This study proposes a hybrid control strategy utilizing an adaptive super-twisting (ASTW) methodology and artificial neural network (NN) techniques to regulate the collective blade pitch (CBP) of floating wind turbines (FWTs) operating in high wind conditions (Region III). In the presence of imposed perturbations, compounded by the complex nature of wind turbine dynamics, this research utilizes radial basis function (RBF) neural networks to approximate the unknown elements of the model. This methodology guarantees the controller’s stability and robustness, independent of the system’s precise mathematical model. The adaptive law is derived through the Lyapunov method, ensuring the convergence of rotor speed to the desired trajectory by fine-tuning the learning rate. The incorporation of the ASTW method, accompanied by self-tuning gains, aims to accelerate convergence speed and mitigate chattering synergistically with the NN approximation. The proposed controller was tested using the OpenFAST simulator, confirming its dynamic and static performance as well as validating its efficacy across scenarios with and without the RBF neural network approximator.

The floating wind turbines equipped with permanent magnet synchronous generators are complex nonlinear physical systems with multiple degrees of freedom. However, the complexity of these systems, the presence of unmodeled dynamics, and external disturbances pose challenges in maximizing power extraction during low-wind periods. This paper focuses on addressing this challenge for a grid-connected spar-buoy type floating offshore wind turbine (FOWT) operating in Region II. We evaluate the performance of the adaptive sliding mode control and the twisting algorithm to maximize power extraction in the low-wind region and deliver generated electricity to the grid considering the dynamic models of these systems. The controllers’ performance is compared against the existing simplified adaptive super-twisting algorithm under identical operating conditions of the FOWT. The co-simulation of the National Renewable Energy Laboratory (NREL) FAST software and the MATLAB/Simulink demonstrate the robustness of the proposed controllers across different conditions regardless of disturbance inputs.

This paper introduces a new control strategy for large-power (25MW) floating wind turbines (FWTs). First, a robust nonlinear controller, an adaptive super-twisting (ASTW) approach, is employed. This method significantly reduces the modeling effort. The strategy is designed to address challenges such as the negative damping effect, which can occur beyond the rated wind speed. A traditional proportional-integral (PI) controller can mitigate this effect, but it is slow when applied to large FWTs due to the system’s small natural pitch frequency. Second, the proposed ASTW controller is compared with the ROSCO control, which is based on the traditional gain-scheduled PI controller. The simulations consider a complete aero-hydro-servo-elastic model of the system, including mooring lines, a floating platform, a tower, a generator, and a rotor. The results suggest that the ASTW controller can maintain good tracking of the rated rotational speed and power output while ensuring satisfactory platform stabilization and fatigue load reduction, thereby improving the overall efficiency of the FWT.

International audience

In this paper, a novel adaptive super-twisting controller is used for a floating wind turbine system working in region III. The tuning of the controller with time varying gains does not need any information on the bounds of the system uncertainties. As it is known, the floating wind turbine is an extremely nonlinear system and a simple model to reproduce all its dynamics does not exist. For these reasons, the proposed new controller is well adapted to this kind of system. The control objectives are power regulation, platform pitch motion reduction and reduction of blades fatigue load. The proposed controller is tested on FAST simulator, and the obtained results show its high level capability compared to other control strategies.

The subject of this paper is about the use of a suspended Cable-Driven Parallel Robot (CDPR) for pick-and-place operations of heavy and heterogeneous objects. The knowledge of the payload mass and its center of mass in realtime is an asset for robust control of the device, which is required to ensure a good stability, especially when the objects have different shapes, sizes and masses. Accordingly, this paper aims at experimentally evaluating the effects of (i) the pulleys modeling and (ii) the use of force sensors for the payload estimation. It turns out that the consideration of the pulleys into the geometric model of the robot improves the mass and center of mass estimations of the payload. A comparison is made between the estimation of cable tensions from force sensors and from motor currents. Finally, a torque controller with a feedforward term for real-time mass compensation is proposed and implemented on a CDPR prototype.

