Abstract
State variables are acquired when tracking the trace of the robotic arm with adaptive fuzzy controller. Since some variables are difficult to or cannot be measured directly, we introduced the second order oscillator and the second order differentiator that converges in finite time to obtain the value of each state variable. In this paper, a model based on the dynamics analysis of robotic arm was build to design the second order oscillator and the second order differentiator that converges in finite time to obtain the value of each state variable. The designed adaptive fuzzy controller for robotic arm achieved high accuracy in trace tracking. Simulation results of two-link robotic arm show the adaptive fuzzy controller for robotic arm based on differentiators is adaptable, flexible. This controller is simple to design, easy to implement, and has a good value for the application of robotic arm system.
Highlights
Robotic arm has been developing continuously; it can do a lot of repeated, monotonous long-hour works or high-risk jobs in harsh environments or instead of mankind [1]
Fuzzy system does not need to rely on accurate mathematical model, in paper [3] [4] adaptive fuzzy controller was used in tracking the robotic arm and achieved good results
Hypothesis 1 can guarantee that the inverse of G( x) exists, Formula (2) can be expressed as a linear form of the static state feedback, assumptions are strictly limited to a MIMO system, the robotic arm system can satisfy the following assumptions: Define trajectory tracking error as e1 (t ) = yd 1 (t ) - y1 (t )
Summary
Robotic arm has been developing continuously; it can do a lot of repeated, monotonous long-hour works or high-risk jobs in harsh environments or instead of mankind [1]. In order to make the robotic arm accomplish the movement in the specified path, its trace must be tracked [2]. Robotic arm system is a complex multiple-input-multiple-output nonlinear system with significant coupling, and there exist load changes, stochastic disturbances, measurement error and many other uncertainties. It is difficult to build an accurate mathematical model of the robotic arm. Fuzzy system does not need to rely on accurate mathematical model, in paper [3] [4] adaptive fuzzy controller was used in tracking the robotic arm and achieved good results. When adopting the fuzzy adaptive methods the condition of the system state is assumed that it can be measured directly, [5] is based on the assumption that the system state can be measured directly. The adaptive fuzzy controller realized high-precision tracking of robotic arm
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