Sliding Mode Control of a 2DOF Robot Manipulator: A Simulation Study Using Artificial Neural Networks with Minimum Parameter Learning

Background: In this paper, we have developed an intelligent control law for the control of mobile manipulator robots by investigating the various techniques proposed in the literature. Thus, we have adopted a hybrid approach that integrates a part of classical and advanced automation in order to create an efficient control structure that can cope with a certain level of complexity. Our research logic is based on the process of keeping in mind that the control system must comply with the constraints imposed during the implementation of the control architecture. Objective: This paper aims to develop a control law in order to guarantee a certain level of performance, more precisely, during a trajectory tracking application for mobile handling missions. The developed control law guarantees robustness with respect to external disturbances and parametric uncertainties due to the modelling of the system. Methods: In this paper, a study of the basic concepts of robotics and robot modelling is presented in order to set up the dynamic model used for the elaboration of the command. A sliding-mode controller based on a radial base function neural network with minimum parameter learning is developed for the Pelican robot as a two-link robot manipulator. This approach, which combines a radial base function neuronal network (RBFNN) and a sliding mode control (SMC), is presented for the tracking control of this class of systems with unknown non-linearities. The centre and output weights of the RBFNN are updated via online learning in accordance with the adaptive laws, allowing the control output of the neural network to approach the equivalent control in the sliding mode in the predetermined direction. The Lyapunov function is used to develop the adaptive control algorithm based on the RBFNN model. For reducing the computational load and increasing real-time arm performance, an RBFNN-based on the SMC with the Minimum Parameter Learning (MPL) method is designed. Results: Neural network sliding mode control is designed to underline the effectiveness of the approach to control the manipulator;—this method of control is used to ensure the tracking trajectories. Conclusion: The results of the simulation for the manipulator's arm demonstrated the effectiveness of the modelling strategy, the correction, and the robustness of the control approach.

Analysis free surface of nonlinear seepage using the MQRBF method

In order to solve the characteristics of low accuracy and slow efficiency in traditional numerical solution the free surface problem, the multiquardatic radial base function collocation method(MQ RBF) is used to analyze the constant seepage and unsteady seepage of the homogeneous earth dam. Computation of transient problem of free surface of earth dam by the linear derivation of Richards equation. The results show that the calculation accuracy of the MQRBF is higher than that of the traditional numerical method. The solution process does not involve numerical integral calculation and grid reorganization, which greatly reduces the calculation amount. Compared with the Trefftz method, it has the advantage of solving boundary values and internal values at the same time. It is not limited by the solution of the Laplace equation, and its application is wider and simpler.

Computational Aeroelastic method for rotor based on MBDYN

A framework of numerical formulations for the aeroelastic analysis of helicopter rotor is presented in this paper. The blade structural dynamics are modeled by an open source multibody dynamic software MBDYN, which solves finite element equation of elastic bodies in general motions. Then the structural deformation is transformed to blade surface grid by radial base function (RBF) interpolation, and volume grids are regenerated by RBF and TFI methods. Lastly, the fluid governing equations are solved. By integrating the above methods, S76 hovering rotors are simulated and compared to the test data. Results show that elastic torsion decreases local angle of attack. For status at [Formula: see text] and [Formula: see text], the shock and shock-induced separation are reduced on the outboard blade, which has remarkable effects on the prediction of rotor hovering performance.

Implementasi Support Vector Regression pada Prediksi Inflasi Indeks Harga Konsumen

Inflation reflects an increase in the prices of these items as well as those used by the Indonesian government, especially Bank Indonesia, in determining monetary policy. An indicator that can be obtained by Bank Indonesia in measuring inflation is the Consumer Price Index. This study discusses inflation prediction using the SVR method. Inflation test data issued by Bank Indonesia. As a comparison material for the kernel used in the SVR method using two kernels, namely Linear and Radial Base Function. The error rate evaluation results show that linear kernels produce better values, with a MAPE rate of 8.70% and MSE of 0.0037

Accuracy Analysis of ANN Back Propagation, Neuro-Fuzzy, and Radial Basis Function: A Case of HDI Forecasting

One measure of the progress of a region or country is the increase in the Human Development Index (HDI) which includes life expectancy, per capita income, and old school expectations. HDI becomes an essential reference in time-series data, so it needs to be done forecasting process with reliable method. We use HDI data as much as the last nine years in West Nusa Tenggara province, which is one of the regions with the highest HDI acceleration in Indonesia in recent years. We do forecasting by comparing three methods namely Back Propagation (BP), Neuro-Fuzzy (NF), and Radial base Function (RBF), covering forecasting with 3 models of training and testing on the Back Propagation method, 9 training and testing models on A Neuro-Fuzzy method, and 1 training and testing model in the Radial base Function method. While the parameter accuracy (error) used in this forecasting is Mean Square Error (MSE). Based on the results of the simulation obtained NTB province predictions in 2019 using the Back Propagation (BP) method of 67.46 (increased by 0.23%); The RBF method amounted to 67.3 (fixed); and the NF method of 67.18 (decreased by 0.17%). From these results, the conclusion that in this case, the BP method is very good at doing simulation and decision-making results. The results were obtained from simulated data witt type training TRAINGDA, TRAINGDX, and TRAINRP. But simulation using type TRAINRP has the best parameter output with a performance (R) of 0.99194, a validation check of 1000, a gradient of 13.8, and a level of accuracy of 99.39%.