Control systems that ensure robot operation during failures are necessary, particularly when manipulators are operating in hazardous or hard-to-reach environments. In such applications, fault-tolerant robot controllers should detect failures and, using fault-tolerant control methods, be able to continue operation without human intervention. Fault-tolerant control (FTC) is becoming increasingly important in all industries, including production lines in which modern robotic manipulators are used. The use of fault-tolerant systems in robotics can prevent the production line from being immobilized due to minor faults. In this paper, an overview of the current state-of-the-art methods of fault-tolerant control in robotic manipulators is provided. This review covers publications from 2003 to 2022. The article pays special attention to the use of artificial intelligence (AI), i.e., fuzzy logic and artificial neural networks, as well as sliding mode and other control methods, in the FTC of robotic manipulators. The cited and described publications were mostly found using Google Scholar.

In this paper, the application of the policy gradient Reinforcement Learning-based (RL) method for obstacle avoidance is proposed. This method was successfully used to control the movements of a robot using trial-and-error interactions with its environment. In this paper, an approach based on a Deep Deterministic Policy Gradient (DDPG) algorithm combined with a Hindsight Experience Replay (HER) algorithm for avoiding obstacles has been investigated. In order to ensure that the robot avoids obstacles and reaches the desired position as quickly and as accurately as possible, a special approach to the training and architecture of two RL agents working simultaneously was proposed. The implementation of this RL-based approach was first implemented in a simulation environment, which was used to control the 6-axis robot simulation model. Then, the same algorithm was used to control a real 6-DOF (degrees of freedom) robot. The results obtained in the simulation were compared with results obtained in laboratory conditions.

This paper presents research on the application of magnetic shape memory alloys (MSMAs) in actuator design. MSMAs are a relatively new group of so-called smart materials that are distinguished by repeatable strains up to 6% and dynamics much better than that of thermally activated shape memory alloys (SMAs). The shape change mechanism in MSMAs is based on the rearrangement of martensite cells in the presence of an external magnetic field. In the first part of the article a review of the current state of MSMA actuator design is presented, followed by a description of the design, modelling and control of a newly proposed actuator. The developed actuator works with MSMA samples of 3 × 10 × 32 mm³, guaranteeing an available operating range of up to 1 mm, despite its great deformation range and dynamics. In the paper its dynamics model is proposed and its transfer function is derived. Moreover, the generalised Prandtl-Ishlinskii model of MSMA-actuator hysteresis is proposed. This model is then inverted and used in the control system for hysteresis compensation. A special test stand was designed and built to test the MSMA actuator with compensation. The step responses are recorded, showing that the compensated MSMA actuator exhibits the positioning accuracy as ±2 µm. As a result, the authors decided to apply a control system based on an inverse hysteresis model. The paper concludes with a summary of the research results, with theoretical analysis compared with the registered actuator characteristics.