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Model Predictive Control in Sensing and Robotic- Methods and Applications
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Model Predictive Control in Sensing and Robotic- Methods and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensors and Robotics".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 5160

Special Issue Editors

Dr. Chuxiong Hu

E-Mail Website
Guest Editor
State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
Interests: precision/intelligent electromechanical control; integrated circuit manufacturing equipment; CNC machine tools and robots
Dr. Ze Wang

E-Mail Website
Guest Editor
State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
Interests: precision/intelligent electromechanical control
Dr. Mingxing Yuan

E-Mail Website
Guest Editor
Tianjin Key Laboratory of Intelligent Robotics, Institute of Robotics and Automatic Information System, Nankai University, Tianjin 300350, China
Interests: motion planning and control; mobile robots; teleoperated robots
Prof. Dr. Zheng Chen

E-Mail Website
Guest Editor
The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
Interests: advanced control of robotic and mechatronic systems; nonlinear adaptive robust control; motion control; trajectory planning; telerobotics; hydraulic system; precision mechatronic system; soft actuator and robot; mobile manipulator; underwater robot; exoskeleton
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Model predictive control is an effective approach to control nonlinear constrained dynamic systems . Due to the remarkable online optimization capability, in the past decades, model predictive control has rapidly developed in both mathematical theory and industrial application. Nowadays, it is believed that advanced model predictive control approaches are also promising to play a pivotal role in sensing, robotics, mechatronics and other related industrial scenarios.

Topic Included：

  • Nonlinear predictive control of hybrid systems;
  • Multimodal nonlinear predictive control;
  • Fuzzy and neural network predictive control;
  • Adaptative predictive control;
  • Predictive control for fast dynamics;
  • Optimization algorithms for model predictive control;
  • Heuristic optimization for model predictive control;
  • Real industrial applications;
  • Real-time model predictive implementation;
  • Model predictive control for NCSs under cyberattacks;
  • Machine learning and artificial intelligence for model predictive control.

Dr. Chuxiong Hu
Dr. Ze Wang
Dr. Mingxing Yuan
Prof. Dr. Zheng Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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

  • model predictive control
  • system modeling and prediction
  • intelligent sensing and control
  • real-time optimization
  • nonlinear and constrained systems
  • robotics and mechatronics

Published Papers (3 papers)

