Robot Trajectory Tracking With Adaptive RBFNN-Based Fuzzy Sliding Mode Control

Abstract

Due to computational burden and dynamic uncertainty, the classical model-based control approaches are hard to be implemented in the multivariable robotic systems. In this paper, a model-free fuzzy sliding mode control based on neural network is proposed. In classical sliding mode controllers, system dynamics and system parameters are required to compute the equivalent control. In Radial Basis Function Neural Network (RBFNN) based fuzzy sliding mode control, a RBFNN is developed to mimic the equivalent control law in the Sliding Mode Control (SMC). The weights of the RBFNN are changed for the system state to hit the sliding surface and slide along it with an adaptive algorithm. The initial weights of the RBFNN are set to zero and then tuned online, no supervised learning procedures are needed. In the proposed method, by introducing the fuzzy concept to the sliding mode and fuzzifying the sliding surface, the chattering can be alleviated. The proposed method is implemented on industrial robot (Manutec-r15) and compared with a PID controller. Experimental studies carried out have shown that this approach is a good candidate for trajectory tracking applications of industrial robot.

A hybrid tactile sensor-based obstacle overcoming method for hexapod walking robots

“…In another study, the LTL-A* algorithm was used calculate a globally optimal path specified by linear temporal logic (LTL) and a weighted transition system [20]. On the other hand, neural network and deep learning-based methods have been proposed recently such as radial basis function neural network (RBFNN) applied for trajectory tracking of industrial Manutec-r15 robot [21], while grid-based search on randomized maps has been adopted in [22]. Recently, a number of hybrid and nature-inspired algorithms were suggested such as particle swarm optimization-modified frequency bat (PSO-MFB) algorithm for multi-target path planning [23], firefly algorithm for trajectory planning in highly uncertain environment [24], dragonfly algorithm [25], a hybrid beetle antennae search (BAS) and artificial potential field (APF) algorithm [26].…”

Workflow Management System for DMS

“…Artificial intelligence, as a modern concept of solving various engineering problems [19,20], represents a creative approach for workflow scheduling. The problems of scheduling that adopt genetic algorithm method for workflow manipulation are addressed in several papers [21,22].…”

Application of a Chaotic Synchronization System to Secure Communication

“…However, to our knowledge, no previously developed scheme can obtain such a robust continuous controller for chaos synchronization control in a secure communications system, necessitating the development of a sliding mode control scheme. Therefore, based on a sliding mode controller [22], this work addresses the synchronization issue of a chaotic system, while devising a sliding mode control criterion. While implemented with electronic components, system synchronization is validated using the chaotic system and the controller.…”