Design and Control of Complex and Intelligent Systems

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

Deadline for manuscript submissions: 15 December 2024 | Viewed by 9322

Special Issue Editor


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Guest Editor
Electrical Engineering Department, Faculty of Engineering, University of Santiago of Chile, Las Sophoras 165, Estación Central, Santiago 9170124, Chile
Interests: system design; nonlinear systems; robotics; modeling; parameter identification; kinematics; dynamics; simulation; control; optimization; automation

Special Issue Information

Dear Colleagues,

Complex dynamic systems and intelligent systems are designed to learn, adapt, and make autonomous decisions in complex and changing environments. Therefore, they play a crucial role in various fields, including industry, biomedical engineering, agriculture, and space exploration, among others. Nowadays, due to the natural trend towards automation, their applications have expanded. However, complex dynamic systems and intelligent systems must be well-designed and controlled to provide the required performance. This way, these systems can increase their performance, operational safety, and contribute to energy efficiency, which has become a major concern in the world. Meeting the growing demand for energy while reducing costs, caring for sustainability and minimizing carbon emissions.

This special issue on "Design and Control of Complex and Intelligent Systems" includes high-quality papers focusing on the latest novel developments in modeling, simulation, design, control, optimization, application, and maintenance of all types of complex dynamic systems and intelligent systems.

Prof. Dr. Claudio Urrea
Guest Editor

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Keywords

  • system design
  • nonlinear systems
  • robotics
  • modeling
  • parameter identification
  • kinematics
  • dynamics
  • simulation
  • control
  • optimization
  • automation

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Published Papers (7 papers)

