Real-time neural identification and inverse optimal control for a tracked robot

Abstract

This work presents the implementation in real-time of a neural identifier based on a recurrent high-order neural network which is trained with an extended Kalman filter-based training algorithm and an inverse optimal control applied to a tracked robot. The recurrent high-order neural network identifier is developed without the knowledge of the plant model or its parameters; on the other hand, the inverse optimal control is designed for tracking velocity references. This article includes simulation and real-time results, both using MATLAB Ò , and also the experimental tests use a modified HD2Ò Treaded ATR Tank Robot Platform with wireless communication.

Germinal Center Optimization Applied to Neural Inverse Optimal Control for an All-Terrain Tracked Robot

“…A tracked robot consists of the following state variables [17,26,27] position x, position y, position θ, velocity 1, velocity 2, current 1 and current 2. In this work, we focus on the controller tracking performance for x, y and θ ( Figure 5) for given references x r , y r and θ r .…”

“…In this work, we focus on the controller tracking performance for x, y and θ ( Figure 5) for given references x r , y r and θ r . The objective is to improve the NIOC results presented in [17] by using GCO to find the optimal parameters of the controller. These parameters are included in the matrices P 1 and P 2 defined in (11) and (12) respectively.…”

“…These parameters are included in the matrices P 1 and P 2 defined in (11) and (12) respectively. The P 1 and P 2 are symmetric positive definite matrices, therefore we can define the set of variables {ψ 1 , ψ 2 , ψ 3 , ψ 4 , ψ 5 } for the optimization problem with the definition in (17).…”

“…For the following experiments we use a GCO algorithm in five dimension running 150 iterations using 30 B-cells (4500 executions) for a test of 10 seconds; we set the parameters F = 1.25 and CR = 0.7. We include graphics for one simulation test using the all-terrain robot model presented in [17], and the results of one experimental test using the modified HD2 R Treaded ATR Tank Robot Platform with wireless communication (Figure 3) presented in [17].…”

“…The control scheme consisting of a neural identifier and an inverse optimal control technique is named neural inverse optimal control (NIOC), this control scheme has shown good results in the literature for trajectory tracking [15][16][17]. However, the designer has to tune the appropriate value of some parameters of the controller discuss later in this work, the quality of the controller depends directly on this selection.…”