The estimation of the state of lateral dynamics of a vehicle is usually performed using information gathered by several sensors, such as cameras, radars, odometers, and accelerometers. However, some quantities and their variations cannot be measured directly in all driving situations whereas they are required for vehicle control. Among these unmeasured quantities, one can find road curvature. This paper proposes to apply nonlinear observers to provide such information, in addition to other quantities related to the vehicle state, in the context of Traffic Jump Pilot. The first contribution of this paper is to formulate the nonlinear dynamics model and then to design two nonlinear observers based on High-Gain and Sliding Mode techniques. Furthermore, in order to ensure their efficient applications in a real situation, an experimental methodology is developed in order to optimize their performances by finding an optimal set of gain values. In that respect, a large database of real driving conditions and different road curvature scenarios is recorded for the performances evaluation and then a recent multi-objective optimization algorithm is used to find the optimal tuning parameters of the observers. The effectiveness of the proposed methodology is illustrated through an application to a driving test, which is not in the database. The use of this optimization tool is a novelty in the context of nonlinear observation and control, which allows getting optimized observers that can be efficiently applied to experimental data.

This paper deals with a planar biped. The aim of this paper is the estimation, during the imbalance phases of a walking cyclic gait, of its absolute orientation by only using the measurement of the actuated joint variables. The main contribution is the experimental evaluation of an original finite-time convergent-posture observer.

This paper proposes, through a survey of models of several UAV-structures, a generic nonlinear model for reduced scale aerial robotic vehicles (6 DOF). Dynamics of an aircraft and some VTOL UAV (quadricopter, ducted fan and classical helicopter) are illustrated. This generic model focuses only on the key physical efforts acting on the dynamics in order to be sufficiently simple to design a controller. The small body forces expression which can introduce a zero dynamics is then discussed.

Experimental validation of absolute orientation estimation solutions is displayed for the dynamical stable five-link biped robot Rabbit during a walking gait. The objective is to prove the technical feasibility of posture online software estimation in order to remove sensors. Finally, this paper presents the first experimental results of walking biped robot posture estimation.

A step-by-step observer based on second order sliding mode approach is proposed to determine the absolute orientation of a biped robot during a walking cyclic gait. The gait are composed of single support phases and impacts. The biped is underactuated in single support because there is no motor in the ankles. The observer and the control law converge in finite-time. The originality is both: firstly, the observer is based on second-order sliding mode approach and is original in biped robot context Secondly, the estimation of all state variables are derived from only actuated joint variables, which induces a minimization of the sensors number. Numerical tests are proposed to show the robustness of the observer coupled with the control.

In this paper, we present a method to plan and control the 6 DOF displacements of a manipulator from an eye-in-hand camera image. With this method, even in the presence of camera calibration errors, we can formally guarantee that the system is stable and that the entire target remains visible during any visually servoed motion.

The primary goal of this paper is to demonstrate a means to prove asymptotically stable walking in an underactuated, planar, five-link biped robot model. The analysis assumes a rigid contact model when the swing leg impacts the ground and an instantaneous double support phase. The specific robot model analyzed corresponds to a prototype under development by the Centre National de la Recherche Scientifique (CNRS), Paris, France. A secondary goal of the paper is to establish the viability of the theoretically motivated control law. This is explored in a number of ways. First, it is shown how known time trajectories, such as those determined on the basis of walking with minimal energy consumption, can be incorporated into the proposed controller structure. Secondly, various perturbations to the walking motion are introduced to verify disturbance rejection capability. Finally, the controller is demonstrated on a detailed simulator for the prototype which includes torque limits and a compliant model of the walking surface, and thus a noninstantaneous double support phase.

This paper focuses on the design of robust motion rate sensor based control of robots. Considering the widely used task function approach of this problem, we first derive a general condition on the control gains with respect to the uncertainties, so as to guarantee bounded tracking errors. This general result is applied to an example case, consisting of a 2-DOF vision based controller. Experimental results of comparing different control strategies are presented.

We use sliding mode control theory to design a 3D vision based controller that is robust to bounded parametric estimation errors. First, a model of an eye-in-hand robotic system is derived and sources of uncertainties are listed. Additionally, bounds on the different uncertainties are discussed and their influence on the overall gain of the system is derived. Due to an appropriate selection of the sliding surface, based on the quaternion representation for rotations, a switching controller is proposed. Six degrees of freedom vision based tracking experiments under weak calibration conditions emphasize the practical efficiency of the algorithm.