This research delves into the development and evaluation of two distinct controllers for a 3-DoF robotic arm in the context of Industry 4.0. Two primary control strategies are presented in the study. The first is a Fuzzy Logic Controller that utilizes joint position error and its derivative as inputs, employing a set of 9 control knowledge rules. The second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller, trained to learn the inverse dynamic model of the robot through a structured dataset. The research emphasizes the importance of accurate parameter tuning and data acquisition to achieve optimal control system performance. Extensive experimentation was conducted to evaluate the controllers’ performance in trajectory tracking and their response against external disturbances, such as load variations. The controllers exhibited remarkable precision and proficiency in tracking reference trajectories, with minimal deviations, overshoots, or oscillations. A quantitative analysis using performance indices such as root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE) further confirmed the controllers’ effectiveness. Notably, the ANFIS Controller consistently outperformed the Fuzzy Logic Controller, demonstrating superior precision in trajectory tracking. The study underscored the importance of selecting the right control method and obtaining high-quality training data. Challenges in parameter tuning for Fuzzy Logic Controllers and potential time constraints in training ANFIS were discussed. The findings have significant implications for advancing robotic control systems, particularly in the era of Industry 4.0.

Fuzzy Control Strategies Development for a 3-DoF Robotic Manipulator in Trajectory Tracking

Study of a serial manipulator involves many non-linear functions and complex sets of equations. The complexity of the problem increases as mobility of the system increases and it becomes a very tedious task to analyse the robotic system especially the inverse kinematics and forward dynamics. This study is aimed to reduce the complexity of analysis by implementing adaptive neuro-fuzzy inference system-based inverse kinematic analysis and forward dynamic analysis. The CAD model of serial manipulators is prepared using SOLIDWORKS to get geometrical and inertial information. The manipulator is also mathematically modelled in MATLAB and its forward kinematics (FK), inverse kinematics (IK), and dynamic analyses are carried out. The ANFIS networks are trained using data generated by a mathematical model, and it is then tested for a new set of data. Finally, the results are validated by comparing with that of the mathematical model.

ANFIS-Based Inverse Kinematics and Forward Dynamics of 3 DOF Serial Manipulator

In robot control systems, inverse dynamics calculation of the robotic arm is essential. In this paper, we propose a different inverse dynamics modeling method—sparse spectral Gaussian process regression (SSGPR). Since using Gaussian process regression for robot inverse dynamics modeling is computationally intensive, to address this drawback, it is proposed to apply sparse spectral Gaussian process regression on robot inverse dynamics modeling, the core of which is the sparsification of the GP’s spectrum for the purpose of improving computational efficiency. Experiments show that sparse spectral Gaussian process regression can improve the computational efficiency and outperform other regression methods while ensuring the computational accuracy.

Inverse dynamics modeling of robots based on sparse spectral gaussian process regression

The dynamic model of a robot is complex as the system is generally nonlinear, multivariable, and time-invariant. Methods have been developed to model the dynamics of robots such as the Newton-Euler and Lagrange-Euler algorithms. For those methods, it is important to have accurate data such as localization of the link’s center of mass about its base, the inertia matrix, lengths, and viscosity. Uncertainties of the data make the model different from the real system. For those reasons, we applied a Neural Network model to identify the inverse dynamic model of a 3-degree-of-freedom robotic with joint torques and joint values. The robot model was developed using Matlab Simscape Multibody in Simulink Toolbox, and sample input torque and output joint time evolution data were obtained to train the model. The data obtained was divided into training data and validation data. The model was validated using the mean square error (MSE), $\mathrm{R}^{2}$, and Cosine Similarity metrics comparing the measures with those of the ground truth.

Approach to Inverse Dynamics Model for a Serial 3 DoF Robot Using Neural Networks

The inverse dynamics calculation of a robot manipulator is essential in a robot manipulator control system. The commonly used approaches to formulate dynamic models of robot manipulators are Newton-Euler and Lagrange-Euler methods. Since both methods are numerical recursive methods, they are computationally expensive and not suitable to be used directly in real time applications. In this paper, the adaptive-network-based fuzzy inference system (ANFIS) is used to construct the input-output mapping of inverse dynamics model of a robot manipulator. To create the training data sets for the ANFIS systems, the dynamic model of a 5 degree-of-freedom robot manipulator is computed using Newton-Euler dynamic formulation. The proposed method is tested in formulating the required torques of a point-to-point trajectory. The results show that the proposed method possesses shorter operational time and its performance is comparable to numerical method. The proposed method can be used for the rigid-body robot manipulators whose dynamical characteristics are known.

Efficient Method for Inverse Dynamics of Robot Manipulators by using Adaptive-network-based Fuzzy Inference System

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Efficient Method for Inverse Dynamics of Robot Manipulators by using Adaptive-network-based Fuzzy Inference System