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        <title>Frontiers in Control Engineering | Control and Automation Systems section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/control-engineering/sections/control-and-automation-systems</link>
        <description>RSS Feed for Control and Automation Systems section in the Frontiers in Control Engineering journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-05-13T11:23:05.905+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2025.1645918</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2025.1645918</link>
        <title><![CDATA[Conflict-based model predictive control for multi-agent path finding experimentally validated on a magnetic planar drive system]]></title>
        <pubdate>2025-07-31T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Kai Janning</author><author>Abdalsalam Housin</author><author>Christopher Schulte</author><author>Frederik Erkens</author><author>Luca Frenken</author><author>Laura Herbst</author><author>Bastian Nießing</author><author>Robert H. Schmitt</author>
        <description><![CDATA[IntroductionThis work presents an approach to collision avoidance in multi-agent systems (MAS) by integrating Conflict-Based Search (CBS) with Model Predictive Control (MPC), referred to as Conflict-Based Model Predictive Control (CB-MPC).MethodsThe proposed method leverages the conflict-avoidance strengths of CBS to generate collision-free paths, which are then refined into dynamic reference trajectories using a minimum jerk trajectory optimizer and then used inside a MPC to follow the trajectories and to avoid collisions. This integration ensures real-time trajectory execution, preventing collisions and adapting to online changes. The approach is evaluated using a magnetic planar drive system for realistic multi-agent scenarios, demonstrating enhanced real-time responsiveness and adaptability. The focus is on the development of a motion planning algorithm and its validation in dynamic environments, which are becoming increasingly relevant in modern adaptive production sites.ResultsOn the MAS demonstrator with four active agents, ten different scenarios were created with varying degrees of complexity in terms of route planning. In addition, external disturbances that hinder the execution of the paths were simulated. All calculation and solution times were recorded and discussed. The result show that all scenarios could be successfully solved and executed., and the CB-MPC is therefore suitable for motion planning on the presented MAS demonstrator.DiscussionThe results show, that the CB-MPC is suitable for motion planning on the presented MAS demonstrator. The greatest limitation of the approach lies in scalability with regard to increasing the number of agents.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2024.1459399</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2024.1459399</link>
        <title><![CDATA[Workpiece temperature control in friction stir welding of Inconel 718 through integrated numerical analysis and process control]]></title>
        <pubdate>2024-10-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ahmed Abotaleb</author><author>Mohammed Al-Azba</author><author>Marwan Khraisheh</author><author>Yves Remond</author><author>Said Ahzi</author>
        <description><![CDATA[Friction stir welding (FSW) offers significant advantages over fusion welding, particularly for high-strength alloys like Inconel 718. However, achieving optimal surface quality in Inconel 718 FSW remains challenging due to its sensitivity to temperature fluctuations during welding. This study integrates finite element simulations, statistical analysis, and advanced control methodologies to enhance weld surface quality through adequate thermal management. High-fidelity simulations of the FSW process were conducted using a validated 3D transient COMSOL Multiphysics model, producing a comprehensive dataset correlating process parameters (rotational speed, axial force, and welding speed) with workpiece temperature. This dataset facilitated statistical analysis and parameter optimization through Analysis of variance (ANOVA) method, leading to a deeper understanding of process variables. A nonlinear state-space system model was subsequently developed using experimental data and the system identification toolbox in Matlab, incorporating domain-specific insights. This model was rigorously validated with an independent dataset to ensure predictive accuracy. Utilizing the validated model, tailored control strategies, including proportional-integral-derivative (PID) and model predictive control (MPC) in both single and multivariable configurations, were designed and evaluated. These control strategies excelled in maintaining welding temperatures within optimal ranges, demonstrating robustness in response times and disturbance handling. This precision in thermal management is poised to significantly refine the FSW process, enhancing both surface integrity and microstructural uniformity. The strategic implementation of these controls is anticipated to substantially improve the quality and consistency of welding outcomes.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2024.1402621</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2024.1402621</link>
        <title><![CDATA[Using reinforcement learning to autonomously identify sources of error for agents in group missions]]></title>
        <pubdate>2024-10-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Keishu Utimula</author><author>Ken-taro Hayaschi</author><author>Trevor J. Bihl</author><author>Kenta Hongo</author><author>Ryo Maezono</author>