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Research

11 pages, 2053 KiB  
Article
Tracking Differentiator-Based Identification Method for Temperature Predictive Control of Uncooled Heating Processes
by Shan Hua, Gang Chen, Yanni Dong, Changhao Fan and Zhuoyun Nie
Processes 2024, 12(10), 2137; https://doi.org/10.3390/pr12102137 - 1 Oct 2024
Viewed by 465
Abstract
The temperature control of uncooled heating processes presents challenges due to a substantial lag and the absence of active cooling mechanisms, which can lead to overshoot and oscillations. To address these issues, we propose an anti-disturbance identification method based on a tracking differentiator [...] Read more.
The temperature control of uncooled heating processes presents challenges due to a substantial lag and the absence of active cooling mechanisms, which can lead to overshoot and oscillations. To address these issues, we propose an anti-disturbance identification method based on a tracking differentiator (TD) and an input-constrained temperature predictive control (ICTPC) strategy. Our approach specifically considers the impact of unknown disturbances on model identification within a second-order heating process. By employing a TD to differentiate the input and output signals, we effectively minimize the identification error caused by low-frequency disturbances, yielding a robust anti-disturbance identification technique. Following this, we establish input constraints to limit the amplitude and variation of the control input, ensuring a more controlled and predictable system response. Using the identified model, an ICTPC algorithm is designed to achieve stable temperature control in uncooled heating processes. Experimental results from a typical uncooled heating system demonstrate that our method not only significantly reduces overshoot but also effectively mitigates temperature fluctuations, leading to enhanced control performance and system stability. This study provides a practical solution for temperature control in systems without cooling capabilities, offering substantial improvements in the efficiency and quality of industrial production processes. Full article
(This article belongs to the Special Issue Design and Control of Complex and Intelligent Systems)
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15 pages, 5784 KiB  
Article
Model Predictive Control for Level Control of a Conical Tank
by Karina Montaluisa, Luis Vargas, Jacqueline Llanos and Paola Velasco
Processes 2024, 12(8), 1702; https://doi.org/10.3390/pr12081702 - 14 Aug 2024
Viewed by 903
Abstract
Conical tanks have a high application rate in industrial processes, especially in colloidal mills, chemical processes, and food processing. The use of conical tanks presents significant benefits because they contribute to sedimentation and reduce the accumulation of impurities compared to conventional cylindrical tanks. [...] Read more.
Conical tanks have a high application rate in industrial processes, especially in colloidal mills, chemical processes, and food processing. The use of conical tanks presents significant benefits because they contribute to sedimentation and reduce the accumulation of impurities compared to conventional cylindrical tanks. However, level control of a conical tank due to its shape requires advanced strategies to guarantee efficient control. In this research, a model predictive control (MPC) method was designed and implemented for the level control of a conical tank on a laboratory scale. To evaluate the performance of the controller, it was compared with a traditional proportional–integral (PI) controller, and the rise time, settling time, overshoot, and error in the steady state were analyzed when different set point changes were tested. In addition, the system was subjected to disturbances, and the MPC demonstrated better performance in a transient state, as well as smooth and stable action controls that allowed for an increase in the useful life of the actuator. In addition, an interactive graphical interface was developed that allowed a dynamic response in a real plant to be experienced; this provides an academic tool for designing control strategies before implementation in a real process. Full article
(This article belongs to the Special Issue Design and Control of Complex and Intelligent Systems)
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13 pages, 625 KiB  
Article
Internal Model Control Design for Nonlinear Systems Based on Inverse Dynamic Takagi–Sugeno Fuzzy Model
by Karama Khamis Karama and Cenk Ulu
Processes 2024, 12(7), 1334; https://doi.org/10.3390/pr12071334 - 27 Jun 2024
Viewed by 794
Abstract
In recent years, applications of inverse model-based control techniques have experienced significant growth in popularity and have been widely used in engineering applications, mainly in nonlinear control system design problems. In this study, a novel fuzzy internal model control (IMC) structure is presented [...] Read more.
In recent years, applications of inverse model-based control techniques have experienced significant growth in popularity and have been widely used in engineering applications, mainly in nonlinear control system design problems. In this study, a novel fuzzy internal model control (IMC) structure is presented for single-input-single-output (SISO) nonlinear systems. The proposed structure uses the forward and inverse dynamic Takagi–Sugeno (D-TS) fuzzy models of the nonlinear system within the IMC framework for the first time in literature. The proposed fuzzy IMC is obtained in a two-step procedure. A SISO nonlinear system is first approximated using a D-TS fuzzy system, of which the rule consequents are linearized subsystems derived from the nonlinear system. A novel approach is used to achieve the exact inversion of the SISO D-TS fuzzy model, which is then utilized as a control element within the IMC framework. In this way, the control design problem is simplified to the inversion problem of the SISO D-TS fuzzy system. The provided simulation examples illustrate the efficacy of the proposed control method. It is observed that SISO nonlinear systems effectively track the desired output trajectories and exhibit significant disturbance rejection performance by using the proposed control approach. Additionally, the results are compared with those of the proportional-integral-derivative control (PID) method, and it is shown that the proposed method exhibits better performance than the classical PID controller. Full article
