This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input deadzone.Co...This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input deadzone.Considering the model complexity and input deadzone,a fractional-order ultra-local model is proposed to formulate the original dynamic system for simple controller design.Firstly,the control gain of ultra-local model is considered as a constant.The fractional-order sliding mode technique is designed to stabilize the closed-loop system,while fractional-order time-delay estimation is combined with neural network to estimate the lumped disturbance.Correspondingly,a fractional-order ultra-local model-based neural network sliding mode controller(FO-NNSMC) is proposed.Secondly,to avoid disadvantageous effect of improper gain selection on the control performance,the control gain of ultra-local model is considered as an unknown parameter.Then,the Nussbaum technique is introduced into the FO-NNSMC to deal with the stability problem with unknown gain.Correspondingly,a fractional-order ultra-local model-based adaptive neural network sliding mode controller(FO-ANNSMC) is proposed.Moreover,the stability analysis of the closed-loop system with the proposed method is presented by using the Lyapunov theory.Finally,with the co-simulations on virtual prototype of 7-DOF iReHave upper-limb exoskeleton and experiments on 2-DOF upper-limb exoskeleton,the obtained compared results illustrate the effectiveness and superiority of the proposed method.展开更多
In this paper we introduce a numerically stable method for determining the stability of n-DOF system without computing eigenvalues. In this sense, at first we reduce the second-order system to a standard eigenvalue pr...In this paper we introduce a numerically stable method for determining the stability of n-DOF system without computing eigenvalues. In this sense, at first we reduce the second-order system to a standard eigenvalue problem with symmetric tridiagonal form. Then we compute the exact inertia by using an algorithm based on floating point arithmetic [1]. Numerical tests report the effectiveness of these methods.展开更多
The transformation process of an m-DOF free-floating robot from one staticstate to a different static state has m degrees of freedom. The proposed approach of thesetransformations utilizes a series of single-DOF trans...The transformation process of an m-DOF free-floating robot from one staticstate to a different static state has m degrees of freedom. The proposed approach of thesetransformations utilizes a series of single-DOF transformation processes as an alternative to them-DOF transformation process. Two static state transformation processes are studied in detail.First, a single-DOF transformation process is established using a newly defined concept, referred toas transformation planning, and the definite integral of conservation of angular momentum. Second,the governing equation of the single-DOF transformation process is established using the dynamicequations of motion of the robot. This allows the joint torques to be computed to effect the statetransformation. Finally, an extension of the single-DOF transformation process is proposed to extendthe application of this proposed transformation methodology to create a transformation net whichallows the reconfiguration of a robot from one state to many other possible states.展开更多
基金supported in part by the National Natural Science Foundation of China (62173182,61773212)the Intergovernmental International Science and Technology Innovation Cooperation Key Project of Chinese National Key R&D Program (2021YFE0102700)。
文摘This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input deadzone.Considering the model complexity and input deadzone,a fractional-order ultra-local model is proposed to formulate the original dynamic system for simple controller design.Firstly,the control gain of ultra-local model is considered as a constant.The fractional-order sliding mode technique is designed to stabilize the closed-loop system,while fractional-order time-delay estimation is combined with neural network to estimate the lumped disturbance.Correspondingly,a fractional-order ultra-local model-based neural network sliding mode controller(FO-NNSMC) is proposed.Secondly,to avoid disadvantageous effect of improper gain selection on the control performance,the control gain of ultra-local model is considered as an unknown parameter.Then,the Nussbaum technique is introduced into the FO-NNSMC to deal with the stability problem with unknown gain.Correspondingly,a fractional-order ultra-local model-based adaptive neural network sliding mode controller(FO-ANNSMC) is proposed.Moreover,the stability analysis of the closed-loop system with the proposed method is presented by using the Lyapunov theory.Finally,with the co-simulations on virtual prototype of 7-DOF iReHave upper-limb exoskeleton and experiments on 2-DOF upper-limb exoskeleton,the obtained compared results illustrate the effectiveness and superiority of the proposed method.
文摘In this paper we introduce a numerically stable method for determining the stability of n-DOF system without computing eigenvalues. In this sense, at first we reduce the second-order system to a standard eigenvalue problem with symmetric tridiagonal form. Then we compute the exact inertia by using an algorithm based on floating point arithmetic [1]. Numerical tests report the effectiveness of these methods.
文摘The transformation process of an m-DOF free-floating robot from one staticstate to a different static state has m degrees of freedom. The proposed approach of thesetransformations utilizes a series of single-DOF transformation processes as an alternative to them-DOF transformation process. Two static state transformation processes are studied in detail.First, a single-DOF transformation process is established using a newly defined concept, referred toas transformation planning, and the definite integral of conservation of angular momentum. Second,the governing equation of the single-DOF transformation process is established using the dynamicequations of motion of the robot. This allows the joint torques to be computed to effect the statetransformation. Finally, an extension of the single-DOF transformation process is proposed to extendthe application of this proposed transformation methodology to create a transformation net whichallows the reconfiguration of a robot from one state to many other possible states.
