based system outperforms the SO- and PP-based systems with 14.21 % and 32.10 % faster rise times, along with 15.38 % and 84.95 % faster settling times, respectively. The OSOPS and SO systems also exhibit higher bandwidths, exceeding the PP-based system by 18.74 % and 17.03 %, respectively. By addressing the key challenges in PID-FF controller design for non-ideal buck converters, this study provides a substantial advancement in control strategy, promising enhanced performance in practical applications

Design, Fabrication, Modeling and Control of Artificial Muscle Actuated Wrist Joint System

This research dissertation presents the design, fabrication, modeling and control of an artificial muscle (AM) actuated wrist joint system, i.e., a thermoelectric (TEM) antagonistically driven shape memory alloy (SMA) actuator, to mimic the muscle behavior of human beings. In the developed AM based wrist joint system, the SMA, exhibiting contraction and relaxation corresponding to its temperature, is utilized as the actuator in the AM. Similar to the nerve stimulation, TEM is introduced to provide heat stimulation to the SMA, which involves heating and cooling of the SMA. SMA possesses superelastic behavior that provides a large force over its weight and effective strain in practical applications. However, such superior material has been underutilized due to its high nonlinear hysteresis behavior, strongly affected by the loading stress. Using the data obtained from the experiments, based on the Prandtl-Ishlinskii (PI) model, a Stress-Dependent Generalized Prandtl-Ishlinskii (SD-GPI) model is proposed, which can describe the hysteresis behavior of the SMA under the influence of various stresses. The parameters of the SD-GPI models at various stresses are obtained using a fitting function from the Matlab. The simulation results of the SD-GPI showed that prediction error is achieved at mean values of ±2% and a standard deviation of less than 7%. Meanwhile, the TEM model is also developed based on the heat balance theory. The model parameters are identified via experimental data using Range-Kutta fourth order integration equation and Matlab curve fitting function. The TEM model has shown a satisfactory temperature prediction. Then, by combining the obtained two models, an integrated model is developed to describe the whole dynamics of the wrist joint system. To control the SMA actuated wrist system, the SD-GPI inverse hysteresis compensator is developed to mitigate the hysteresis effect. However, such a compensator shows errors in compensating the hysteresis effect. Therefore, the inverse hysteresis compensator error and the system tracking error are analyzed, and the adaptive back-stepping based control approach is adopted to develop the inverse based adaptive control for the antagonistic AM wrist joint. Subsequently, a corresponding control law is developed for the TEM system to generate the required temperature obtained from the adaptive controller. Simulations verified the developed approach. Finally, experiments are conducted to verify the proposed system. Input sinusoidal signal with frequency 0.1rad/s and amplitude of ±0.524rad (±30°) is applied to the wrist joint system. Experimental results verified that the TEMs antagonistically driven SMA actuators for artificial muscle resembling wrist joint has been successfully achieved

Smart Tendon Actuated Flexible Actuator

We investigate the kinematic feasibility of a tendon-based flexible parallel platform actuator. Much of the research on tendon-driven Stewart platforms is devoted either to the completely restrained positioning mechanism (CRPM) or to one particular type of the incompletely restrained positioning mechanism (IRPM) where the external force is provided by the gravitational pull on the platform such as in cable-suspended Stewart platforms. An IRPM-based platform is proposed which uses the external force provided by a compliant member. The compliant central column allows the configuration to achieve n DOFs with n tendons. In particular, this investigation focuses on the angular deflection of the upper platform with respect to the lower platform. The application here is aimed at developing a linkable module that can be connected to one another so as to form a “snake robot” of sorts. Since locomotion takes precedence over positioning in this application, a 3-DOF Stewart platform is adopted. For an arbitrary angular displace of the end-effector, the corresponding length of each tendon can be determined through inverse kinematics. Mathematical singularities are investigated using the traditional analytical method of defining the Jacobian

A Robot to Measure Water Parameters in Water Distribution Systems

Water distribution systems (WDS) are critical infrastructures that transfer drinking water to consumers. In the U.S., around 42 billion gallons of water are being delivered per day via one million miles of pipes to be used in different sectors. Incidents to pipelines cause leak or let contaminants enter purified water in pipe that is harmful to public health. Hence, periodic condition assessments of pipelines and water inside it are required. However, due to the long and complicated configurations of these networks, access to all parts of the pipelines is a cumbersome task. To this aim, in-pipe robots are promising solution that facilitate access to different locations inside pipelines and perform different in-pipe missions. In this project, we design and fabricate an in-pipe robotic system is that is used for water quality monitoring. The robot is equipped with a wireless sensor module and the sensor module is synchronized with the motion unit of the robot. The wireless sensor module facilitates bi-directional data transmission between the robot and base station aboveground. The integrated robotic system navigates in different configurations of the pipeline with smart motion. To this aim, the mechanical design of the self-powered robot based on three adjustable arm modules and three actuator modules is designed. The components of the robot are characterized based on real operation conditions in pipes. A multi-phase motion control algorithm is developed for the robot to move in straight path and non-straight configurations like bends and T-junctions. A bi-directional wireless sensor module is designed to send data packets through underground environment. Finally, the multi-phase motion controller is synchronized with the wireless sensor module and we propose an operation procedure for the robot. In the operation procedure, some radio transceivers are located at non-straight configurations of pipelines and receive the sensor measurements from the robot and guide the robot in the desired direction. The proposed operation procedure provides smart navigation and data transmission during operation for the robot

