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It is a common hypothesis in the field of robot-assisted gait rehabilitation that the active involvement and voluntary participation of neurologically impaired subjects in the robotic gait training process may enhance the outcomes of such therapy. An adaptive seamless assist-as-needed (AAN) control scheme was developed for the robotic gait training. The AAN control scheme learns in real time the disability level of human subjects based on the trajectory tracking errors and adapts the robotic assistance accordingly. The overall AAN control architecture works on the basis of a robust adaptive control approach. The performance of seamless AAN control scheme was evaluated during treadmill training with a compliant robotic orthosis having six degrees of freedom. Two experiments, namely, trajectory following experiment and the AAN experiment were carried out to evaluate the performance of seamless adaptive AAN control scheme. It was found that the robotic orthosis is capable of guiding the subjects’ limbs on reference trajectories during the trajectory following experiment. Also, a variation in robotic assistance was recorded during the AAN experiment based on the voluntary participation of human subjects. This work is an advance on the current state of the art in the compliant actuation of robotic gait rehabilitation orthoses in the context of seamless AAN gait training. [ABSTRACT FROM AUTHOR]

This paper describes the design analysis and optimization of a novel 3-degrees of freedom (DOF) wearable parallel robot developed for ankle rehabilitation treatments. To address the challenges arising from the use of a parallel mechanism, flexible actuators, and the constraints imposed by the ankle rehabilitation treatment, a complete robot design analysis is performed. Three design stages of the robot, namely, kinematic design, actuation design, and structural design are identified and investigated, and, in the process, six important performance objectives are identified which are vital to achieve design goals. Initially, the optimization is performed by considering only a single objective. Further analysis revealed that some of these objectives are conflicting, and hence these are required to be simultaneously optimized. To investigate a further improvement in the optimal values of design objectives, a preference-based approach and evolutionary-algorithm-based nondominated sorting algorithm (NSGA II) are adapted to the present design optimization problem. Results from NSGA II are compared with the results obtained from the single objective optimization and preference-based optimization approaches. It is found that NSGA II is able to provide better design solutions and is adequate to optimize all of the objective functions concurrently. Finally, a fuzzy-based ranking method has been devised and implemented in order to select the final design solution from the set of nondominated solutions obtained through NSGA II. The proposed design analysis of parallel robots together with the multiobjective optimization and subsequent fuzzy-based ranking can be generalized with modest efforts for the development of all of the classes of parallel robots. [ABSTRACT FROM AUTHOR]

This paper presents the development of a novel adaptive wearable ankle robot for the treatments of ankle sprain through physical rehabilitation. The ankle robot has a bioinspired design, devised after a careful study of the improvement opportunities in the existing ankle robots. Robot design is adaptable to subjects of varying physiological abilities and age groups. Ankle robot employs lightweight but powerful pneumatic muscle actuators (PMA) which mimics skeletal muscles in actuation. To address nonlinear characteristics of PMA, a fuzzy-based disturbance observer (FBDO) has been developed. Another instance of an adaptive fuzzy logic controller based on Mamdani inference has been developed and appended with the FBDO to compensate for the transient nature of the PMA. With the proposed control scheme, it is possible to simultaneously control four parallel actuators of the ankle robot and achieve three rotational degrees of freedom. To evaluate the robot design, the disturbance observer, and the adaptive fuzzy logic controller, experiments were performed. The ankle robot was used by a neurologically intact subject. The robot–human interaction was kept as active–passive while the robot was operated on predefined trajectories commonly adopted by the therapists. Trajectory tracking results are reported in the presence of an unpredicted human user intervention, use of compliant and nonlinear actuators, and parallel kinematic structure of the ankle robot. [ABSTRACT FROM PUBLISHER]

Ionic polymer metal composites (IPMCs) are actuators that lend themselves well to microfluidic applications due to their lightweight, flexibility, ability to tailor their geometry, as well as the capability to be miniaturized and implanted into microelectro-mechanical systems devices. The major issue with implementing IPMCs into such devices is the ability to control their actuation and, hence, their reliability over a long period of time. This paper presents a novel iterative feedback tuning (IFT) algorithm that tunes the system online using experimental data during normal system operation. The controller adaptively tunes the highly nonlinear and time varying IPMC for a newly proposed micropump. This demonstrates the ability of the system to have a reliable performance over a long period of time without the need of any offline tuning or system identification. The system was run for 20 controller updates. This corresponds to 10 and 20 min of operation for the 0.1 and 0.05 Hz reference inputs, respectively. 100 and 300 μm amplitudes were tested to demonstrate the ability of the system to adaptively tune to different input signals. Experimental results show the newly proposed IFT algorithm has successfully tuned the controller to achieve up to 92% better performance when compared with a conventional model-based tuned controller. [ABSTRACT FROM PUBLISHER]

Some of the most exciting possibilities for dielectric elastomer artificial muscles consist of biologically inspired networks of smart actuators working towards common goals. However, the creation of these networks will only be realised once intelligence and feedback can be fully distributed throughout an artificial muscle device. Here we show that dielectric elastomer artificial muscles can be built with intrinsic sensor, control, and driver circuitry, bringing them closer in capability to their natural analogues. This was achieved by exploiting the piezoresistive behaviour of the actuator’s highly compliant electrodes using what we have called the dielectric elastomer switch. We developed suitable switching material using carbon loaded silicone grease and experimentally demonstrated the primitives required for self-sensing actuators and digital computation, namely compliant electromechanical NAND gates and oscillator circuits. We anticipate that dielectric elastomer switches will reduce the need for bulky and rigid external circuitry as well as provide the simple distributed intelligence required for soft, biologically inspired networks of actuators. Examples include many-degree-of-freedom robotic hearts, intestines, and manipulators; wearable assistive devices; smart sensor skins and fabrics; and ultimately new types of artificial muscle embedded, electromechanical computers. [ABSTRACT FROM AUTHOR]