This letter presents the electromechanical design, system architecture, baseline control development, and the preliminary evaluation of a novel upper-limb exoskeleton, named CLEVERarm. The developed system can support the motions of inner shoulder, glenohumeral joint, elbow and wrist’s pronation/supination and flexion/extension. Enhanced portability, design robustness, and ergonomic performance are chosen as the pillars of the design. These qualities are selected due to their importance for enabling exoskeletons’ use outside the clinical and research setting. CLEVERarm’s links are manufactured using carbon-fiber-reinforced 3D-printed plastic to achieve a lightweight and compact design. For design robustness, minimizing maintenance requirements are taken into account in the selection and placement of actuation, sensing and controller components. Finally to realize an ergonomic interaction, inner shoulder motions are supported by a back-drivable motorized design, controlled by an impedance controller. This architecture enables accommodating natural variations in scapulohumeral rhythm among users. The performance of the developed exoskeleton is evaluated using experiments involving healthy subjects. It is shown that despite its lightweight, CLEVERarm is structurally stiff and supports a large subset of the healthy range of motion. Additionally, experimental results confirming the performance of the baseline controller are presented.

Summary: This paper studies the problem of optimizing the kinematic structure of an eight degree-of-freedom upper-limb rehabilitation exoskeleton. The objective of optimization is achieving minimum volume and maximum dexterity in the workspace of daily activities specified by a set of upper-arm configurations. To formulate the problem, a new index is proposed for effective characterization of kinematic dexterity for wearable robots. Additionally, a set of constraints are defined to ensure that the optimal design can cover the desired workspace of the exoskeleton, while singular configurations and physical interferences are avoided. The formulated multi-objective optimization problem is solved using an evolutionary algorithm (Non-dominated Sorting Genetic Algorithm II) and the weighted sum approach. Among the resulted optimal points, the point with least sensitivity with respect to the variations of design variables is chosen as the final design.

Precise control of air–fuel ratio (AFR) is one of the most challenging tasks in lean‐burn spark ignition engines control. The main problem arises because of the large time‐varying delay in the engine operating envelope. In this study, a new sliding mode‐based synthesis method is presented to control AFR in order to improve fuel economy and decrease the tailpipe emissions. The time‐varying delay dynamics is first estimated by Padé approximation, which transfers the system into a system with parameter‐varying non‐minimum phase dynamics. Non‐minimum phase characteristics restrict the application of conventional sliding mode control approach because of the unstable internal dynamics. The system dynamics is then rendered into the normal form to investigate the system unstable internal dynamics. A systematic approach is proposed to design a linear dynamic parameter‐varying sliding manifold (LDPVSM) in order to stabilise the unstable internal dynamics according to the desired output tracking error dynamics. Additionally, the proposed LDPVSM provides the system with robustness against unmatched perturbation. The method that can be easily implemented in practical settings exhibits the desired dynamics independent of the matched and unmatched disturbances. The results of applying the proposed method to experimental data demonstrate the closed‐loop system stability and a superior performance against time‐varying delay, canister purge disturbances and measurement noise.