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In this paper, we presents a new nonlinear iterative learning control law for a class of nonlinear systems, denoted NP-D-INP-D, which is composed by limiting the action of error in PID learning law, that is, use a bounded nonlinear function of error instead of error, and adding a differential feedback in the integrator of PID learning law to inject the suited damping. By using Bellman-Gronwall lemma,...
In this paper, a passivity-based control scheme for contour following applications is developed. However, the original passive velocity field control requires an accurate dynamic model. To cope with this problem, in the proposed approach an alternative control law design based on the Lyapunov function is employed to speed up the convergence rate of velocity error. Moreover, in order to deal with the...
A nonlinear reference shaping method for manipulators which are operated in living environments is proposed. It generates an intermediate reference position, and by connecting the position and the manipulator's endpoint with virtual spring and damper, the robot acceleration is moderated and smooth reaching motion is realized. By feeding back the hand position of the manipulator to the proposed reference...
A neural network training method for identification in bounded time of nonlinear systems is presented in this paper. A sliding mode surface drives the adalines, perceptrons and multilayer perceptrons so as to a new second order sliding mode is enforced for all time. This neural network-based sliding mode enforces an invariant differential manifold, with a time-varying feedback gain to give rise to...
This paper proposes a method to suboptimally tune the control parameters in a conventional Lyapunov-based method which shares the same concept of control design with sliding mode approach as applied to the robot manipulators. Optimal tuning of such parameters involves handling of nonlinearities in system dynamics and cost functions, which makes the problem challenging. We propose a step-by-step numerical...
In this paper, a new iterative learning algorithm is proposed for repetitive nonlinear systems. The control system employs a combination of state feedback and iterative learning control (ILC) in which the coefficients of states are learned similar to ILC methods. The control system is in a closed loop format both in iteration domain (because of ILC) and in time domain (because of feedback control)...
Model predictive control (MPC) an optimization-based approach that decides a control input by the optimal computation as the system output tracks the reference trajectory which is the ideal trajectory while the system output converges on the desired value. In this paper, a tracking controller for the two-link manipulator on the horizontal space via nonlinear model predictive control (NMPC) is proposed...
We propose a globally convergent observer for three-state nonlinear systems verifying the uniform complete observability condition. By constructing a time-varying differentiator, we are then able to reproduce the first and the second derivatives of the system output without imposing the boundedness of the states or the output. By exploiting the algebraic observability of the system, we show that the...
A novel second-order update law proposed in this paper, whose coefficients are decided by a linear matrix differential equation with the regressors as input, achieves uniform asymptotic stability parameters convergence as well as motion tracking error of robot manipulators, if the persistency of excitation condition is satisfied. This algorithm is simpler for the execution. Based on this adaptive...
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