A control structure for ambidextrous robot arm based on Multiple Adaptive Neuro‐Fuzzy Inference System

Abstract

This paper presents the novel design of an ambidextrous robot arm that offers double range of motion as compared to dexterous arms. The arm is unique in terms of design (ambidextrous feature), actuation (use of two different actuators simultaneously: Pneumatic Artificial Muscle (PAM) and Electric Motors)) and control (combined use of Proportional Integral Derivative (PID) with Neural Network (NN) and Multiple Adaptive Neuro‐fuzzy Inference System (MANFIS) controller with selector block). In terms of ambidextrous robot arm control, a solution based on forward kinematic and inverse kinematic approach is presented, and results are verified using the derived equation in MATLAB. Since solving inverse kinematics analytically is difficult, Adaptive Neuro Fuzzy Inference system (ANFIS) is developed using ANFIS MATLAB toolbox. When generic ANFIS failed to produce satisfactory results due to ambidextrous feature of the arm, MANFIS with a selector block is proposed. The efficiency of the ambidextrous arm has been tested by comparing its performance with a conventional robot arm. The results obtained from experiments proved the efficiency of the ambidextrous arm when compared with conventional arm in terms of power consumption and stability.

Parameter-Tunable RBF Neural Network Control Facing Dual-Joint Manipulators

“…So far, in describing the elastic deformation of flexible arms, there are mainly three deformation descriptions: classical deformation theory, quadratic deformation theory, and comprehensive deformation theory. Classical deformation theory is currently the most widely used method [9]. Literature [10] uses the classical deformation theory to model the single-joint flexible manipulator and considers the influence of nonlinear centrifugal force on the accuracy of the model.…”

Nutrient Film Technique-Based Hydroponic Monitoring and Controlling System Using ANFIS

“…As for future work, the system can be improved by incorporating GoAT ranking sensors to optimize the system flow [12]. Additionally, by implementing a multi-adaptive neuro fuzzy inference system (MANFIS), the system can be made more efficient, as only one fuzzy controller model will be required instead of four [22]. Furthermore, the system can be expanded to a larger scale and integrated with edge-fog-cloud computing environments to achieve near real-time data processing, reduce latency, and increase system efficiency [4].…”

Automatic vibration control method for grasping end of flexible joint robot

“…The Singular perturbation method is a method of solving nonlinear, high-order differential equations with minimal parameters, which cannot approximate the small parameters to zero. The Singular perturbation method can be used to reduce the order of this higher-order differential equation and decompose it into several small equations to solve them respectively [20]. The sliding mode variable structure method does not need an accurate mathematical model and has strong robustness.…”

“…In addition to the dynamic parameters of the robot in Table 2, RBF combined with SMC is adopted in this study. The control rate parameters are [3.5×107, 3.5×107, 2.2×107, 3×106, 1.5×106, 1.7×106] T; The sliding surface parameter is [400382300130,20,12] T; The sign function coefficient is [70000100000011000040001000800] T. The radial base width of neurons is 100; The adaptive rate parameter is diag (5,5,15,5,10,5). To test the tracking effect of SMC…”