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This paper proposes the synthesis of a human knee exoskeleton using the reduced number of necessary and sufficient constraints. The proposed human knee exoskeleton consists of a defect-free crank-rocker mechanism capable of accurately moving its coupler point along the prescribed trajectory. For synthesizing the crank-rocker mechanism based on proposed reduced number of constraints, an optimization problem is formulated to synthesize the mechanism. An algorithm based on teaching-learning-based-optimization (TLBO) is presented to solve this highly nonlinear optimization problem. The optimization minimizes the error between generated and prescribed trajectory and simultaneously avoids any defect in the synthesized mechanism. The penalty method is used to manage all the constraints. Besides a realistic nontrivial example of human knee flexion/extension, a straight line trajectory is also considered to demonstrate the effectiveness of the refinement scheme in the optimal syntheses of planar crank-rocker linkage free from all defects. The optimization problem is solved using well-established nature-inspired algorithms such as genetic algorithm (GA), particle swarm algorithm (PSO), and teaching-learning-based-optimization (TLBO) algorithm. It is found that TLBO is computationally more efficient than other algorithms used here. Additionally, the proposed human knee exoskeleton model is experimentally validated for one gait cycle.

This paper presents a two stage optimization procedure for dynamic balancing of planar mechanisms and finding optimum link shapes. In the first stage, the shaking force and shaking moment are minimized by optimizing mass distribution of links using the equimomental system of point-masses for each link. Then for the optimum inertial parameters of the balanced mechanism, the optimum links shapes are synthesized systematically using closed parametric curve such as cubic B-spline in the second stage. The control points of cubic B-spline curve are taken as the design variables for link shape formation to minimize the percentage error in the resulting link inertia values. The constraints on design variables are defined for both symmetrical and non-symmetrical shapes in the optimization problem formulation. The proposed method of balancing and shape synthesis can be applied to any planar single and multiloop mechanism with revolute as well as prismatic joints. Its effectiveness is demonstrated by applying it to four-bar, five-bar, six-bar and slider-crank mechanisms.

This paper presents a new generic optimization technique for the balancing of the shaking force and shaking moment due to inertia forces in spatial mechanisms. For given dimensions and input speed of a mechanism, the inertia forces depend only upon the mass distribution of the moving links. The equimomental system of seven point-masses is introduced to represent the inertial properties of the links and to identify optimizing variables. The effectiveness of the proposed methodology is illustrated using a spatial RSSR mechanism.

A general mathematical formulation of optimization problem for balancing of planar mechanisms is presented in this paper. The inertia properties of mechanisms are represented by dynamically equivalent systems, referred as equimomental systems, of point-masses to identify design variables and formulate constraints. A set of three equimomental point-masses for each link is proposed. In order to determine the shaking forces and the shaking moments, the dynamic equations of motion for mechanisms are formulated systematically in the parameters related to the equimomental point-masses. The formulation leads to an optimization scheme for the mass distribution to improve the dynamic performances of mechanisms. The method is illustrated with two examples. Balancing of combined shaking force and shaking moment shows a significant improvement in the dynamic performances compared to that of the original mechanisms.

This paper presents a simple, computationally efficient technique for the optimum balancing of four-bar linkages. The methodology is based on the maximum recursiveness of the dynamic equations for the evaluation of bearing forces. Besides, the balancing problem is posed as an optimum problem for which the optimality criteria and the constraints are formulated. Mass distribution of linkage is embedded in the constraints to obtain physically feasible linkage. The formulation is very generic and can be easily extended to multi-loop as well.