Robust Control of Robotic and Complex Mechatronic Systems

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 4129

Special Issue Editor

Department of electrical, electronics and telecommunications engineering, Universidad de Cuenca, Cuenca, Ecuador
Interests: applied control engineering

Special Issue Information

Dear Colleagues,

Nowadays, robotics systems are everywhere, from tiny applications related to nano- and microbots to large-scale robotics systems used in industry for complex process automation. These applications require increased reliability to extend the systems' life cycle and guarantee their availability when required. Nevertheless, guaranteeing the performance of a control system under the uncertainty of the operation in contrast with the often over-simplified operation assumptions made during controller design is still a challenging task to be tackled by control engineering scientists.

Achieving a robust operation of any mechatronic system implies, apart from dealing with model uncertainty and other requirements, accommodating sensor and actuator faults, if possible. Such enhanced system performance corresponds to fault-tolerant control systems, which are a natural application of robust control systems. Therefore, several novel modeling and control strategies are still required for complex mechatronics systems that require high reliability and enhanced performance.

This Special Issue (SI) will provide a forum for researchers and practitioners to exchange their latest theoretical and engineering achievements and identify critical issues and challenges for near-future studies for robust control applied to robotics and complex mechatronic systems. Results of experimental research in field conditions are mostly encouraged for submission. The theoretical papers accepted into this SI are expected to contain original ideas and potential solutions for resolving real problems.

The topics of this Special Issue include, but are not limited to, the following domains:

  • Robust control of mobile robots (aerial, ground, and water);
  • Robust control of industrial mechatronics systems;
  • Uncertainty management in modeling of robotic systems;
  • Novel design methodologies for robust control systems;
  • Complex mechatronic systems modeling;
  • Robust control systems implementation in real-time platforms;
  • Performance estimation in complex mechatronic systems;
  • Fault-tolerant control applied to robotics and mechatronics systems;
  • Fault-detection and diagnosis applied to robotics and mechatronic systems.

Prof. Dr. Ismael Minchala
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • control systems
  • robust criterion
  • fault detection
  • diagnosis
  • robust control systems

Published Papers (3 papers)

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Research

29 pages, 7258 KiB  
Article
Robust Nonlinear Trajectory Controllers for a Single-Rotor UAV with Particle Swarm Optimization Tuning
by Patricia Portillo, Luis E. Garza-Castañón, Luis I. Minchala-Avila, Adriana Vargas-Martínez, Vicenç Puig Cayuela and Pierre Payeur
Machines 2023, 11(9), 870; https://doi.org/10.3390/machines11090870 - 29 Aug 2023
Viewed by 991
Abstract
This paper presents the utilization of robust nonlinear control schemes for a single-rotor unmanned aerial vehicle (SR-UAV) mathematical model. The nonlinear dynamics of the vehicle are modeled according to the translational and rotational motions. The general structure is based on a translation controller [...] Read more.
This paper presents the utilization of robust nonlinear control schemes for a single-rotor unmanned aerial vehicle (SR-UAV) mathematical model. The nonlinear dynamics of the vehicle are modeled according to the translational and rotational motions. The general structure is based on a translation controller connected in cascade with a P-PI attitude controller. Three different control approaches (classical PID, Super Twisting, and Adaptive Sliding Mode) are compared for the translation control. The parameters of such controllers are hard to tune by using a trial-and-error procedure, so we use an automated tuning procedure based on the Particle Swarm Optimization (PSO) method. The controllers were simulated in scenarios with wind gust disturbances, and a performance comparison was made between the different controllers with and without optimized gains. The results show a significant improvement in the performance of the PSO-tuned controllers. Full article
(This article belongs to the Special Issue Robust Control of Robotic and Complex Mechatronic Systems)
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20 pages, 1414 KiB  
Article
Self-Optimizing Control System to Maximize Power Extraction and Minimize Loads on the Blades of a Wind Turbine
by Carlos E. Rivas, Gilson D. Malo, Luis I. Minchala and Oliver Probst
Machines 2023, 11(6), 601; https://doi.org/10.3390/machines11060601 - 01 Jun 2023
Cited by 1 | Viewed by 1066
Abstract
This research proposes a methodology for designing and testing a self-optimizing control (SOC) algorithm applied to a wind energy conversion system (WECS). The SOC maximizes WECS power output and reduces the mechanical stress of the wind turbine (WT) blades by optimizing a multiobjective [...] Read more.
This research proposes a methodology for designing and testing a self-optimizing control (SOC) algorithm applied to a wind energy conversion system (WECS). The SOC maximizes WECS power output and reduces the mechanical stress of the wind turbine (WT) blades by optimizing a multiobjective cost function. The cost function computation uses a combined blade element momentum (BEM) and thin-wall beam (TWB) model for calculating wind the turbine power output and blades’ stress. The SOC deployment implies a low computational cost due to an optimization space reduction via a matrix projection applied to a measurement vector, based on a prior offline calculation of a projection matrix, H. Furthermore, the SOC optimizes the operation of the WECS in the presence of uncertainty associated with the wind speed variation by controlling a linear combination of measured variables to a set point. A MATLAB simulation of a wind turbine model allows us to compare the WECS operating with the SOC, a baseline classic control system (BCS), and a nonlinear model predictive controller (NMPC). The SOC algorithm is evaluated in terms of power output, blades’ stress, and computational cost against the BCS and NMPC. The power output and blades’ stress performance of the SOC algorithm are compared with that of the BCS and NMPC, showing a significant improvement in both cases. The simulation results demonstrate that the proposed SOC can effectively optimize a WECS operation in real time with minimal computational costs. Full article
(This article belongs to the Special Issue Robust Control of Robotic and Complex Mechatronic Systems)
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23 pages, 1293 KiB  
Article
Robust Control of UAV with Disturbances and Uncertainty Estimation
by Domenico Bianchi, Stefano Di Gennaro, Mario Di Ferdinando and Cuauhtémoc Acosta Lùa
Machines 2023, 11(3), 352; https://doi.org/10.3390/machines11030352 - 03 Mar 2023
Cited by 4 | Viewed by 1635
Abstract
In this work, a nonlinear estimator-based robust controller is designed for the position and yaw control of a quadrotor with uncertainty estimation. This controller ensures the tracking of desired references in the presence of parameters variation and external disturbances, making use of high-order [...] Read more.
In this work, a nonlinear estimator-based robust controller is designed for the position and yaw control of a quadrotor with uncertainty estimation. This controller ensures the tracking of desired references in the presence of parameters variation and external disturbances, making use of high-order sliding mode (HOSM) estimators to estimate these perturbations that can be canceled by the control, thus improving the dynamic behavior of the controlled system. Its performance is evaluated making use of a Simcenter Amesim quadrotor based on physical models generated from experimental data in a co-simulation framework with Matlab–Simulink used to implement the designed controller with FPGA implementation. A challenging and generic maneuver with time-varying wind disturbances and uncertainty model parameters is considered. Full article
(This article belongs to the Special Issue Robust Control of Robotic and Complex Mechatronic Systems)
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