        <description><![CDATA[When deploying agents to execute a mission with collective behavior, it is common for accidental malfunctions to occur in some agents. It is challenging to distinguish whether these malfunctions are due to actuator failures or sensor issues based solely on interactions with the affected agent. However, we humans know that if we cause a group behavior where other agents collide with a suspected malfunctioning agent, we can monitor the presence or absence of a positional change and identify whether it is the actuator (position changed) or the sensor (position unchanged) that is broken. We have developed artificial intelligence that can autonomously deploy such “information acquisition strategies through collective behavior” using machine learning. In such problems, the goal is to plan collective actions that result in differences between the hypotheses for the state [e.g., actuator or sensor]. Only a few of the possible collective behavior patterns will lead to distinguishing between hypotheses. The evaluation function to maximize the difference between hypotheses is therefore sparse, with mostly flat values across most of the domain. Gradient-based optimization methods are ineffective for this, and reinforcement learning becomes a viable alternative. By handling this maximization problem, our reinforcement learning surprisingly gets the optimal solution, resulting in collective actions that involve collisions to differentiate the causes. Subsequent collective behaviors, reflecting this situation awareness, seemed to involve other agents assisting the malfunctioning agent.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2024.1452442</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2024.1452442</link>
        <title><![CDATA[State dependent vagus nerve stimulation for targeted plasticity therapy: challenges and considerations]]></title>
        <pubdate>2024-10-11T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Bharadwaj Nandakumar</author><author>Ramanamurthy V. Mylavarapu</author><author>Rivaldo Harris</author><author>Eric R. Albuquerque</author><author>Zihan Yan</author><author>Cameron Herter</author><author>David W. McMillan</author><author>Vivek V. Kanumuri</author><author>Patrick D. Ganzer</author>
        <description><![CDATA[Targeted plasticity therapy (TPT) utilizes vagus nerve stimulation (VNS) to promote improvements in function following neurological injury and disease. During TPT, a brief burst of VNS induces neuromodulator release, which when paired with relevant behavioral events can influence functionally relevant neuroplasticity. Functional improvements following TPT are therefore in part mediated by neuromodulator signaling. Unfortunately, comorbidities associated with neurological disease often result in altered cognitive states that can influence neuromodulator signaling, potentially impeding neuroplasticity induced by TPT. Aside from altered cognitive states, cardiorespiratory rhythms also affect neuromodulator signaling, due to the vagus nerve’s role in relaying visceral sensory information from the cardiovascular and respiratory systems. Moreover, precise VNS delivery during specific periods of the cardiorespiratory rhythms may further improve TPT. Ultimately, understanding the impact of patient-specific states on neuromodulator signaling may likely facilitate optimized VNS delivery, paving the way for personalized neuromodulation during TPT. Overall, this review explores challenges and considerations for developing advanced TPT paradigms, focusing on altered cognitive states and cardiorespiratory rhythms. We specifically discuss the possible impact of these cognitive states and autonomic rhythms on neuromodulator signaling and subsequent neuroplasticity. Altered cognitive states (arousal deficits or pain) could affect VNS intensity, while cardiorespiratory rhythms may further inform optimized timing of VNS. We propose that understanding these interactions will lead to the development of personalized state dependent VNS paradigms for TPT.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2024.1394668</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2024.1394668</link>
        <title><![CDATA[Reinforcement learning for path planning of free-floating space robotic manipulator with collision avoidance and observation noise]]></title>
        <pubdate>2024-05-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ahmad Al Ali</author><author>Zheng H. Zhu</author>
        <description><![CDATA[This study introduces a novel approach for the path planning of a 6-degree-of-freedom free-floating space robotic manipulator, focusing on collision and obstacle avoidance through reinforcement learning. It addresses the challenges of dynamic coupling between the spacecraft and the robotic manipulator, which significantly affects control and precision in the space environment. An innovative reward function is introduced in the reinforcement learning framework to ensure accurate alignment of the manipulator’s end effector with its target, despite disturbances from the spacecraft and the need for obstacle and collision avoidance. A key feature of this study is the use of quaternions for orientation representation to avoid the singularities associated with conventional Euler angles and enhance the training process’ efficiency. Furthermore, the reward function incorporates joint velocity constraints to refine the path planning for the manipulator joints, enabling efficient obstacle and collision avoidance. Another key feature of this study is the inclusion of observation noise in the training process to enhance the robustness of the agent. Results demonstrate that the proposed reward function enables effective exploration of the action space, leading to high precision in achieving the desired objectives. The study provides a solid theoretical foundation for the application of reinforcement learning in complex free-floating space robotic operations and offers insights for future space missions.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2024.1343851</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2024.1343851</link>
