An Inverse Neural Controller Based on the Applicability Domain of RBF Network Models

Abstract

This paper presents a novel methodology of generic nature for controlling nonlinear systems, using inverse radial basis function neural network models, which may combine diverse data originating from various sources. The algorithm starts by applying the particle swarm optimization-based non-symmetric variant of the fuzzy means (PSO-NSFM) algorithm so that an approximation of the inverse system dynamics is obtained. PSO-NSFM offers models of high accuracy combined with small network structures. Next, the applicability domain concept is suitably tailored and embedded into the proposed control structure in order to ensure that extrapolation is avoided in the controller predictions. Finally, an error correction term, estimating the error produced by the unmodeled dynamics and/or unmeasured external disturbances, is included to the control scheme to increase robustness. The resulting controller guarantees bounded input-bounded state (BIBS) stability for the closed loop system when the open loop system is BIBS stable. The proposed methodology is evaluated on two different control problems, namely, the control of an experimental armature-controlled direct current (DC) motor and the stabilization of a highly nonlinear simulated inverted pendulum. For each one of these problems, appropriate case studies are tested, in which a conventional neural controller employing inverse models and a PID controller are also applied. The results reveal the ability of the proposed control scheme to handle and manipulate diverse data through a data fusion approach and illustrate the superiority of the method in terms of faster and less oscillatory responses.

Multi-Ship Control and Collision Avoidance Using MPC and RBF-Based Trajectory Predictions

“…RBF NNs have been successfully employed to approximate nonlinear system dynamics in order to predict future system states in numerous diverse applications [32,33]. Their success can be mainly attributed to their structure, which is simpler when compared to other NN architectures, as they comprise a single hidden layer, attached linearly to the network output.…”

Robust ℋ∞-Fuzzy Logic Control for Enhanced Tracking Performance of a Wheeled Mobile Robot in the Presence of Uncertain Nonlinear Perturbations

“…Motor systems play a foundational role for precise positioning and motion control in robotics and automation, but they are usually subject to nonlinearities, disturbances, as well as sensors' and environmental noise [1,2]. DC motors in particular are typically vulnerable to the mentioned perturbations; hence, they can vary widely in performance, although they are constructed by the same manufacturer using similar raw materials [3,4].…”