Deadzone compensation in motion control systems using adaptive fuzzy logic control

An adaptive fuzzy dead-zone compensation scheme and its application to electro-hydraulic systems

“…The most common approaches are adaptive schemes (Tao and Kokotovi¢, 1994;Wang et al, 2004;Zhou et al, 2006;Ibrir et al, 2007), fuzzy systems (Kim et al, 1994;Oh and Park, 1998;Lewis et al, 1999), neural networks (Šelmić and Lewis, 2000;Tsai and Chuang, 2004;Zhang and Ge, 2007) and variable structure methods (Corradini and Orlando, 2002;Shyu et al, 2005). Many of these works (Tao and Kokotović, 1994;Kim et al, 1994;Oh and Park, 1998;Šelmić and Lewis, 2000;Tsai and Chuang, 2004;Zhou et al, 2006) use an inverse dead-zone to compensate the negative effects of the deadzone nonlinearity, even though this approach leads to a discontinuous control law and requires instantaneous switching, which in practice cannot be accomplished with mechanical actuators.…”

“…Many of these works (Tao and Kokotović, 1994;Kim et al, 1994;Oh and Park, 1998;Šelmić and Lewis, 2000;Tsai and Chuang, 2004;Zhou et al, 2006) use an inverse dead-zone to compensate the negative effects of the deadzone nonlinearity, even though this approach leads to a discontinuous control law and requires instantaneous switching, which in practice cannot be accomplished with mechanical actuators. An alternative scheme, without using the dead-zone inverse, was originally proposed by Lewis et al (1999) and also adopted by Wang et al (2004). In both works, the dead-zone is treated as a combination of a linear and a saturation function.…”

LMI-Based Additive Gain for Guaranteed Cost Control of Robotic Manipulator with Deadzone

“…Therefore, the exact dynamic model to compute the torque such that such nonlinear dynamics of the robotic manipulator system is compensated is necessary. So far, to estimate the deadzone, various reliable approaches such as neural networks, adaptive fuzzy logic control, and iterative learning control have been developed [1], [2], [3]. Particularly, stability proofs and design of deadzone compensator for two-link robot arm using a neural networks (NNs) or fuzzy logic are given in [2], [3], respectively.…”

Feedback Linearization with Fuzzy Compensation for Uncertain Nonlinear Systems

“…For control purposes, equation (22) can be rewritten as a combination of a linear and a saturation function [8,12]:…”