Haptic Identification by ELM-Controlled Uncertain Manipulator

Abstract

Abstract-This paper presents an extreme learning machine (ELM) based control scheme for uncertain robot manipulators to perform haptic identification. ELM is used to compensate for the unknown nonlinearity in the manipulator dynamics. The ELM enhanced controller ensures that the closed-loop controlled manipulator follows a specified reference model, in which the reference point as well as the feedforward force is adjusted after each trial for haptic identification of geometry and stiffness of an unknown object. A neural learning law is designed to ensure finite-time convergence of the neural weight learning, such that exact matching with the reference model can be achieved after the initial iteration. The usefulness of the proposed method is tested and demonstrated by extensive simulation studies.

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

“…According to Theorems 6 and 9, the main objective of this section is to use the proposed stable adaptive neural controller (22) with virtual control law (15) and neural weight adaptation law (23) such that the full-state tracking errors 1 and 2 satisfy the prescribed performance (51); the neural weight estimator̂exponentially converges to the constant weight value ; the unknown system dynamics Φ( ) in (20) is accurately approximated by the constant RBF NNs ( ). In the simulation studies, the RBF network given in Figure 6 full-state tracking performance constraints, the simulation comparison is given between the proposed method and the existing method without prescribed performances [30].…”

“…where 20 and 21 are positive design constants, Γ > 0 is positive diagonal matrix, and > 0 is a small value, which is used to improve the robustness of the adaptive controller (22).…”

“…Consider the closed-loop system consisting of the robotic system (1) with Assumption 1, the bounded reference model (2), full-state tracking error constrained condition (3), the transformation error (10) and adaptive neural control law (22) with the virtual control law (15), and the NN updated law (23 Proof. Up to now, we have verified that ( ) satisfies the PE condition.…”

“…Using the stored knowledge in (39), a static control law without neural weight estimated parameter adjustment online, instead of the adaptive NN control law (22), is designed as follows:…”

“…In order to enhance system robustness on the uncertain parameters in the presence of external disturbances, sliding mode control [8,9] has been proposed to obtain the desired robotic tracking control performance. Owing to the universal approximation property [10][11][12][13][14][15][16][17], a great number of intelligent control schemes, such as adaptive neural/fuzzy control, have been developed for controlling robotic systems with uncertain nonlinearities [18][19][20][21][22].…”