CPG Human Motion Phase Recognition Algorithm for a Hip Exoskeleton with VSA Actuator

Due to the dynamic stiffness characteristics of human joints, it is easy to cause impact and disturbance on normal movements during exoskeleton assistance. This not only brings strict requirements for exoskeleton control design, but also makes it difficult to improve assistive level. The Variable Stiffness Actuator (VSA), as a physical variable stiffness mechanism, has the characteristics of dynamic stiffness adjustment and high stiffness control bandwidth, which is in line with the stiffness matching experiment. However, there are still few works exploring the assistive human stiffness matching experiment based on VSA. Therefore, this paper designs a hip exoskeleton based on VSA actuator and studies CPG human motion phase recognition algorithm. Firstly, this paper puts forward the requirements of variable stiffness experimental design and the output torque and variable stiffness dynamic response standards based on human lower limb motion parameters. Plate springs are used as elastic elements to establish the mechanical principle of variable stiffness, and a small variable stiffness actuator is designed based on the plate spring. Then the corresponding theoretical dynamic model is established and analyzed. Starting from the CPG phase recognition algorithm, this paper uses perturbation theory to expand the first-order CPG unit, obtains the phase convergence equation and verifies the phase convergence when using hip joint angle as the input signal with the same frequency, and then expands the second-order CPG unit under the premise of circular limit cycle and analyzes the frequency convergence criterion. Afterwards, this paper extracts the plate spring modal from Abaqus and generates the neutral file of the flexible body model to import into Adams, and conducts torque-stiffness one-way loading and reciprocating loading experiments on the variable stiffness mechanism. After that, Simulink is used to verify the validity of the criterion. Finally, based on the above criterions, the signal mean value is removed using feedback structure to complete the phase recognition algorithm for the human hip joint angle signal, and the convergence is verified using actual human walking data on flat ground.

AN ALGORITHM OF UNCONSTRAINED MINIMIZATION WITHOUT DERIVATIVE AND ITS CONVERGENCE

In [1] the unconstrained minimization problem was considered and presented an algorithm without derivative. But the terminative conditions and convergence proof of the algorithm were not given. In this paper, we present a revised algorithm and prove its convergence.

NEW HMM ALGORITHM FOR TOPOLOGY OPTIMIZATION

A new hybrid MMA-MGCMMA （HMM） algorithm for solving topology optimization problems is presented. This algorithm combines the method of moving asymptotes （MMA） algorithm and the modified globally convergent version of the method of moving asymptotes （MGCMMA） algorithm in the optimization process. This algorithm preserves the advantages of both MMA and MGCMMA. The optimizer is switched from MMA to MGCMMA automatically, depending on the numerical oscillation value existing in the calculation. This algorithm can improve calculation efficiency and accelerate convergence compared with simplex MMA or MGCMMA algorithms, which is proven with an example.

MERIT FUNCTION AND GLOBAL ALGORITHMFOR BOX CONSTRAINED VARIATIONALINEQUALITIES

The authors consider optimization methods for box constrained variational inequalities. First, the authors study the KKT-conditions problem based on the original problem. A merit function for the KKT-conditions problem is proposed, and some desirable properties of the merit function are obtained. Through the merit function, the original problem is reformulated as minimization with simple constraints. Then, the authors show that any stationary point of the optimization problem is a solution of the original problem. Finally, a descent algorithm is presented for the optimization problem, and global convergence is shown.

Proximal point algorithm for a new class of fuzzy set-valued variational inclusions with (H,η)-monotone mappings

We introduced a new class of fuzzy set-valued variational inclusions with (H,η)-monotone mappings. Using the resolvent operator method in Hilbert spaces, we suggested a new proximal point algorithm for finding approximate solutions, which strongly converge to the exact solution of a fuzzy set-valued variational inclusion with (H,η)-monotone. The results improved and generalized the general quasi-variational inclusions with fuzzy set-valued mappings proposed by Jin and Tian Jin MM, Perturbed proximal point algorithm for general quasi-variational inclusions with fuzzy set-valued mappings, OR Transactions, 2005, 9(3): 31-38, (In Chinese); Tian YX, Generalized nonlinear implicit quasi-variational inclusions with fuzzy mappings, Computers & Mathematics with Applications, 2001, 42: 101-108.

Relative efficiency appraisal of discrete choice modeling algorithms using small-scale maximum likelihood estimator through empirically tailored computing environment

Discrete choice models are widely used in multiple sectors such as transportation, health, energy, and marketing, etc., where the model estimation is usually carried out by using commercial software. Nonetheless, tailored computer codes offer modellers greater flexibility and control of unique modelling situation. Aligned with empirically tailored computing environment, this research discusses the relative performance of six different algorithms of a discrete choice model using three key performance measures： convergence time, number of iterations, and iteration time. The computer codes are developed by using Visual Basic Application （VBA）. Maximum likelihood function （MLF） is formulated and the mathematical relationships of gradient and Hessian matrix are analytically derived to carry out the estimation process. The estimated parameter values clearly suggest that convergence criterion and initial guessing of parameters are the two critical factors in determining the overall estimation performance of a custom-built discrete choice model.