(This article belongs to the Special Issue Design and Control of Complex and Intelligent Systems)
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29 pages, 5197 KiB  
Article
Metaheuristic Optimization Algorithm Based Cascaded Control Schemes for Nonlinear Ball and Balancer System
by Farhan Zafar, Suheel Abdullah Malik, Tayyab Ali, Amil Daraz, Atif M. Alamri, Salman A. AlQahtani and Farkhunda Bhatti
Processes 2024, 12(2), 291; https://doi.org/10.3390/pr12020291 - 29 Jan 2024
Cited by 1 | Viewed by 1333
Abstract
The ball and balancer system is a popular research platform for studying underactuated mechanical systems and developing control algorithms. It is a well-known two-dimensional balancing problem that has been addressed by a variety of controllers. This research work proposes two controllers that are [...] Read more.
The ball and balancer system is a popular research platform for studying underactuated mechanical systems and developing control algorithms. It is a well-known two-dimensional balancing problem that has been addressed by a variety of controllers. This research work proposes two controllers that are proportional integral derivative-second derivative-proportional integrator (PIDD2-PI) controller and tilt integral derivative with filter (TID-F) controller in a multivariate, electromechanical, and nonlinear under-actuated ball and balancer system. Integral Time Absolute Error (ITAE) is an objective function used for designing controllers because of its ability to be more sensitive to overshooting as well as reduced settling time and steady-state error. As part of the analysis, four metaheuristic optimization algorithms are compared in the optimization of proposed control strategies for cascaded control of the ball and balancer system. The algorithms are the Grey Wolf optimization algorithm (GWO), Cuckoo Search algorithm (CSA), Gradient Base Optimization (GBO), and Whale Optimization Algorithm (WOA). The effectiveness of proposed controllers PIDD2-PI and TID-F is investigated to be better in terms of transient time response than proportional integral derivative (PID), proportional integral-derivative (PI-D), proportional integral-proportional derivative (PI-PD) and proportional integral derivative-second derivative-proportional derivative (PIDD2-PD). Moreover, these two proposed controllers have also been compared with recently published work. During the analysis, it is shown that the proposed control strategies exhibit significantly greater robustness and dynamic responsiveness compared to other structural controllers. The proposed controller WOA-PIDD2-PI reduced the 73.38% settling time and 88.16% rise time compared to classical PID. The other proposed controller GWO-TID-F reduced 58.06% the settling time and 26.96% rise time compared to classical PID. These results show that proposed controllers are particularly distinguished in terms of rise time, settling time, maximum overshoot, and set-point tracking. Full article
(This article belongs to the Special Issue Design and Control of Complex and Intelligent Systems)
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23 pages, 10094 KiB  
Article
Distributed Cooperative Tracking Control Strategy for Virtual Coupling Trains: An Event-Triggered Model Predictive Control Approach
by Zhongqi Li, Lingyu Zhong, Hui Yang and Liang Zhou
Processes 2023, 11(12), 3293; https://doi.org/10.3390/pr11123293 - 24 Nov 2023
Cited by 2 | Viewed by 1097
Abstract
Virtual coupling (VC) technology has received much attention because of its significant advantages in enhancing the railway transport capacity; it achieves efficient train coupling operation through advanced communication technology. However, due to the uncertainty of the operating environment, a stable and effective control [...] Read more.
Virtual coupling (VC) technology has received much attention because of its significant advantages in enhancing the railway transport capacity; it achieves efficient train coupling operation through advanced communication technology. However, due to the uncertainty of the operating environment, a stable and effective control system is the key enabler for realization. In this paper, an event-triggered distributed model predictive control (ET-DMPC) method is proposed for the cooperative tracking control of virtual coupling trains (VCTS), considering resource limitations and multiple constraints. Firstly, a distributed model predictive control (DMPC) framework is designed. Based on the established VCTS dynamics model of the dual-leader communication topology, a distributed optimization objective function and safety constraints containing state information of the neighboring train system are constructed. Secondly, due to the limitations of communication and computational resources, the event triggering (ET) mechanism is further introduced, and an ET-DMPC method suitable for VCTS is proposed. The trigger condition of each unit train is designed on the premise of guaranteeing system stability, under which the system can guarantee the input-state stability (ISS), and the recursive feasibility of the system is proven via theoretical analysis. Finally, the VCTS composed of four CRH380A unit trains is used as the control object for simulation experiments, and through two sets of experimental simulation analysis, the effectiveness of the proposed method is verified. Full article
