Abstract
The learning space for executing general motions of a flexible joint manipulator is quite large and the dynamics are, in general, nonlinear, time-varying, and complex. The objective of this paper is to design a nonlinear system based on the fuzzy neural network control using supervised training, into executing reference trajectories by a flexible joint manipulator. The structure identifications of controller networks are performed by using the Adaptive Neural Fuzzy Inference System (ANFIS), with new parameters and weight coefficients automatically adapted and adjusted, in order to decrease position tracking errors. In order to adapt and reduce the number of undefined parameters in the network, a new technique is used. Reported research works use the Euler method for the resolution of the arm's dynamic function, in this paper, a more exact method was used, represented by the Fourth-Order Runge-Kutta (RK4) method. A comparative study has been carried out between these two methods in order to prove the effectiveness of the later. Finally, in order to test the robustness of the proposed approach, it was also investigated considering parameter variations. The tracking speed of the model on the system control accuracy was also analyzed. The simulation results show that the proposed approach has a good tracking effect.
Highlights
The determination of the optimum number of rules is very important in fuzzy modeling, it can be determined by a structure identification scheme [26], and the parameters of the initial fuzzy inference system are tuned in Adaptive Neural Fuzzy Inference System (ANFIS) architecture
This paper presents a solution for the problem of learning and controlling a 2DoF industrial manipulator
One of the major problems in applying learning controllers to govern general motions is that the dynamics of robot manipulators are, in general, nonlinear, time-varying, and complex, which makes implementation in real time difficult
Summary
Most controllers are not able of effectively controlling movements of a flexible joint manipulator under different distance, velocity, and load requirements [23], and the use of a complicated nonlinear dynamic model makes real-time implementation difficult [24] It is by no means an easy task to identify the model parameters accurately. On the other hand, supervised learning is guided learning, i.e. when the network is formed it compares the input and the desired result which is represented in our case by the reference joint In this case, new parameters and weight coefficients are automatically adapted and adjusted, through online error learning, in order to decrease the position tracking error. Control laws were subjected to various test inputs in a simulation to characterize the tracking performance, and some results are illustrated to show the validity of the proposed approach
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