Adaptive neural network control of underactuated surface vessels with guaranteed transient performance: Theory and experimental results

In this paper, an adaptive trajectory trackingcontrol algorithm for underactuated unmanned surfacevessels (USVs) with guaranteed transient performance isproposed. To meet the realistic dynamical model of USVs,we consider that the mass and damping matrices are notdiagonal and the input saturation problem. Neural Networks(NNs) are employed to approximate the unknown externaldisturbances and uncertain hydrodynamics of USVs. Moreover,both full state feedback control and output feedbackcontrol are presented, and the unmeasurable velocities ofthe output feedback controller are estimated via a highgainobserver. Unlike the conventional control methods,we employ the error transformation function to guaranteethe transient tracking performance. Both simulation andexperimental results are carried out to validate the superiorperformance via comparing with traditional potential integral(PI) control approaches

Admittance-based adaptive cooperative control for multiple manipulators with output constraints

This paper proposes a novel adaptive control methodology based on the admittance model for multiple manipulators transporting a rigid object cooperatively along a predefined desired trajectory. First, an admittance model is creatively applied to generate reference trajectory online for each manipulator according to the desired path of the rigid object, which is the reference input of the controller. Then, an innovative integral barrier Lyapunov function is utilized to tackle the constraints due to the physical and environmental limits. Adaptive neural networks (NNs) are also employed to approximate the uncertainties of the manipulator dynamics. Different from the conventional NN approximation method, which is usually semiglobally uniformly ultimately bounded, a switching function is presented to guarantee the global stability of the closed loop. Finally, the simulation studies are conducted on planar two-link robot manipulators to validate the efficacy of the proposed approach

Neural networks enhanced adaptive admittance control of optimized robot-environment interaction

In this paper, an admittance adaptation method has been developed for robots to interact with unknown environments. The environment to be interacted with is modeled as a linear system. In the presence of the unknown dynamics of environments, an observer in robot joint space is employed to estimate the interaction torque, and admittance control is adopted to regulate the robot behavior at interaction points. An adaptive neural controller using the radial basis function is employed to guarantee trajectory tracking. A cost function that defines the interaction performance of torque regulation and trajectory tracking is minimized by admittance adaptation. To verify the proposed method, simulation studies on a robot manipulator are conducted

Virtual Simulation Platform for Training Semi-Autonomous Robotic Vehicles’ Operators

This chapter covers the development of a virtual simulation platform for training a semiautonomous robotic vehicle (SARV) operator via an open-source game engine called Unity3D. The SARV such as remotely operated vehicles (ROVs) is becoming increasingly popular in the maritime industry for risky jobs in inhospitable environments. The primary element in carrying out underwater missions in a hostile environment lies within the skills and experience of an ROV pilot. Training for ROV pilots is essential to prevent damage to expensive field equipment during the real operations. The proposed simulator differs from the existing simulators in the market is the use of modern game engine software to develop a “serious game” for ROV pilot trainee at much lower cost and shorter time-to-market. The results revealed that proposed virtual simulator can develop a high-fidelity virtual reality training for the underwater operation guided by classification society

Poly[penta­kis­(μ-cyanido-κ2 N:C)tris­(5-phenyl-2,2′-bipyridine-κ2 N,N′)penta­copper(I)]

The hydro­thermal reaction of Cu(acetate)2 and K3[Fe(CN)6] with 5-phenyl-2,2′-bipyridine (5-ph-2,2′-bpy) in water yields the polymeric title complex, [Cu5(CN)5(C16H12N2)3]n, which consists of ribbons along the a axis, constructed from 26-membered {Cu10(CN)8} rings. In these rings, the metal atoms are bridged by cyanide groups, except for one close Cu⋯Cu contact [2.7535 (12) Å], which can be considered as ligand-unsupported. Within the rings, one Cu atom has a distorted tetra­hedral geometry through the coordination to two N atoms from 5-ph-2,2′-bpy and two N/C atoms from two cyanide groups. Two Cu atoms have a trigonal planar environment being coordinated by three cyanide groups and two other Cu atoms have a distorted square planar geometry through coordination to two N atoms from 5-ph-2,2′-bpy and two N/C atoms from two cyanide groups

Intelligent Marine Robotics Modelling, Simulation and Applications

The biennial Congress of the Italian Society of Oral Pathology and Medicine (SIPMO) is an International meeting dedicated to the growing diagnostic challenges in the oral pathology and medicine field. The III International and XV National edition will be a chance to discuss clinical conditions which are unusual, rare, or difficult to define. Many consolidated national and international research groups will be involved in the debate and discussion through special guest lecturers, academic dissertations, single clinical case presentations, posters, and degree thesis discussions. The SIPMO Congress took place from the 17th to the 19th of October 2019 in Bari (Italy), and the enclosed copy of Proceedings is a non-exhaustive collection of abstracts from the SIPMO 2019 contributions

Smooth Path Planning for Robot Docking in Unknown Environment with Obstacles

This paper presents an integrated approach to plan smooth path for robots docking in unknown environments with obstacles. To determine the smooth collision-free path in obstacle environment, a tree structure with heuristic expanding strategy is designed as the foundation of path planning in this approach. The tree employs 3D Dubins curves as its branches and foundation for path feasibility evaluation. For the efficiency of the tree expanding in obstacle environment, intermediate nodes and collision-free branches are determined inspired by the elastic band theory. A feasible path is chosen as the shortest series of branches that connects to the docking station after the sufficient expansion of the tree. Simulation results are presented to show the validity and feasibility of the proposed approach

Discrete-Time Dynamical Maximum Power Tracking Control for a Vertical Axis Water Turbine with Retractable Blades

This paper addresses the power generation control system of a new drag-type Vertical Axis Turbine with several retractable blades. The returning blades can be entirely hidden in the drum, and negative torques can then be considerably reduced as the drum shields the blades. Thus, the power efficiency increases. Regarding the control, a Linear Quadratic Tracking (LQT) optimal control algorithm for Maximum Power Point Tracking (MPPT) is proposed to ensure that the wave energy conversion system can operate highly effectively under fluctuating conditions and that the tracking process accelerates over time. Two-dimensional Computational Fluid Dynamics (CFD) simulations are performed to obtain the maximum power points of the turbine’s output. To plot the tip speed ratio curve, the least squares method is employed. The efficacy of the steady and dynamic performance of the control strategy was verified using Matlab/Simulink software. These validation results show that the proposed system can compensate for power fluctuations and is effective in terms of power regulation