A New Adaptive RISE Feedforward Approach based on Associative Memory Neural Networks for the Control of PKMs

Abstract

In this paper, a RISE (Robust Integral of the Sign Error) controller with adaptive feedforward compensation terms based on Associative Memory Neural Network (AMNN) type B-Spline is proposed to regulate the positioning of a Delta Parallel Robot (DPR) with three degrees of freedom. Parallel Kinematic Manipulators (PKMs) are highly nonlinear systems, so the design of a suitable control scheme represents a significant challenge given that these kinds of systems are continually dealing with parametric and non-parametric uncertainties and external disturbances. The main contribution of this work is the design of an adaptive feedforward compensation term using B-Spline Neural Networks (BSNNs). They make an on-line approximation of the DPR dynamics and integrates it into the control loop. The BSNNs' functions are bounded according to the extreme values of the desired joint space trajectories that are the BSNNs' inputs, and their weights are on-line adjusted by gradient descend rules. In order to evaluate the effectiveness of the proposed control scheme with respect to the standard RISE controller, numerical simulations for different case studies under different scenarios were performed.

Neuroadaptive Robust Speed Control for PMSM Servo Drives with Rotor Failure

“…In the past two decades, adaptive controllers based on BSNN have been used in many industrial, electronic, mechatronics, and electromechanical control systems to regulate output variables, such as parallel kinematic manipulators, electrical power systems, shunt DC motors, UPS inverters, quadrotor, power control of wind turbine and induction motor [16][17][18][19][20][21]. The typical structure of a BSNN is shown in Figure 2.…”

“…The adaptability feature of the neural network gives the ability to learn from previous events, by interconnecting the input data to output, as shown in Figure 2. However, the initial values of weights of the BSNN are often generated randomly or adjusted based on the designer's experience to get excellent performance for a specific controller [16,17,19].…”

“…The adaptability feature of the neural network gives the ability to learn from previous events, by interconnecting the input data to output, as shown in Figure 2. However, the initial values of weights of the BSNN are often generated randomly or adjusted based on the designer's experience to get excellent performance for a specific controller [16,17,19]. The BSNN can compute its response on-line, based on a modest structure and a short number of basic mathematical operations that are an important desirable feature for efficiently controlled physical systems.…”

“…In the last twenty years, various control schemes based on B-Spline neural network (BSNN) have been designed because BSNN can compute different control signals due to its adaptability nature through proper online training. The diverse shapes of BSNN have been used to constitute the control schemes for mechatronics and electromechanical systems [17][18][19][20][21].…”

Adaptive Finite-time Synergetic Control of Delta Robot based on Radial basis Function Neural Networks

“…However, the choice of the linguistic rules and guaranteeing the system stability remain a challenging issue. A RISE (Robust Integral of the Sign Error) controller in [29] with a BSNN feedforward compensation was applied to a delta robot to regulate the trajectory tracking for a Pick and Place application. Since the addition of an intelligent compensation term may reduce the tracking error considerably and might cancel the steady-state error.…”