Trajectory tracking control for perturbed robot manipulators using iterative learning method

Bouakrif, Farah; Zasadzinski, Michel

doi:10.1007/s00170-016-8550-3

Cited by 32 publications

(31 citation statements)

References 27 publications

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“…In this subsection, some numerical results are investigated and analyzed through a spacecraft with one axis of symmetry model 1,19 and 2-degree-of-freedom manipulator. 26 The simulation results report that the optimal feedback controller which is feasible and effective for nonlinear system.…”

Section: Simulationsmentioning

confidence: 90%

“…In the future works, the developed unified framework may be extended to address optimal feedback controllers and rapid exponential stability for nonlinear hybrid systems and nonlinear stochastic systems, for instance, bipedal walking robot systems and stochastic systems. In addition, the proposed sufficient conditions of this article may open a door to the performance improvement of the related applications, such as nontriangular stochastic nonlinear systems, 22 switched stochastic nonlinear systems, 25 robot control, 26,29,30 and path planning, 28 with great capacity in accelerating convergence and computing accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the page limit, the detailed descriptions are omitted in this article but can be seen in Bouakrif and Zasadzinski. 26 Equation (42) can be transformed into the following controlled nonlinear affine system As shown in Figure 12, the desired trajectories are dashed blue lines and the actual trajectories are dotted red lines. It is can be seen that the proposed approach of this article is feasible and effective.…”

Section: Two-degree-of-freedom Manipulatormentioning

confidence: 99%

“…The trajectories of 2-degree-of-freedom manipulator:26 (a) trajectory q 1 versus time t and (b) trajectory q 2 versus time t. Performance comparisons of this article and the literature26 for soling time-varying 2-degree-of-freedom manipulator: (a, c) error q 1 versus time t and (b, d) error q 2 versus time t.…”

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confidence: 99%

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A unified framework of rapid exponential stability and optimal feedback control for nonlinear systems

2019

Advances in Mechanical Engineering

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A novel framework of rapid exponential stability and optimal feedback control is investigated and analyzed for a class of nonlinear systems through a variant of continuous Lyapunov functions and Hamilton-Jacobi-Bellman equation. Rapid exponential stability means that the trajectories of nonlinear systems converge to equilibrium states in accelerated time. The sufficient conditions of rapid exponential stability are developed using continuous Lyapunov functions for nonlinear systems. Furthermore, according to a variant of continuous Lyapunov functions, a rapid exponential stability is guaranteed which satisfies some canonical conditions and Hamilton-Jacobi-Bellman equation for controlled nonlinear systems. It is can be seen that the solution of Hamilton-Jacobi-Bellman equation is a continuous Lyapunov function, and, therefore, rapid exponential stability and optimality are guaranteed for nonlinear systems. Last, the main result of this article is investigated via a nonlinear model of a spacecraft with one axis of symmetry through simulations and is used to check rapid exponential stability. Moreover, for the disturbance problem of initial point, a rapid exponential stable controller can reject the large-scale disturbances for controlled nonlinear systems. In addition, the proposed optimal feedback controller is applied to the tracking trajectories of 2-degree-of-freedom manipulator, and the numerical results have illustrated high efficiency and robustness in real time. The simulation results demonstrate the use of the rapid exponential stability and optimal feedback approach for real-time nonlinear systems.

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Section: Simulationsmentioning

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Two-degree-of-freedom Manipulatormentioning

confidence: 99%

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confidence: 99%

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A unified framework of rapid exponential stability and optimal feedback control for nonlinear systems

2019

Advances in Mechanical Engineering

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“…For example in Reference [33], it has been shown that the nonrepetitive disturbance can deteriorate the system performance. In References [34][35][36][37], the nonrepetitive disturbances are assumed to be norm bounded by a known constant. In this article, the unknown disturbances are assumed to be nonlinear time varying and also varying from iteration to iteration.…”

Section: Introductionmentioning

confidence: 99%

Adaptive P‐type iterative learning radial basis function control for robot manipulators with unknown varying disturbances and unknown input dead zone

Bensidhoum

Bouakrif

2020

Intl J Robust & Nonlinear

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This article proposes an adaptive iterative learning radial basis function (RBF) scheme to solve the trajectory-tracking problem for perturbed robot manipulators with unknown iteration varying disturbances and unknown dead-zone input. It is well known that the presence of the dead zone in actuators and mechatronics devices gives rise to extra difficulty due to the presence of singularity in the input channels. Hence, it is interesting to take this problem into account when synthesizing a controller. This synthesis is made here. In addition, the control design is very simple in the sense that we use, only, the proportional gain. Therefore, the considerable amount noise caused by the sensors for velocity measurements of robot manipulators is avoided. Another advantage of this work is that the unknown disturbances are assumed to be time varying and also varying from iteration to iteration. Thus, the RBF neural network is used to approximate these unknown nonlinear functions. Using the Lyapunov theory, the analysis of the stability of the closed-loop system is guaranteed when the iteration number tends to infinity. Finally, simulation results on the PUMA 560 arm are provided to illustrate the effectiveness of the proposed method. In order to evaluate the performance of our controller, a comparison of our results with other method is also given. K E Y W O R D SIterative learning control, Radial basis function, Dead-zone, Adaptive, Robot manipulators, Lyapunov theory 1 Indeed, input uncertainties such as dead-zone, saturation, backlash, and hysteresis are kind of nonsmooth and nonaffine in input factor widely existing in actuators and sensors. In fact, in many practical applications, the presence of Int J Robust Nonlinear Control. 2020;30:4075-4094.wileyonlinelibrary.com/journal/rnc

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Model predictive controller–based spatiotemporal path tracking method for transhumeral prostheses

Madusanka

Gopura

Amarasinghe

et al. 2019

Robotics Computer Surgery

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BackgroundTranshumeral prostheses are worn by transhumeral amputees to replace the missing upper limb segment between shoulder and elbow. Prostheses should be able to function as a natural limb for the user to gain the full advantage of wearing a prosthesis. When performing reach‐to‐grasp and pointing motions by the upper limb, the hand is capable of adhering to a straight‐line path with a bell‐shaped velocity profile.AimAim was to develop a dynamic path‐tracking method for transhumeral prostheses to gain the capability of adhering to a straight‐line path.MethodProposed method uses model predictive controller (MPC) developed based on the kinematic model of the prosthesis. Moreover, a shoulder matcher is proposed to match actual shoulder pose with the predicted shoulder pose and to select the best joint angles for the prosthesis for a particular instance. Furthermore, the proposed method is capable of dynamically updating the path if the human performs shoulder motions, which are not as planned by the MPC.ResultsSeveral experiments are conducted to validate the proposed method. The proposed method is capable of taking a straight‐line path similar to a natural human.ConclusionThis paper proposed a dynamic path‐tracking method based on a model predictive controller. The proposed method is capable of taking the prosthetic hand on a straight‐line path, which is similar to a path taken by a natural human hand.

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Trajectory tracking control for perturbed robot manipulators using iterative learning method

Cited by 32 publications

References 27 publications

A unified framework of rapid exponential stability and optimal feedback control for nonlinear systems

A unified framework of rapid exponential stability and optimal feedback control for nonlinear systems

Adaptive P‐type iterative learning radial basis function control for robot manipulators with unknown varying disturbances and unknown input dead zone

Model predictive controller–based spatiotemporal path tracking method for transhumeral prostheses

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