IEEE Trans Neural Netw Learn Syst
August 2024
Model-based impedance learning control can provide variable impedance regulation for robots through online impedance learning without interaction force sensing. However, the existing related results only guarantee the closed-loop control systems to be uniformly ultimately bounded (UUB) and require the human impedance profiles being periodic, iteration-dependent, or slowly varying. In this article, a repetitive impedance learning control approach is proposed for physical human-robot interaction (PHRI) in repetitive tasks.
View Article and Find Full Text PDFThe existing model-based impedance learning control methods can provide variable impedance regulation for physical human-robot interaction (PHRI) in repetitive tasks without interactive force sensing, however, these methods require the completion of the repetitive tasks with constant time, which restricts their applications. For PHRI in repetitive tasks with different completion time, this paper proposes a spatial hybrid adaptive impedance learning control (SHAILC) strategy by using the spatial periodic characteristics of the tasks. In the spatial hybrid adaptation, spatial periodic adaptation is used for estimating time-varying human impedance and differential adaptation is designed for estimating robotic constant unknown parameters.
View Article and Find Full Text PDFSensors (Basel)
December 2022
This paper presents an impedance learning-based adaptive control strategy for series elastic actuator (SEA)-driven compliant robots without the measurement of the robot-environment interaction force. The adaptive controller is designed based on the command filter-based adaptive backstepping approach, where a command filter is used to decrease computational complexity and avoid the requirement of high derivatives of the robot position. In the controller, environmental impedance profiles and robotic parameter uncertainties are estimated using adaptive learning laws.
View Article and Find Full Text PDFBackground: Human gait involves activities in nervous and musculoskeletal dynamics to modulate joint torques with time continuously for adapting to varieties of walking conditions.
Objective: The goal of this paper is to estimate the joint torques of lower limbs in human gait based on Gaussian process.
Method: The potential uses of this study include optimization of exoskeleton assistance, control of the active prostheses, and modulating the joint torque for human-like robots.
This paper proposes a finite-time multi-modal robotic control strategy for physical human-robot interaction. The proposed multi-modal controller consists of a modified super-twisting-based finite-time control term that is designed in each interaction mode and a continuity-guaranteed control term. The finite-time control term guarantees finite-time achievement of the desired impedance dynamics in active interaction mode (AIM), makes the tracking error of the reference trajectory converge to zero in finite time in passive interaction mode (PIM), and also guarantees robotic motion stop in finite time in safety-stop mode (SSM).
View Article and Find Full Text PDFThis paper proposes a saturated smooth adaptive controller for regulating a certain type of underactuated Euler-Lagrange systems (UELSs) with modeling uncertainties and control saturations based on a singular perturbation approach. Compared with relevent literature, the advantages of the proposed controller include: (1) it renders the UELS semiglobally asymptotically track the desired position without the violation of control input constraints; (2) high-order derivatives of positions are not required in its implementation. The Hoppensteadt's Theorem is employed to show that the proposed saturated controller renders the UELS semiglobally asymptotically stable about the desired set point with the satisfaction of control input constraints.
View Article and Find Full Text PDFThe desired impedance dynamics can be achieved for a robot if and only if an impedance error converges to zero or a small neighborhood of zero. Although the convergence of impedance errors is important, it is seldom obtained in the existing impedance controllers due to robots modeling uncertainties and external disturbances. This brief proposes two composite learning impedance controllers (CLICs) for robots with parameter uncertainties based on whether a factorization assumption is satisfied or not.
View Article and Find Full Text PDFIn existing neural network (NN) learning control methods, the trajectory of NN inputs must be recurrent to satisfy a stringent condition termed persistent excitation (PE) so that NN parameter convergence is obtainable. This paper focuses on command-filtered backstepping adaptive control for a class of strict-feedback nonlinear systems with functional uncertainties, where an NN composite learning technique is proposed to guarantee convergence of NN weights to their ideal values without the PE condition. In the NN composite learning, spatially localized NN approximation is employed to handle functional uncertainties, online historical data together with instantaneous data are exploited to generate prediction errors, and both tracking errors and prediction errors are employed to update NN weights.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
December 2015
High-gain observers have been extensively applied to construct output-feedback adaptive neural control (ANC) for a class of feedback linearizable uncertain nonlinear systems under a nonlinear separation principle. Yet due to static-gain and linear properties, high-gain observers are usually subject to peaking responses and noise sensitivity. Existing adaptive neural network (NN) observers cannot effectively relax the limitations of high-gain observers.
View Article and Find Full Text PDFIt is well-known that standard adaptive fuzzy control (AFC) can only guarantee uniformly ultimately bounded stability due to inherent fuzzy approximation errors (FAEs). This paper proves that standard AFC with proportional-derivative (PD) control can guarantee global asymptotic stabilization even in the presence of FAEs for a class of uncertain affine nonlinear systems. Variable-gain PD control is designed to globally stabilize the plant.
View Article and Find Full Text PDFThis paper presents a methodology of asymptotically synchronizing two uncertain generalized Lorenz systems via a single continuous composite adaptive fuzzy controller (AFC). To facilitate controller design, the synchronization problem is transformed into the stabilization problem by feedback linearization. To achieve asymptotic tracking performance, a key property of the optimal fuzzy approximation error is exploited by the Mean Value Theorem.
View Article and Find Full Text PDF