Backstepping-Based Hybrid Adaptive Control of Robot Manipulators Incorporating Actuator Dynamics

Su, Chun‐Yi; Stepanenko, Y.

doi:10.1002/(sici)1099-1115(199703)11:2<141::aid-acs434>3.0.co;2-i

Cited by 39 publications

(34 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The applied torque O K 2 I for the mechanical subsystem in (10) is a synthesized vector and depends on the output, the current signal I(t), of the electrical subsystem. From the concept of virtually applied force in the integrator backstepping technique [11,13,18], let I* denote the desired value of the current signal I(t) and de"ne…”

Section: Controller Design and Stability Analysismentioning

confidence: 99%

“…Substituting (27) into (19), the derivative of xN can be modi"ed aş (21) along the error trajectories (9), (17), (18), and (29), we get…”

Section: Q Qr and I Are Measurable For Feedbackmentioning

confidence: 99%

“…Finally, suppose that the disturbances d and d are also bounded functions. From the closed-loop error system (9), (17), (18), and (29), it is clear that #xN Q (t)#, #xN Q (t)#, #eN Q (t)# and #xN Q (t)# are all uniformly bounded. This implies that xN (t), xN (t) eN (t), and xN (t) are all uniformly continuous.…”

Section: Q Qr and I Are Measurable For Feedbackmentioning

confidence: 99%

“…By utilizing a dynamic "lter and the desired compensation adaptation law (DCAL) algorithm, this design procedure does not require a substitution of the mechanical dynamics into the current tracking error dynamics. But, it should be noted that in order to compensate the e!ect of the uncertain electromechanical conversion matrix, this design procedure (see also in References [17,18]) employs the product of an augmented regressor matrix and an augmented mechanical parameter vector to learn the behaviorus of the uncertain dynamics in the mechanical subsystem. Here, as many as nn + estimates where n + denotes the number of unknown mechanical parameters and n denotes the order of the mechanical subsystem, must be continuously updated.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive tracking control for electrically‐driven robots without overparametrization

Chang

2002

Adaptive Control & Signal

View full text Add to dashboard Cite

This paper addresses the motion tracking control of robot systems actuated by brushed direct current motors in the presence of parametric uncertainties and external disturbances. By using the integrator backstepping technique, two kinds of adaptive control schemes are developed: one requires the measurements of link position, link velocity and armature current for feedback and the other requires only the measurements of link position and armature current for feedback. The developed adaptive controllers guarantee that the resulting closed-loop system is locally stable, all the states and signals are bounded, and the tracking error can be made as small as possible. The attraction region can be not only arbitrarily preassigned but also explicitly constructed. The main novelty of the developed adaptive control laws is that the number of parameter estimates is exactly equal to the number of unknown parameters throughout the entire electromechanical system. Consequently, the phenomenon of overparametrization, a signi"cant drawback of employing the integrator backstepping technique to treat the control of electrically driven robots in the previous literature, is eliminated in this study. Finally, simulation examples are given to illustrate the tracking performance of electrically driven robot manipulators with the developed adaptive control schemes.

show abstract

Section: Controller Design and Stability Analysismentioning

confidence: 99%

“…Substituting (27) into (19), the derivative of xN can be modi"ed aş (21) along the error trajectories (9), (17), (18), and (29), we get…”

Section: Q Qr and I Are Measurable For Feedbackmentioning

confidence: 99%

Section: Q Qr and I Are Measurable For Feedbackmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive tracking control for electrically‐driven robots without overparametrization

Chang

2002

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…Backstepping control as an efficient control method of nonlinear systems can provide a systematic framework for the tracking control of robot manipulators. Its design flexibility has led to the robust adaptive backstepping control being extensively studied in the nonlinear control system design including the robust control design of robot manipulators [61,62].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation

Mei

Cheng-jiang

2017

Complexity

View full text Add to dashboard Cite

This paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN) and disturbance observer. First, the RBFNN is used to approximate the system uncertainty, and the unknown approximation error of the RBFNN and the time-varying unknown external disturbance of robot manipulators are integrated as a compounded disturbance. Then, the state observer and the disturbance observer are proposed to estimate the unmeasured system state and the unknown compounded disturbance based on RBFNN. At the same time, the adaptation technique is employed to tackle the control input saturation problem. Utilizing the estimate outputs of the RBFNN, the state observer, and the disturbance observer, the adaptive neural output feedback control scheme is developed for robot manipulators using the backstepping technique. The convergence of all closed-loop signals is rigorously proved via Lyapunov analysis and the asymptotically convergent tracking error is obtained under the integrated effect of the system uncertainty, the unmeasured system state, the unknown external disturbance, and the input saturation. Finally, numerical simulation results are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for uncertain robot manipulators.

show abstract

Model Reference Adaptive Control

Yucelen

2019

Wiley Encyclopedia of Electrical and Electronics Engineering

View full text Add to dashboard Cite

Adaptive control methods present effective system‐theoretical tools in order to achieve closed‐loop system stability and performance in the presence of exogenous disturbances and system uncertainties, where they are generally classified as either direct or indirect. A well‐known class of direct adaptive control methods is model reference adaptive control architectures. In particular, these architectures employ two major components – a reference model and a parameter adjustment mechanism. A desired closed‐loop dynamical system response is captured by the reference model for which its response is compared with the response of the uncertain dynamical system. The system error signal resulting from this comparison drives the parameter adjustment mechanism. This mechanism then adjusts the controller parameters in a real‐time (i.e., online) manner for driving the trajectories of the uncertain dynamical system to the trajectories of the reference model. This article discusses these components in a basic state feedback setting in order to provide an introduction to the model reference adaptive control design procedure. The article also makes connections to several other, relatively advanced model reference adaptive control methods for interested readers.

show abstract

Backstepping-Based Hybrid Adaptive Control of Robot Manipulators Incorporating Actuator Dynamics

Cited by 39 publications

References 19 publications

Adaptive tracking control for electrically‐driven robots without overparametrization

Adaptive tracking control for electrically‐driven robots without overparametrization

Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation

Model Reference Adaptive Control

Contact Info

Product

Resources

About