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Robot manipulators are now used in various domains and environments, where they can be subjected to random vibrations. Random vibrations mainly affect the torque control signal, and a torque controller is therefore required to be designed for stabilization purposes. However, for security or intellectual property protection reasons, most commercialized robots are manufactured with unknown and inaccessible torque controller interface such that the user can only design a position/velocity controller. This paper proposes an adaptive task-space velocity controller free from the inner controller's structure and exhibiting stochastic and deterministic disturbances rejection to deal with these issues. To deal with the unknown inner controller, the paper exploits the fact that most torque controllers use a velocity feedback term, and it considers the other terms as an unknown functions vector. To cope with random disturbances, it is demonstrated that the random excitation matrix can be linearly parameterized, and therefore, a direct adaptive method is constructed. Using radial basis function neural network (RBF NN), an indirect adaptive method is developed to cope with deterministic uncertainties. Through Lyapunov theory, the paper proves that all the closed-loop signals are bounded in probability. The effectiveness of the proposed approach is further demonstrated through simulation comparisons.INDEX TERMS Robot manipulators, Adaptive task-space control, Closed inner controller, Random vibrations, Neural networks.

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots’ autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller’s outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system’s efficiency.

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