Linear convergence of an algorithm for largest singular value of a nonnegative rectangular tensor

An algorithm for finding the largest singular value of a nonnegative rectangular tensor was recently proposed by Chang, Qi, and Zhou [J. Math. Anal. Appl., 2010, 370： 284-294]. In this paper, we establish a linear conver- gence rate of the Chang-Qi-Zhou algorithm under a reasonable assumption.

SOME IMPROVED PROJECTED QUASI-NEWTON ALGORITHMS AND THEIR CONVERGENCE Ⅱ.LOCAL CONVERGENCE RATE AND NUMERICAL TESTS

For the improved two-sided projected quasi-Newton algorithms, which were presented in PartI, we prove in this paper that they are locally one-step or two-step superlinearly convergent. Numerical tests are reported thereafter. Results by solving a set of typical problems selectedfrom literature have demonstrated the extreme importance of these modifications in making Nocedal& Overton's original methon practical. Furthermore, these results show that the improved algoritnmsare very competitive in comparison with some highly praised sequential quadratic programmingmethods.

Convergence analysis of filtered-X LMS algorithm with secondary path modeling error

A more relaxed sufficient condition for the convergence of filtered-X LMS (FXLMS) algorithm is presented. It is pointed out that if some positive real condition for secondary path transfer function and its estimates is satisfied within all the frequency bands, FXLMS algorithm converges whatever the reference signal is like. But if the above positive real condition is satisfied only within some frequency bands, the convergence of FXLMS algorithm is dependent on the distribution of power spectral density of the reference signal, and the convergence step size is determined by the distribution of some specific correlation matrix eigenvalues.Applying the conclusion above to the Delayed LMS (DLMS) algorithm, it is shown that DLMS algorithm with some error of time delay estimation converges in certain discrete frequency bands, and the width of which are determined only by the 'time-delay estimation error frequency' which is equal to one fourth of the inverse of estimated error of the time delay.

Coupled modified Kd V equations, skew orthogonal polynomials, convergence acceleration algorithms and Laurent property

In this paper, we show that the coupled modified Kd V equations possess rich mathematical structures and some remarkable properties. The connections between the system and skew orthogonal polynomials,convergence acceleration algorithms and Laurent property are discussed in detail.

SOME IMPROVED PROJECTED QUASI-NEWTON ALGORITHMS AND THEIR CONVERGENCE Ⅰ.METHODS AND GLOBAL BEHAVIOR

In this paper we improve the two versions of the two-sided projected quasi-Newton method-onewas proposed by Nocedal & Overton in [1] and the other was discussed in our previous paper, byintroducing three different merit functions to make inexact one-dimensional searches. It is shown that these improved quasi-Newton algorithms have gained global convergence propertywhich is not possessed by the original two algorithms.

AN OVERALL STUDY OF CONVERGENCE CONDITIONS FOR ALGORITHMS IN NONLINEAR PROGRAMMING

Since the point-to-set maps were introduced by Zangwill in the study of conceptual algorithms, various sufficient conditions for the algorithms to be of global convergence have been established.In this paper, the relations among all these conditions are illustrated by a unified approach;still more, unlike the sufficient conditions previously given in the literature,a new necessary condition is put forward at the end of the paper, so that it implies more applications.

An Algorithm to Solve Linear Bilevel Programs

Linear bilevel programming deals with optimization problems in which the constraint region is implicitly determined by another optimization problem. This paper presents a new algorithm for solving linear bilevel programs. A numerical example is given to illustrate this method.

A Numerical Algorithm for Arbitrary Real-Order Hankel Transform

The Hankel transform is widely used to solve various engineering and physics problems,such as the representation of electromagnetic field components in the medium,the representation of dynamic stress intensity factors,vibration of axisymmetric infinite membrane and displacement intensity factors which all involve this type of integration.However,traditional numerical integration algorithms cannot be used due to the high oscillation characteristics of the Bessel function,so it is particularly important to propose a high precision and efficient numerical algorithm for calculating the integral of high oscillation.In this paper,the improved Gaver-Stehfest(G-S)inverse Laplace transform method for arbitrary real-order Bessel function integration is presented by using the asymptotic characteristics of the Bessel function and the accumulation of integration,and the optimized G-S coefficients are given.The effectiveness of the algorithm is verified by numerical examples.Compared with the linear transformation accelerated convergence algorithm,it shows that the G-S inverse Laplace transform method is suitable for arbitrary real order Hankel transform,and the time consumption is relatively stable and short,which provides a reliable calculation method for the study of electromagnetic mechanics,wave propagation,and fracture dynamics.

A GLOBALLY CONVERGENT ALGORITHM FOR THE EUCLIDEAN MULTIPLICITY LOCATION PROBLEM

The Euclidean single facility location problem (ESFL) and the Euclidean multiplicity lo-cation problem (EMFL) are two special nonsmooth convex programming problems which haveattracted a largr literature. For the ESFL problem. there are algorithms which converge bothglobally and quadratically For the EMFL problem, there are some quadratically convergentalgorithms. but for global convergencel they all need nontrivial assumptions on the problem.In this paper, we present an algorithm for EMFL. With no assumption on the problem, it isproved that from any initial point, this algorithm generates a sequence of points which convergesto the closed convex set of optimal solutions of EMFL.