(This article belongs to the Special Issue Design and Control of Complex and Intelligent Systems)
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27 pages, 3933 KiB  
Article
Improving Exoskeleton Functionality: Design and Comparative Evaluation of Control Techniques for Pneumatic Artificial Muscle Actuators in Lower Limb Rehabilitation and Work Tasks
by Claudio Urrea and Rayko Agramonte
Processes 2023, 11(12), 3278; https://doi.org/10.3390/pr11123278 - 23 Nov 2023
Viewed by 1903
Abstract
The study of lower limbs has become relevant in recent years. Lower limbs have several classifications, but the most widespread categories are robots for patient rehabilitation and robots for work tasks. Two of the main pillars in the development of exoskeletons are actuators [...] Read more.
The study of lower limbs has become relevant in recent years. Lower limbs have several classifications, but the most widespread categories are robots for patient rehabilitation and robots for work tasks. Two of the main pillars in the development of exoskeletons are actuators and control strategies. Pneumatic artificial muscles are similar to human muscles in their function. This work focuses on this similarity to develop control techniques for this type of actuator. The purpose of this investigation is to design, evaluate, and compare the effectiveness of three different control systems—the proportional–integrative–derivative (PID) system, the sliding mode control (SMC) system, and the fuzzy logic controller (FLC) system—in executing precise trajectory tracking using an exoskeleton and including very realistic dynamic considerations. This study aims to design and implement these controllers and assess their performance in following three distinct trajectories, thereby determining the most efficient and reliable control method for exoskeleton motion. Additionally, the analysis centers on both the response of the controllers to external perturbations and the reaction of the controllers when the time delay inherent to their dynamic is added to the mathematical model. Finally, the results are compared, revealing through the analysis of performance indexes and time response that the FLC is the controller that exhibits the best global results in the tracking of the different trajectories. This work demonstrates that, for the system in question, the action of adding a time delay in the actuator causes the FLC and PID controllers to maintain a similar response, which is obtained without the delay action, in contrast to the system with an SMC controller. However, the same does not occur when including other dynamic factors, such as disturbances external to the system. Full article
(This article belongs to the Special Issue Design and Control of Complex and Intelligent Systems)
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26 pages, 7499 KiB  
Article
Research on Path Tracking and Yaw Stability Coordination Control Strategy for Four-Wheel Independent Drive Electric Trucks
by Feng Gao, Fengkui Zhao and Yong Zhang
Processes 2023, 11(8), 2473; https://doi.org/10.3390/pr11082473 - 17 Aug 2023
Cited by 4 | Viewed by 2022
Abstract
Achieving accurate path tracking and vehicle stability control for four-wheel independent drive electric trucks under complex driving conditions, such as high speed and low adhesion, remains a major challenge in current research. Poor coordination control may cause the vehicle to deviate from its [...] Read more.
Achieving accurate path tracking and vehicle stability control for four-wheel independent drive electric trucks under complex driving conditions, such as high speed and low adhesion, remains a major challenge in current research. Poor coordination control may cause the vehicle to deviate from its intended path and become unstable. To address this issue, this article proposes a coordinated control strategy consisting of a three-layer control framework. In the upper layer controller design, establish a linear quadratic regulator (LQR) path tracking controller to ensure precise steering control by eliminating steady-state errors through feedforward control. The middle layer controller utilizes the fractional order sliding mode control (FOSMC) yaw moment controller to calculate the additional yaw moment based on the steering angle of the upper input, utilizing the error of yaw rate and sideslip angle as the state variable. To collectively optimize the control system, establish a coordinated optimization objective function and utilize the hybrid genetic-particle swarm optimization algorithm (GA-PSO) to optimize the weight coefficient of LQR and sliding mode parameters of FOSMC, effectively improving the performance of the controller. In the lower layer torque distribution controller, use the quadratic programming method to achieve real-time optimal torque distribution based on tire utilization, which improves vehicle stability control. Through simulations conducted under four different working conditions, the proposed control scheme demonstrates a 15.54% to 23.17% improvement in tracking performance and a 10.83% to 23.88% optimization in vehicle driving stability compared to other control methods. This scheme provides a theoretical reference for path tracking and stability control in four-wheel independent drive electric trucks. Full article
(This article belongs to the Special Issue Design and Control of Complex and Intelligent Systems)
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