        <title><![CDATA[Self-paced heart rate control during treadmill exercise for persons with gait impairment: a case study]]></title>
        <pubdate>2024-04-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hanjie Wang</author><author>Diana Guimaraes</author><author>Tobias Nef</author><author>Kenneth J. Hunt</author>
        <description><![CDATA[Introduction:A self-paced (SP) heart rate (HR) control system proposed in a previous study was found to be feasible for healthy participants. The aims of this work were to investigate whether the SP HR control system is feasible to achieve accurate HR control in a participant with gait impairments, and to assess its interaction with an existing motor-driven body weight support (BWS) system.Methods:One participant with cerebral palsy was recruited in this case study. Three preliminary tests were completed to determine the appropriate mean value and amplitude of the target heart rate curve, and to identify a customised heart rate response model. Two series of formal self-paced heart rate control tests were then conducted to investigate the influence of different heart rate compensators and the presence of the BWS system.Results:The customised heart rate controller achieved improved accuracy in heart rate control and reduced oscillation in the treadmill target speed: the root-mean-square heart rate tracking error (RMSE) was 2.38 beats per minute (bpm) vs. 3.91 bpm (customised controller vs. nominal controller), and the average power of changes in the treadmill target speed was 0.4 × 10−4 m2/s2 vs. 8.4 × 10−4 m2/s2. The BWS system resulted in improved HR tracking accuracy: RMSE on heart rate tracking was 3.02 bpm vs. 3.50 bpm (with BWS vs. without BWS). The BWS system had no influence on the automatic position control accuracy: RMSE on distance tracking was 0.0159 m vs. 0.0164 m.Conclusion:After customising the heart rate compensator, the self-paced heart rate control system is feasible to achieve accurate heart rate control in an individual with gait impairments, and it can correctly interact with the BWS system.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2023.1237759</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2023.1237759</link>
        <title><![CDATA[Data-driven non-parametric chance-constrained model predictive control for microgrids energy management using small data batches]]></title>
        <pubdate>2023-08-17T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Leon Babić</author><author>Marco Lauricella</author><author>Glenn Ceusters</author><author>Matthias Biskoping</author>
        <description><![CDATA[This paper presents a stochastic model predictive control approach combined with a time-series forecasting technique to tackle the problem of microgrid energy management in the face of uncertainty. The data-driven non-parametric chance constraint method is used to formulate chance constraints for stochastic model predictive control, while removing the dependency on probability density assumptions of uncertain variables and retaining the linear structure of the resulting optimization problem. The proposed approach is suitable for implementation on systems with limited computational power or limited memory storage, thanks to its simple linear structure and its ability to provide accurate results within pre-defined confidence levels, even when using small data batches. The proposed forecasting and stochastic model predictive control approaches are applied on a numerical example featuring a small grid-connected microgrid with PV generation, a battery storage system, and a non-controllable load, showing the ability to reduce costs by reducing the confidence level, and to satisfy pre-defined confidence levels.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2023.1228462</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2023.1228462</link>
        <title><![CDATA[Editorial: Insights in control and automation systems]]></title>
        <pubdate>2023-06-08T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Antonio Visioli</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2023.1185502</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2023.1185502</link>
        <title><![CDATA[A process-model-free method for model predictive control via a reference model-based proportional-integral-derivative controller with application to a thermal power plant]]></title>
        <pubdate>2023-04-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Gengjin Shi</author><author>Miaomiao Ma</author><author>Donghai Li</author><author>Yanjun Ding</author><author>Kwang Y. Lee</author>
