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Machines
Special Issues
Nonlinear and Adaptive Control of Intelligent Machines
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Nonlinear and Adaptive Control of Intelligent Machines

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 6270

Special Issue Editor

Dr. Kwanho You

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
Interests: nonlinear and optimal control; radio resource management in wireless communication; adaptive optimization methods in nonlinear process; estimation theory

Special Issue Information

Dear Colleagues,

The control of modern intelligent machines presents new challenges due to their complexity, nonlinearity, and uncertainty. Conventional control methods may not be sufficient to address these challenges, and, therefore, new approaches based on soft computing and artificial intelligence have been developed. Soft computing and artificial intelligence techniques have been applied successfully to many areas of machine control, including robotics, automation, process control, and power systems. This Special Issue will cover recent advances and new directions in the theory and application of non-linear and adaptive control for intelligent machines, including robots, unmanned vehicle systems, autonomous vehicles, and more. We are particularly interested in papers that address challenges, such as modeling and control of complex systems, learning-based control, control of uncertain systems, and integration of machine learning techniques with control methods.

We encourage submissions of original research articles, reviews, and short communications that present innovative approaches, novel theoretical developments, and real-world applications in the field of non-linear and adaptive control for intelligent machines, including practical experimental results. We are looking for the state-of-the-art advances in research with the topics related to machine control, including, but not limited to, the following:

  • Non-linear and adaptive control algorithms for robotic systems, unmanned aerial vehicles, and other intelligent machines.
  • Real-time optimization of machine control systems.
  • Machine learning and artificial intelligence techniques for control design.
  • Control strategies for complex systems.
  • Design and implementation of advanced sensor and actuator systems for machine control.
  • Intelligent adaptive learning and control for industrial machines.
  • Adaptive observer-based non-linear control techniques.
  • Non-linear observer-based design approach.
  • Autonomous machine dynamics and control.

Prof. Dr. Kwanho You
Guest Editor

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. 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.

Published Papers (5 papers)