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Research

19 pages, 1866 KiB  
Article
Computationally Efficient Continuous-Time Model Predictive Control of a 2-DOF Helicopter via B-Spline Parameterization
by Boris Rohaľ-Ilkiv, Martin Gulan and Peter Minarčík
Sensors 2023, 23(9), 4463; https://doi.org/10.3390/s23094463 - 03 May 2023
Viewed by 1357
Abstract
This paper investigates one way to reduce the computational burden of continuous-time model predictive control (MPC) laws by representing the input/output signals and related models using B-spline functions. Such an approximation allows to implement the resulting feedback control law more efficiently, requiring less [...] Read more.
This paper investigates one way to reduce the computational burden of continuous-time model predictive control (MPC) laws by representing the input/output signals and related models using B-spline functions. Such an approximation allows to implement the resulting feedback control law more efficiently, requiring less online computational effort. As a result, the proposed controller formulates the control signals as continuous polynomial spline functions. All constraints assumed over the prediction horizon are then expressed as constraints acting on the B-splines control polygon vertices. The performance of the proposed theoretical framework has been demonstrated with several real-time experiments using the well-known 2-DOF laboratory helicopter setup. The aim of the presented experiments was to track given step-like reference trajectories for pitch and yaw angles under notable parameter uncertainties. In order to suppress the influence of uncertainties, the control algorithm is implemented in an adaptive mode, equipped with the recursive least squares (RLS) estimation of model parameters and with the adaptation of stabilizing terminal set and terminal cost calculations. Thanks to the presented framework, it is possible to significantly reduce the computational burden, measured by the number of decision variables and input constrains, indicating the potential of the proposed concept for real-time applications, even when using embedded control hardware. Full article
(This article belongs to the Special Issue Model Predictive Control in Sensing and Robotic- Methods and Applications)
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23 pages, 1633 KiB  
Article
Wind Preview-Based Model Predictive Control of Multi-Rotor UAVs Using LiDAR
by Arthur P. Mendez, James F. Whidborne and Lejun Chen
Sensors 2023, 23(7), 3711; https://doi.org/10.3390/s23073711 - 03 Apr 2023
Cited by 3 | Viewed by 2058
Abstract
Autonomous outdoor operations of Unmanned Aerial Vehicles (UAVs), such as quadrotors, expose the aircraft to wind gusts causing a significant reduction in their position-holding performance. This vulnerability becomes more critical during the automated docking of these vehicles to outdoor charging stations. Utilising real-time [...] Read more.
Autonomous outdoor operations of Unmanned Aerial Vehicles (UAVs), such as quadrotors, expose the aircraft to wind gusts causing a significant reduction in their position-holding performance. This vulnerability becomes more critical during the automated docking of these vehicles to outdoor charging stations. Utilising real-time wind preview information for the gust rejection control of UAVs has become more feasible due to the advancement of remote wind sensing technology such as LiDAR. This work proposes the use of a wind-preview-based Model Predictive Controller (MPC) to utilise remote wind measurements from a LiDAR for disturbance rejection. Here a ground-based LiDAR unit is used to predict the incoming wind disturbance at the takeoff and landing site of an autonomous quadrotor UAV. This preview information is then utilised by an MPC to provide the optimal compensation over the defined horizon. Simulations were conducted with LiDAR data gathered from field tests to verify the efficacy of the proposed system and to test the robustness of the wind-preview-based control. The results show a favourable improvement in the aircraft response to wind gusts with the addition of wind preview to the MPC; An 80% improvement in its position-holding performance combined with reduced rotational rates and peak rotational angles signifying a less aggressive approach to increased performance when compared with only feedback based MPC disturbance rejection. System robustness tests demonstrated a 1.75 s or 120% margin in the gust preview’s timing or strength respectively before adverse performance impact. The addition of wind-preview to an MPC has been shown to increase the gust rejection of UAVs over standard feedback-based MPC thus enabling their precision landing onto docking stations in the presence of wind gusts. Full article
(This article belongs to the Special Issue Model Predictive Control in Sensing and Robotic- Methods and Applications)
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20 pages, 2702 KiB  
Article
Dynamic QoS Prediction Algorithm Based on Kalman Filter Modification
by Yunfei Yan, Peng Sun, Jieyong Zhang, Yutang Ma, Liang Zhao and Yueyi Qin
Sensors 2022, 22(15), 5651; https://doi.org/10.3390/s22155651 - 28 Jul 2022
Cited by 1 | Viewed by 1163
Abstract
With the widespread adoption of service-oriented architectures (SOA), services with the same functionality but the different Quality of Service (QoS) are proliferating, which is challenging the ability of users to build high-quality services. It is often costly for users to evaluate the QoS [...] Read more.
With the widespread adoption of service-oriented architectures (SOA), services with the same functionality but the different Quality of Service (QoS) are proliferating, which is challenging the ability of users to build high-quality services. It is often costly for users to evaluate the QoS of all feasible services; therefore, it is necessary to investigate QoS prediction algorithms to help users find services that meet their needs. In this paper, we propose a QoS prediction algorithm called the MFDK model, which is able to fill in historical sparse QoS values by a non-negative matrix decomposition algorithm and predict future QoS values by a deep neural network. In addition, this model uses a Kalman filter algorithm to correct the model prediction values with real-time QoS observations to reduce its prediction error. Through extensive simulation experiments on the WS-DREAM dataset, we analytically validate that the MFDK model has better prediction accuracy compared to the baseline model, and it can maintain good prediction results under different tensor densities and observation densities. We further demonstrate the rationality of our proposed model and its prediction performance through model ablation experiments and parameter tuning experiments. Full article
(This article belongs to the Special Issue Model Predictive Control in Sensing and Robotic- Methods and Applications)
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