This paper presents a sensor system for robot localization based on the information obtained from a single camera attached in a fixed place external to the robot. Our approach firstly obtains the 3D geometrical model of the robot based on the projection of its natural appearance in the camera while the robot performs an initialization trajectory. This paper proposes a structure-from-motion solution that uses the odometry sensors inside the robot as a metric reference. Secondly, an online localization method based on a sequential Bayesian inference is proposed, which uses the geometrical model of the robot as a link between image measurements and pose estimation. The online approach is resistant to hard occlusions and the experimental setup proposed in this paper shows its effectiveness in real situations. The proposed approach has many applications in both the industrial and service robot fields.

This paper combines the well-known linear quadratic Gaussian (LQG) control and frequency-adaptive resonators and presents a frequency-adaptive multiresonant LQG state-feedback current controller for LCL-filtered voltage-source converters connected to a distorted grid. The paper also provides a design guideline and procedure based on robust control criteria, which, in combination with the linear quadratic regulator technique, offers flexibility in the control structure and automatizes the design of the controller. The frequency-adaptive resonators, based on second-order IIR resonators and on an online tuning algorithm, and the robustness criteria considered for the design process offer robustness in the face of grid voltage disturbances. The controller is evaluated and validated in a 9-kVA voltage-source converter (VSC) setup configured as a rectifier.

In recent years, there has been a growing interest in using model-free deep reinforcement learning (DRL)-based controllers as an alternative approach to improve the dynamic behavior, efficiency, and other aspects of DC–DC power electronic converters, which are traditionally controlled based on small signal models. These conventional controllers often fail to self-adapt to various uncertainties and disturbances. This paper presents a design methodology using proximal policy optimization (PPO), a widely recognized and efficient DRL algorithm, to make near-optimal decisions for real buck converters operating in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) while handling resistive and inductive loads. Challenges associated with delays in real-time systems are identified. Key innovations include a chattering-reduction reward function, engineering of input features, and optimization of neural network architecture, which improve voltage regulation, ensure smoother operation, and optimize the computational cost of the neural network. The experimental and simulation results demonstrate the robustness and efficiency of the controller in real scenarios. The findings are believed to make significant contributions to the application of DRL controllers in real-time scenarios, providing guidelines and a starting point for designing controllers using the same method in this or other power electronic converter topologies.

This article presents a systematic robust current control design approach for three-phase voltage-source converters. Robustness is guaranteed by combining intrinsic passive properties of the impedance uncertainty at the point of common coupling together with stability results from a passivity-based control theory. This approach ensures stability against typical uncertainty sources at mid and high frequencies, such as cable resonances or other converters interaction, with significant less conservative performances than the obtained with the traditional robust control theory. The approach uses multiobjective controller synthesis formulation that allows us to logically combine robustness requirements with performance objectives avoiding heuristic iteration over the control structure and parameters. The controller synthesis is performed by means of a nonsmooth $\mathcal {H}_{\infty }$ optimization technique that tunes all free parameters of a vector-based controller function, which constraints its structure. This results in a synthesized controller with lower order than those obtained with convex optimization definitions of the $\mathcal {H}_{\infty }$ control problem. The design methodology is validated in time and frequency domain by means of theoretical analysis and experimental results with three usual grid filters: L , LCL, and LLCL .

This paper presents a current controller that shapes, in the frequency domain, the input admittance of voltage-source converters connected to the grid. The controller is obtained by means of a H _∞ synthesis procedure, which minimizes the difference between the application closed-loop input admittance and a model-reference defined by the designer. This formulation achieves good accuracy in both modulus and phase. The proposed methodology allows the fulfilment of other current control objectives, such as current tracking, by defining frequency regions where each objective is desired. Experimental results show the good response of the proposed controller, both in frequency and time domain.

The inherent complexity of wind energy systems has necessitated the development of sophisticated control methodologies to optimize operational efficiency. Artificial neural networks (ANN) have emerged as a powerful tool in wind turbine applications, offering sophisticated control capabilities for addressing the intricate challenges of energy conversion. This study focuses on the critical generator control block, where precise power management is essential to maintaining system stability and preventing operational disruptions. This research introduces an innovative ANN-based Direct Power Control (DPC) approach for a Doubly fed induction generator (DFIG) integrated into a wind power system, introducing a dual-MLP approach for precise power regulation. The proposed DPC-ANN controller proved effective in mitigating current ripples and achieving a near-unity power factor, indicating substantial improvement in power quality. Moreover, the spectrum harmonic analysis revealed that the controller yielded the lowest stator current total harmonic distortion of 1.29%, significantly outperforming traditional DPC-PI (2.76%) and DPC-Classic (2.24%) approaches. The proposed technique was rigorously implemented and validated using a real-time simulator (OPAL-RT) and MATLAB/Simulink (2020–2022) environment, specifically tested under a step wind profile. The real-time experimental validation highlights the practical applicability of this approach, bridging the gap between theoretical ANN-based control and real-world wind energy system implementation. These findings reinforce the potential of intelligent control strategies for optimizing renewable energy technologies, paving the way for more efficient and adaptive wind turbine control solutions.