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Processes
Special Issues
Adaptive Control: Design and Analysis
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Adaptive Control: Design and Analysis

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 4994

Special Issue Editors

Dr. Weicun Zhang

E-Mail Website
Guest Editor
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: adaptive control; self-tuning control; multiple model adaptive control; multiple model adaptive estimation; stability analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hassan el Fadil

E-Mail Website
Guest Editor
National School of Applied Sciences (ENSA), Ibn Tofail University, Kénitra 14000, Morocco
Interests: nonlinear control systems; adaptive control; nonlinear observers
Prof. Dr. Quanmin Zhu

E-Mail Website
Guest Editor
School of Engineering, University of the West of England, Frenchay Campus, Coldharbour Lane, Bristol BS16 1QY, UK
Interests: complex dynamic system identification and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

From the viewpoint of application, adaptive control originated from the gain-scheduling control of high-performance aircraft in the early 1950s. Model reference adaptive control (MRAC) was suggested by Whitaker et al. to solve the autopilot control problem.

From the viewpoint of theory research into the optimal control of stochastic systems with unknown or time-varying parameters, self-tuning control (STC) was suggested by Kalman, and then connected with applications through the pioneering work of Astrom and Wittenmark.

The aim of this Special Issue is to explore recent technological developments in adaptive control (design methods and theoretical analysis), especially for nonlinear stochastic processes such as robotic systems, manufacturing systems, transportation systems, power systems, chemical systems, and more.

Original research articles and reviews are welcome in this Special Issue. Research areas may include (but are not limited to) the following:

  • Model reference adaptive control;
  • Self-tuning adaptive control;
  • Multiple model adaptive control;
  • Intelligent adaptive control;
  • Robust adaptive control;
  • Adaptive sliding-mode control.

Dr. Weicun Zhang
Prof. Dr. Hassan el Fadil
Prof. Dr. Quanmin Zhu
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. Processes 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

  • adaptive control systems
  • design and analysis
  • control system simulation
  • process modeling/identification
  • control system application
  • intelligent adaptive control
  • stability and convergence

Published Papers (5 papers)