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Soft pneumatic actuators: a review of design, fabrication, modeling, sensing, control and applications

Soft robotics is a rapidly evolving field where robots are fabricated using highly deformable materials and usually follow a bioinspired design. Their high dexterity and safety make them ideal for applications such as gripping, locomotion, and biomedical devices, where the environment is highly dynamic and sensitive to physical interaction. Pneumatic actuation remains the dominant technology in soft robotics due to its low cost and mass, fast response time, and easy implementation. Given the significant number of publications in soft robotics over recent years, newcomers and even established researchers may have difficulty assessing the state of the art. To address this issue, this article summarizes the development of soft pneumatic actuators and robots up until the date of publication. The scope of this article includes the design, modeling, fabrication, actuation, characterization, sensing, control, and applications of soft robotic devices. In addition to a historical overview, there is a special emphasis on recent advances such as novel designs, differential simulators, analytical and numerical modeling methods, topology optimization, data-driven modeling and control methods, hardware control boards, and nonlinear estimation and control techniques. Finally, the capabilities and limitations of soft pneumatic actuators and robots are discussed and directions for future research are identified

Bio-inspired robotic control in underactuation: principles for energy efficacy, dynamic compliance interactions and adaptability.

Biological systems achieve energy efficient and adaptive behaviours through extensive autologous and exogenous compliant interactions. Active dynamic compliances are created and enhanced from musculoskeletal system (joint-space) to external environment (task-space) amongst the underactuated motions. Underactuated systems with viscoelastic property are similar to these biological systems, in that their self-organisation and overall tasks must be achieved by coordinating the subsystems and dynamically interacting with the environment. One important question to raise is: How can we design control systems to achieve efficient locomotion, while adapt to dynamic conditions as the living systems do? In this thesis, a trajectory planning algorithm is developed for underactuated microrobotic systems with bio-inspired self-propulsion and viscoelastic property to achieve synchronized motion in an energy efficient, adaptive and analysable manner. The geometry of the state space of the systems is explicitly utilized, such that a synchronization of the generalized coordinates is achieved in terms of geometric relations along the desired motion trajectory. As a result, the internal dynamics complexity is sufficiently reduced, the dynamic couplings are explicitly characterised, and then the underactuated dynamics are projected onto a hyper-manifold. Following such a reduction and characterization, we arrive at mappings of system compliance and integrable second-order dynamics with the passive degrees of freedom. As such, the issue of trajectory planning is converted into convenient nonlinear geometric analysis and optimal trajectory parameterization. Solutions of the reduced dynamics and the geometric relations can be obtained through an optimal motion trajectory generator. Theoretical background of the proposed approach is presented with rigorous analysis and developed in detail for a particular example. Experimental studies are conducted to verify the effectiveness of the proposed method. Towards compliance interactions with the environment, accurate modelling or prediction of nonlinear friction forces is a nontrivial whilst challenging task. Frictional instabilities are typically required to be eliminated or compensated through efficiently designed controllers. In this work, a prediction and analysis framework is designed for the self-propelled vibro-driven system, whose locomotion greatly relies on the dynamic interactions with the nonlinear frictions. This thesis proposes a combined physics-based and analytical-based approach, in a manner that non-reversible characteristic for static friction, presliding as well as pure sliding regimes are revealed, and the frictional limit boundaries are identified. Nonlinear dynamic analysis and simulation results demonstrate good captions of experimentally observed frictional characteristics, quenching of friction-induced vibrations and satisfaction of energy requirements. The thesis also performs elaborative studies on trajectory tracking. Control schemes are designed and extended for a class of underactuated systems with concrete considerations on uncertainties and disturbances. They include a collocated partial feedback control scheme, and an adaptive variable structure control scheme with an elaborately designed auxiliary control variable. Generically, adaptive control schemes using neural networks are designed to ensure trajectory tracking. Theoretical background of these methods is presented with rigorous analysis and developed in detail for particular examples. The schemes promote the utilization of linear filters in the control input to improve the system robustness. Asymptotic stability and convergence of time-varying reference trajectories for the system dynamics are shown by means of Lyapunov synthesis