This article explores the relationship between optimal control and Cosserat beam theory from the perspective of solving the forward and inverse dynamics (and statics as a subcase) of continuous manipulators and snake-like bioinspired locomotors. By invoking the principle of minimum potential energy and the Gauss principle of least constraint, it is shown that the quasi-static and dynamic evolutions of these robots are the solutions of optimal control problems in the space variable, which can be solved at each step (of loading or time) of a simulation with the shooting method. In addition to offering an alternative viewpoint on several simulation approaches proposed in the recent past, the optimal control viewpoint allows us to improve some of them while providing a better understanding of their numerical properties. The approach and its properties are illustrated through a set of numerical examples validated against a reference simulator.

This paper explores some variations of a hier-archical control framework that has been recently proposed. This framework is dedicated to the control of a network of interconnected subsystems such as those describing cryogenic processes or power plants. Recent studies have shown that handling constraints and non-linearities could challenge the real-time feasibility of the approach. This paper investigates and combines two successful directions, namely the use of truncated fast gradient and deep-neural-network-based controller modeling, to reduce the computational time of the most critical subsystem. It is also shown that by doing so, the control update period can be significantly reduced and the closed-loop performance is greatly improved. This paper can therefore be seen as a concrete implementation and validation of some key ideas in the design of real-time distributed NMPCs. All concepts are validated using the realistic and challenging example of a real cryogenic refrigerator.

This paper investigates the use of fixed-point Anderson method (AM) to a recently proposed hierarchical control framework [1]. In the previous works, the synthesis of a filter that ensures the convergence of fixed-point iteration is made by using the mathematical knowledge of subsystems, which violates the privacy-preserving requirement for the coordinator at the upper layer. Due to its model-free property, the AM-based resulting hierarchical framework becomes more generic since no mathematical model of the subsystems at the lower layer is required at the upper coordinator layer. Numerical results are proposed to evaluate the effectiveness of this approach in showing that the AM could converge faster in some cases where the filter is not compatible with the model.

This paper proposes an observer-based current controller for a virtual synchronous generators (VSG) to improve its operational performances. First, the model and the controllers are analytically described. Two controller are compared to the proposed one (PI integrating a state observer): a traditional PI and a PI integrating virtual impedances for filtering. Secondly, the advantage of the observer in the current controller loop for the VSG application is studied and compared to the other controllers. Finally, experimental results validate the capacity of the VSG-based inverter to supply unknown and unpredictable loads, thanks to the integration of the observer in the current control loop.

Permanent magnet synchronous motor (PMSM) is extensively used in industries. Recently, active disturbance rejection controller (ADRC) based control designs stand out due to advantages for a complex uncertain PMSM servo system. A general design procedure for the ADRC based PMSM control is discussed at the beginning. Then, the ADRC based control designs applied to PMSM servo systems are fully reviewed, in which several aspects are involved: position control, speed control, current control, cascaded ADRC control, and position identification. Moreover, to verify the effectiveness of the ADRC based control, simulations for the PMSM speed control are conducted in the Matlab/Simulink. Conclusions and the future research directions are indicated in the end.

Micro-hydropower has been paid more attention for its huge potential in the renewable energy section. The hydraulic turbine efficiency is sensitive to the water flow rates and rotation speed variations, which makes the variable speed operation preferable to ensure the maximum power point tracking (MPPT) in a micro-hydropower generation system (MHPGS). However, a MHPGS is a complex coupled nonlinear system disturbed by large internal dynamics and external disturbances, which bring negative influences on the speeding tracking performance. The active disturbance rejection control (ADRC) has advantages in dealing with large uncertainties, as it can estimate both internal and external disturbances and then compensate the influences in the closed loop dynamics in real time. To improve the anti-disturbance ability of the speed tracking process for a MHPGS, an ADRC based MPPT strategy is put up in this paper. Finally, simulation and experiment results have proved the high efficiency of the proposed strategy.

International audience

The growing need of energy, global warming and recent nuclear power plant accidents have shown that renewable energies need to be developed for tomorrow's world. Wind energy is generally harvested using wind turbines. Unfortunately, these systems have some drawbacks such as their cost, and the amount of steel and concrete used for construction. As their size grows, their complexity increases exponentially. This paper studies an alternative solution for the production of wind energy, using a kite's traction force. The aim of this paper is to control the amount of energy produced by the kite, and to be able to fly it safely in the presence of strong wind gusts. Our theoretical work has been implemented in a scale model flying autonomously in a wind tunnel. The proposed control strategy has led to control the system output power with an accuracy greater than 95%, with unknown wind speeds varying from 7.5 to 9 m/s.

In this paper, a multi-variable feedback design for the 3D movement of an eel-like robot is presented. Such a robot is under construction in the context of a national French robotic project. The proposed feedback enables the tracking of a desired 3D position of the eelpsilas head as well as the stabilization of the rolling angle. The control design is based on a recently developed reduced model that have been validated using a 3D complete continuous model described in [3]. Several scenarios are proposed to assess the efficiency of the proposed feedback law.

This paper relates recent advances in the design of feedback laws for the 3D movement of an Eel-like robot. Such a robot is under construction in the context of a national French robotic project. The proposed feedback enables the tracking of a desired 3D position of the Eel head as well as the stabilization of the rolling angle. A velocity controller is also proposed. The controller is tested on a recently developed complete 3D model in order to assess its efficiency in tackling 3D manoeuvres.

In this paper, a new nonlinear predictive control scheme is proposed for a planar under-actuated walking robot. The basic feature in the proposed strategy is to use online optimization in order to update the tracked trajectories in the completely controlled variables in order to enhance the stability of the remaining indirectly controlled ones. The stability issue is discussed using the Poincare's section tool leading to a computable criterion that enables the stability of the overall scheme to be investigated as well as the computation of a candidate region of attraction. The whole framework is illustrated through a simulation case-study.