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Research

21 pages, 764 KiB  
Article
MK-DCCA-Based Fault Diagnosis for Incipient Faults in Nonlinear Dynamic Processes
by Junzhou Wu, Mei Zhang and Lingxiao Chen
Processes 2023, 11(10), 2927; https://doi.org/10.3390/pr11102927 - 07 Oct 2023
Cited by 1 | Viewed by 744
Abstract
Incipient fault diagnosis is particularly important in process industrial systems, as its early detection helps to prevent major accidents. Against this background, this study proposes a combined method of mixed kernel principal components analysis and dynamic canonical correlation analysis (MK-DCCA). The robust generalization [...] Read more.
Incipient fault diagnosis is particularly important in process industrial systems, as its early detection helps to prevent major accidents. Against this background, this study proposes a combined method of mixed kernel principal components analysis and dynamic canonical correlation analysis (MK-DCCA). The robust generalization performance of this approach is demonstrated through experimental validation on a randomly generated dataset. Furthermore, comparative experiments were conducted on a CSTR Simulink model, comparing the MK-DCCA method with DCCA and DCVA methods, demonstrating its excellent detection performance for incipient faults in nonlinear and dynamic systems. Meanwhile, fault identification experiments were conducted, validating the high accuracy of the fault identification method based on contribution. The experimental findings demonstrate that the method possesses a certain industrial significance and academic relevance. Full article
(This article belongs to the Special Issue Adaptive Control: Design and Analysis)
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22 pages, 2176 KiB  
Article
Improved Time-Varying BLF-Based Tracking Control of a Position-Constrained Robot
by Tan Zhang and Jinzhong Zhang
Processes 2023, 11(9), 2785; https://doi.org/10.3390/pr11092785 - 18 Sep 2023
Viewed by 695
Abstract
In this work, one improved symmetric time-variant logarithmic barrier, Lyapunov function (BLF), is developed for the first time to handle the state constraint problem of nonlinear systems. It is universal in the sense that the improved barrier function is a general one that [...] Read more.
In this work, one improved symmetric time-variant logarithmic barrier, Lyapunov function (BLF), is developed for the first time to handle the state constraint problem of nonlinear systems. It is universal in the sense that the improved barrier function is a general one that can be used not only in systems with constrained requirements but also in systems without constrained requirements, without altering the designed controller. First of all, the n-link robotic system is transformed into a kind of multi-input and multi-output (MIMO) system. Then, a trajectory tracking control scheme is designed by combining the improved time-variant logarithmic BLF with the disturbance observer to solve the problems of model uncertainty and position constraint for the robotic system. We give that under the proposed controller, all the robotic system’s error vectors can trend to the equilibrium point asymptotically while the constraint conditions on the position are always met. Finally, the effectiveness of the presented scheme is indicated by completing two simulation experiment cases. Full article
(This article belongs to the Special Issue Adaptive Control: Design and Analysis)
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17 pages, 389 KiB  
Article
A Filtering-Based Stochastic Gradient Estimation Method for Multivariate Pseudo-Linear Systems Using the Partial Coupling Concept
by Ping Ma, Yuan Liu and Yiyang Chen
Processes 2023, 11(9), 2700; https://doi.org/10.3390/pr11092700 - 09 Sep 2023
Viewed by 435
Abstract
Solutions for enhancing parameter identification effects for multivariate equation-error systems in random interference and parameter coupling conditions are considered in this paper. For the purpose of avoiding the impact of colored noises on parameter identification precision, an appropriate filter is utilized to process [...] Read more.
Solutions for enhancing parameter identification effects for multivariate equation-error systems in random interference and parameter coupling conditions are considered in this paper. For the purpose of avoiding the impact of colored noises on parameter identification precision, an appropriate filter is utilized to process the autoregressive moving average noise. Then, the filtered system is transformed into a number of sub-identification models based on system output dimensions. Founded on negative gradient search, a new multivariate filtering algorithm employing a partial coupling approach is proposed, and a conventional gradient algorithm is derived for comparison. Parameter identification for multivariate equation-error systems has a high estimation accuracy and an efficient calculation speed with the application of the partial coupling approach and the data filtering method. Two simulations are performed to reveal the proposed method’s effectiveness. Full article
(This article belongs to the Special Issue Adaptive Control: Design and Analysis)
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20 pages, 9745 KiB  
Article
ROV Sliding Mode Controller Design and Simulation
by Fushen Ren and Qing Hu
Processes 2023, 11(8), 2359; https://doi.org/10.3390/pr11082359 - 05 Aug 2023
Cited by 1 | Viewed by 1251
Abstract
Underwater robots play a vital role in the exploration and development of marine resources and the inspection and maintenance of offshore platforms. In this paper, the motion control technology of ROV is studied, the kinematics and dynamics of ROV are analyzed, the kinematics [...] Read more.
Underwater robots play a vital role in the exploration and development of marine resources and the inspection and maintenance of offshore platforms. In this paper, the motion control technology of ROV is studied, the kinematics and dynamics of ROV are analyzed, the kinematics and dynamics models of ROV are established, and the degrees of freedom of the models are decouple according to the control requirements. The fluid damping coefficient of ROV was obtained using Fluent software, and an ROV control system based on sliding mode variable structure was designed. The saturation function was introduced into the sliding mode controller to reduce the adverse effects of buffeting. The classical PID controller, fuzzy PID controller, and sliding mode controller designed in this paper were simulated and analyzed by Simulink. A semi-physical simulation platform based on Unity3D was established. It can be seen from the simulation results and the pool experiment results that the performance of the sliding mode controller designed in this paper is better than the classical PID controller and the fuzzy PID controller. The sliding mode control method is used to control the ROV motion, which has better control effect and precision. Full article
(This article belongs to the Special Issue Adaptive Control: Design and Analysis)
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16 pages, 9071 KiB  
Article
Active Disturbance Rejection Control of Five-Phase Motor Based on Parameter Setting of Genetic Algorithm
by Rongtao Zeng, Jinghong Zhao, Yiyong Xiong and Xiangyu Luo
Processes 2023, 11(6), 1712; https://doi.org/10.3390/pr11061712 - 03 Jun 2023
Cited by 2 | Viewed by 833
Abstract
Five-phase induction motors have the characteristics of high torque density, low torque ripple, and flexible control, making them suitable for medium- and low-voltage power supply situations. However, with the expansion of application scenarios, five-phase motors need to cope with increasingly complex operating conditions. [...] Read more.
Five-phase induction motors have the characteristics of high torque density, low torque ripple, and flexible control, making them suitable for medium- and low-voltage power supply situations. However, with the expansion of application scenarios, five-phase motors need to cope with increasingly complex operating conditions. Five-phase motors for propeller propulsion will face various complex sea conditions during actual use, and five-phase motors for electric vehicles will also face various complex road conditions and operating requirements during use. Therefore, as a propulsion motor, its speed control system must have strong robustness and anti-disturbance performance. The use of traditional PI algorithms has problems, such as poor adaptability and inability to adapt to various complex working conditions, but the use of an active disturbance rejection controller (ADRC) can effectively solve these problems. However, due to the significant coupling between the variables of induction motors and the large number of parameters in the ADRC, tuning the parameters of the ADRC is complex. Traditional empirical tuning methods can only obtain a rough range of parameter values and may have significant errors. Therefore, this paper uses ADRC based on genetic algorithm(GAADRC) to tune the parameters of the control and design an objective function based on multi-objective optimization. The parameters to be adjusted were obtained through multiple iterations. The simulation and experimental results indicate that GAADRC has lower startup overshoot, faster adjustment time, and lower load/unload speed changes compared to the empirically tuned PI controller and ADRC. Meanwhile, using a genetic algorithm for motor ADRC parameter tuning can obtain optimal control parameters while the control parameter range is completely uncertain; therefore, the method proposed in this paper has strong practical value. Full article
(This article belongs to the Special Issue Adaptive Control: Design and Analysis)
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