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Research

27 pages, 6470 KiB  
Article
Enhanced Output Tracking Control for Direct Current Electric Motor Systems Using Bio-Inspired Optimization
by Hugo Yañez-Badillo, Francisco Beltran-Carbajal, Ivan Rivas-Cambero, Antonio Favela-Contreras, Jose Humberto Arroyo-Nuñez and Juan Nabor Balderas-Gutierrez
Machines 2023, 11(11), 1006; https://doi.org/10.3390/machines11111006 - 02 Nov 2023
Viewed by 871
Abstract
In this paper, an efficient output reference trajectory tracking control scheme for direct current electric motor systems based on bio-inspired optimization is proposed. The differential flatness structural property of the electric motor along with dynamic tracking error compensation is suitably exploited for the [...] Read more.
In this paper, an efficient output reference trajectory tracking control scheme for direct current electric motor systems based on bio-inspired optimization is proposed. The differential flatness structural property of the electric motor along with dynamic tracking error compensation is suitably exploited for the backstepping control design. Off-line optimal selection of control parameters, implementing bio-inspired ant colony and particle swarm optimization algorithms, is addressed by minimizing an objective function where the decision variables are the tracking error and control input effort. A novel adaptive version of the control approach based on B-spline artificial neural networks is provided as well. The introduced flat output feedback tracking control design approach can be further extended for other differentially flat dynamic systems. Considerably perturbed, diverse velocity and position reference trajectory tracking scenarios are developed for demonstrating the acceptable closed-loop system performance. The results prove the efficient and robust tracking of the position and velocity reference profiles planned for the operation of the controlled electric motor system under variable torque disturbances using bio-inspired optimization. Full article
(This article belongs to the Special Issue Nonlinear and Adaptive Control of Intelligent Machines)
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22 pages, 5887 KiB  
Article
A Sliding Mode Approach-Based Adaptive Steering Control Algorithm for Path Tracking of Autonomous Mobility with Weighted Injection
by Sehwan Kim and Kwangseok Oh
Machines 2023, 11(10), 972; https://doi.org/10.3390/machines11100972 - 18 Oct 2023
Viewed by 968
Abstract
The increasing complexity of mathematical models developed as part of the recent advancements in autonomous mobility platforms has led to an escalation in uncertainty. Despite the intricate nature of such models, the detection, decision, and control methods for autonomous mobility path tracking remain [...] Read more.
The increasing complexity of mathematical models developed as part of the recent advancements in autonomous mobility platforms has led to an escalation in uncertainty. Despite the intricate nature of such models, the detection, decision, and control methods for autonomous mobility path tracking remain critical. This study aims to achieve path tracking based on pixel-based control errors without parameters in the mathematical model. The proposed approach entails deriving control errors from a multi-particle filter based on a camera, estimating the error dynamics coefficients through a recursive least squares (RLS) approach, and using the sliding mode approach and weighted injection to formulate a cost function that leverages the estimated coefficients and control errors. The resultant adaptive steering control expedites the convergence of control errors towards zero by determining the magnitude of the injection variable based on the control errors and the finite-time convergence condition. The efficacy of the proposed approach is evaluated through an S-curved and elliptical path using autonomous mobility equipped with a single steering and driving module. The results demonstrate the capability of the approach to reasonably track target paths through driving and steering control facilitated by a multi-particle filter and a lidar-based obstacle detection system. Full article
(This article belongs to the Special Issue Nonlinear and Adaptive Control of Intelligent Machines)
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16 pages, 1014 KiB  
Communication
Sliding Mode Control for Sensorless Speed Tracking of PMSM with Whale Optimization Algorithm and Extended Kalman Filter
by Ahyeong Choi, Hyeongki Ahn, Yoonuh Chung and Kwanho You
Machines 2023, 11(9), 851; https://doi.org/10.3390/machines11090851 - 22 Aug 2023
Viewed by 751
Abstract
This paper proposes a sensorless speed control strategy for a permanent magnet synchronous motor system. Sliding mode control with a whale optimization algorithm was developed for robustness and chattering reduction. To estimate the position and speed of the rotor, an extended Kalman filter [...] Read more.
This paper proposes a sensorless speed control strategy for a permanent magnet synchronous motor system. Sliding mode control with a whale optimization algorithm was developed for robustness and chattering reduction. To estimate the position and speed of the rotor, an extended Kalman filter using Gaussian process regression was designed. In this controller, the whale optimization method adjusts the switching gain to minimize the tracking error. However, it provides chattering reduction and robustness, owing to the adaptive gain. The extended Kalman estimator calculates the rotor speed by using the current and voltage of the motor as an observer. The observer ensures the high reliability and low cost of the controller. The noise covariance and weight matrices that validated the performance of the estimation were optimized using a regression algorithm. The Gaussian process regression was trained to approximate the best covariance and matrices from the results of the motor controller execution. The performance of the proposed method was demonstrated through simulations under several conditions of tracking speed and load torque changes. Full article
(This article belongs to the Special Issue Nonlinear and Adaptive Control of Intelligent Machines)
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18 pages, 1758 KiB  
Article
A qLPV-MPC Control Strategy for Trajectory Tracking of Quadrotors
by Daniel Rodriguez-Guevara, Antonio Favela-Contreras and Oscar Julian Gonzalez-Villarreal
Machines 2023, 11(7), 755; https://doi.org/10.3390/machines11070755 - 19 Jul 2023
Cited by 1 | Viewed by 1258
Abstract
This article proposes a model predictive control (MPC) strategy for a quadrotor drone trajectory tracking based on a compact state-space model based on a quasi-linear parameter varying (qLPV) representation of the nonlinear quadrotor. The use of a qLPV representation allows for faster execution [...] Read more.
This article proposes a model predictive control (MPC) strategy for a quadrotor drone trajectory tracking based on a compact state-space model based on a quasi-linear parameter varying (qLPV) representation of the nonlinear quadrotor. The use of a qLPV representation allows for faster execution times, which can be suitable for real-time applications and for solving the optimization problem using quadratic programming (QP). The estimation of future values of the scheduling parameters along the prediction horizon is made by using the planned trajectory based on the previous optimal control actions. The performance of the proposed approach is tested by following different trajectories in simulation to show the effectiveness of the proposed control scheme. Full article
(This article belongs to the Special Issue Nonlinear and Adaptive Control of Intelligent Machines)
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25 pages, 4547 KiB  
Article
Adaptive Control Strategy for a Pumping System Using a Variable Frequency Drive
by Dorin Bordeasu, Octavian Prostean, Ioan Filip and Cristian Vasar
Machines 2023, 11(7), 688; https://doi.org/10.3390/machines11070688 - 30 Jun 2023
Viewed by 1664
Abstract
Currently, the most implemented solution for driving a pumping system (PS) at variable speed is using a variable frequency drive (VFD). Because most of the VFDs have integrated only one proportional-integral (PI) frequency controller whose parameters (proportional gain/the integration time) can be off-line [...] Read more.
Currently, the most implemented solution for driving a pumping system (PS) at variable speed is using a variable frequency drive (VFD). Because most of the VFDs have integrated only one proportional-integral (PI) frequency controller whose parameters (proportional gain/the integration time) can be off-line tuned but cannot be changed during real-time operation, and many PS must operate in different regimes (at variable speed, variable flow rate, at variable pumping head or even at variable power, e.g., those powered by renewable energy sources), the adaptive control strategy proposed in this paper overcomes these problems with very good performances. The proposed adaptive control strategy uses only simple PI controllers for managing several operating regimes. The adaptive character is not ensured by re-tuning the PI controller parameters as in self-tuning controllers but by readjusting the control law through a change in the control loop depending on the controlled output of the process (pump speed, pump discharge, pumping head or absorbed power). The deviations of the mentioned controlled outputs from their referenced values are converted into electrical frequency error (the deviation of the current value from its reference), which is the regular input to the already tuned controller; therefore, no controller re-tuning is required. Full article
(This article belongs to the Special Issue Nonlinear and Adaptive Control of Intelligent Machines)
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