In this work, we focus on iterative distributed model predictive control (DMPC) of large-scale nonlinear systems subject to asynchronous, delayed state feedback. The motivation for studying this control problem is the presence of asynchronous, delayed measurement samplings in chemical processes and the potential use of networked sensors and actuators in industrial process control applications to improve closed-loop performance. Under the assumption that there exist upper bounds on the time interval between two successive state measurements and on the maximum measurement delay, we design an iterative DMPC scheme for nonlinear systems via Lyapunov-based control techniques. Sufficient conditions under which the proposed distributed MPC design guarantees that the state of the closed-loop system is ultimately bounded in a region that contains the origin are provided. The theoretical results are illustrated through a catalytic alkylation of benzene process example.

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In this paper, we further develop our framework to design new assistance and rehabilitation protocols based on motor primitives. In particular, we extend our recent results of oscillator-based assistance to the case of walking. The adaptive oscillator used in this paper is capable of predicting the angular position of the user’s joints in the future, based on the pattern learned during preceding cycles. Assistance is then provided by attracting the joints to this future position using a force field in a compliant lower-limb exoskeleton. To demonstrate the method efficiency, we computed the rate of metabolic energy expended by the participants during a walking task, with and without assistance. Results show a significant decrease of energy expenditure with the assistance switched on, although not to a point to entirely compensate for the burden due to the exoskeleton lack of transparency. The results further show changes in the kinematics: with assistance, the participants walked with a faster cadence and ampler movements. These results tend to prove the relevance of designing assistance protocols based on adaptive oscillators (or primitives in general) and pave the way to the design of new rehabilitation protocols.

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In this paper, we explore the mechanical behavior of the knee during the weight acceptance stage of stance during normal walking. We show that the torque/angle behavior of the knee during this stage can be approximated by a linear torsional spring. The mechanical parameters completely specifying this spring are identified, including stiffness, amount of rotation, and angle of engagement, and the effect of gait speed and body/load mass on those parameters are discussed. We discuss how the findings of this paper can be applied to the design of leg orthoses, prostheses and exoskeletons, and bipedal robots in general, allowing the implementation of human-like leg compliance during stance with a relatively simple latched-spring mechanism.

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Symmetric ankle propulsion is the cornerstone of efficient human walking. The ankle plantar flexors provide the majority of the mechanical work for the step-to-step transition and much of this work is delivered via elastic recoil from the Achilles’ tendon — making it highly efficient. Even though the plantar flexors play a central role in propulsion, body-weight support and swing initiation during walking, very few assistive devices have focused on aiding ankle plantarflexion. Our goal was to develop a portable ankleexoskeleton taking inspiration from the passive elastic mechanisms at play in the human triceps surae-Achilles’ tendon complex during walking. The challenge was to use parallel springs to provide ankle joint mechanical assistance during stance phase but allow free ankle rotation during swing phase. To do this we developed a novel `smart-clutch’ that can engage and disengage a parallel spring based only on ankle kinematic state. The system is purely passive — containing no motors, electronics or external power supply. This `energy-neutral’ ankle exoskeleton could be used to restore symmetry and reduce metabolic energy expenditure of walking in populations with weak ankle plantar flexors (e.g. stroke, spinal cord injury, normal aging).

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The movements of the shoulder, elbow, and wrist play a vital role in the performance of essential daily activities. We therefore have developed a 2DOF exoskeleton robot (ExoRob) to rehabilitate the elbow and forearm movements of physically disabled individuals with impaired upper-limb function. The proposed ExoRob is supposed to be worn on the lateral side of forearm in order to provide naturalistic range movements of elbow (flexion/extension) and forearm (pronation/supination) motions. This paper focuses on the modelling, design (electrical and mechanical components), development, and control of the proposed ExoRob. The kinematic model of ExoRob has been developed based on modified Denavit-Hartenberg notations. Non-linear modified computed torque control technique is employed to control the proposed ExoRob, where trajectories (i.e., pre-programmed trajectories recommended by therapist/clinician) tracking corresponding to typical rehabilitation (passive) exercises has been carried out to evaluate the performances of the developed ExoRob and controller. Furthermore, experiments were carried out with the master exoskeleton arm [mExoArm, an upper-limb prototype 7DOF (lower scaled) exoskeleton arm] where subjects (robot users) or experimenter operate the mExoArm (like a joystick) to manoeuvre the proposed ExoRob to provide passive rehabilitation. Experimental results show that the controller is able to manoeuvre the ExoRob efficiently to track the desired trajectories. Such movements are widely used in rehabilitation and have been performed efficiently with the developed ExoRob and the controller.

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