This paper presents and discusses the implementation results of a model-predictive control (MPC) scheme with friction compensation applied to trajectory following of an omnidirectional three-wheeled robot. A cascade structure is used with an inverse kinematics block to generate the velocity references given to the predictive controller. Part of the control effort is used to compensate for the effects of static friction, allowing the use of efficient algorithms for linear MPC with constraints. Experimental results show that the proposed strategy is efficient in compensating for frictional effects as well as for tracking predefined trajectories.

This paper presents a nonlinear modeling approach for omnidirectional mobile robots. Three experimental methods of estimation of the parameters related to dynamic equations of the robot's model are developed. The estimation methods are based on least-squares methods to obtain the viscous friction coefficients, the coulomb friction coefficients, and the moment of inertia of the robot. Simulation results and real results of navigation are provided to demonstrate the performance of the proposed modeling approach.

This paper presents an experimental analysis of a nonlinear model predictive controller combined with an integral sliding mode control component and an explicit dead-time compensator applied to the closed-loop control of FlatFish, an autonomous underwater vehicle (AUV). The proposed strategy is composed of three control instances that can be integrated to deal with constraints, disturbances, and dead time. The integral sliding mode control instance is considered to attenuate unmeasured disturbance effects, the filtered Smith predictor component is used to compensate for the dead-time effect, and the model predictive control layer aims at systematically optimizing closed-loop performance with constraint satisfaction. The discussion highlights that any of these control layers can be either included or removed from the control chain, which depends on the challenges presented by the AUV problem. The benchmark for such evaluation is the original control chain of FlatFish, which is based on a P cascade controller. Experimental tests were performed with FlatFish in a saltwater tank located in Bremen, Germany, and showed the superior performance of the proposed system. The experimental results show that the proposed control chain may be effectively used to provide improved tracking performance as expected from theoretical analysis.

The Industry 4.0 paradigm, since its initial conception in Germany in 2011, has extended its scope and adoption to a broader set of technologies. It is being considered as the most vital mechanism in the production systems lifecycle. It is the key element in the digital transformation of manufacturing industry all over the world. This scenario imposes a set of major unprecedented challenges which require to be overcome. In order to enable integration in horizontal, vertical, and end-to-end formats, one of the most critical aspects of this digital transformation process consists of effectively coupling digital integrated service/products business models with additive manufacturing processes. This integration is based upon advanced AI-based tools for decentralized decision-making and for secure and trusted data sharing in the global value. This paper presents the FASTEN IIoT Platform, which targets to provide a flexible, configurable, and open solution. The platform acts as an interface between the shop floor and the industry 4.0 advanced applications and solutions. Examples of these efforts comprise management, forecasting, optimization, and simulation, by harmonizing the heterogeneous characteristics of the data sources involved while meeting real-time requirements.

SUMMARY: This paper discusses about a proposed solution to the obstacle avoidance problem in multi-robot systems when applied to active target tracking. It is explained how a nonlinear model predictive formation control (NMPFC) previously proposed solves this problem of fixed and moving obstacle avoidance. First, an approach is presented which uses potential functions as terms of the NMPFC. These terms penalize the proximity with mates and obstacles. A strategy to avoid singularity problems with the potential functions using a modified A* path planning algorithm was then introduced. Results with simulations and experiments with real robots are presented and discussed demonstrating the efficiency of the proposed approach.