        <description><![CDATA[Introduction: Model predictive control (MPC) is an advanced control strategy which can achieve fast reference tracking response and deal with process constraints, time delay and multivariable problems. However, thermal processes in coal-fired power plants are usually difficult to model accurately, which limits the application of MPC to thermal power plants.Methods: To solve the problem, this paper proposes a process-model-free method for MPC via a reference model (RM)-based controller, i.e., a desired dynamic equational (DDE) proportional-integral-derivative (PID) controller (DDE-PID).Results and Discussion: The DDE-PID can provide the design model and enhance the disturbance rejection ability for MPC. Simulations and results of field tests on a coal-fired unit show the superiorities of the proposed controller in reference tracking, disturbance rejection and robustness, which indicates the promising prospect of the field application of the MPC with DDE-PID, or MPC-DDE in short, to thermal power plants.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2023.1158164</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2023.1158164</link>
        <title><![CDATA[Self-paced heart rate control for treadmill exercise]]></title>
        <pubdate>2023-03-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hanjie Wang</author><author>Kenneth J. Hunt</author>
        <description><![CDATA[Introduction: With conventional heart rate (HR) control systems, the exercising person is bound to walk or run at a pace determined by the feedback. This may be challenging for people with impairments that make it difficult for them to achieve a smooth, continuous pace. The aim of this work was to assess the technical feasibility of a novel self-paced heart rate control strategy and to compare its accuracy with conventional heart rate control.Methods: We propose a self-paced heart rate control system that embeds an automatic positioning controller within the heart rate control loop. The treadmill speed command is decoupled from the heart rate compensator, whereas speed is determined by the exerciser’s own volition: target speed is displayed visually to the person and, when they try to follow this target, the position controller sets the treadmill speed while keeping the person at a safe reference position on the track. A further novel contribution of this work is a new input-sensitivity-shaping, frequency-domain design strategy for feedback control of position.Results: Experimental evaluation with four participants showed that self-paced heart rate control is technically feasible: all participants were able to accurately follow the target running speed calculated by the HR compensator and presented to them visually; for all four participants, self-paced HR tracking accuracy was not substantially different from conventional HR control performance; on average, the self-paced heart rate controller gave slightly better performance than conventional HR control, with RMS tracking error of 2.98 beats per minute (bpm) vs 3.11 bpm and higher average control signal power.Conclusion: The proposed self-paced heart rate control strategy with embedded automatic position control is deemed feasible. This approach may be helpful for people with gait impairments or other limitations that make it difficult for them to follow an imposed treadmill speed.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2022.1083419</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2022.1083419</link>
        <title><![CDATA[Tuning of PIDD2 controllers for oscillatory systems with time delays]]></title>
        <pubdate>2023-01-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Hu Xingqi</author><author>Hou Guolian</author><author>Tan Wen</author>
        <description><![CDATA[Proportional–integral–derivative (PID) control is a durable control technology that has been widely applied in the process control industry. However, PID controllers cannot achieve satisfactory performance for oscillatory systems with long time delays; thus, high-order controllers like the proportional–integral–double derivative (PIDD2) can be adopted to enhance the control performance. In this paper, we propose a tuning formula for the PIDD2 controller for oscillatory systems with time delays and its practical implementation via an observer bandwidth-based state-space PIDD2. Simulation results show that the state-space PIDD2 controller tuned from the proposed formula trades-off among robustness, time domain performance, and measurement noise attenuation and can arrive at a better control effect than PID for oscillatory systems.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2022.1061830</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2022.1061830</link>
        <title><![CDATA[PID control: Resilience with respect to controller implementation]]></title>
        <pubdate>2022-11-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>V. M. Alfaro</author><author>R. Vilanova</author>
        <description><![CDATA[One of the major drawbacks of the basic parallel formulations of a PID controller is the effects of proportional and derivative kick. In order to minimize these effects, modified forms of parallel controller structures such as PI-D and I-PD are usually considered in practice. In addition, there is a usual servo/regulation tradeoff regarding closed-loop control system operation. Appropriate tuning is needed for each situation. One way of focusing explicitly on load disturbance is by the appropriate selection of a controller equation. A gap is generated here between the conception of a tuning rule and its final application that may need deployment on different controller equations. There is no danger when we go from PI-D to I-PD as we just change reference processing. However, there will be a loss of performance. The potential loss of performance, depending on the final controller equations used, motivates the authors to introduce the idea of resilient PID tuning: minimize the effects of changing the controller equation on the achieved performance/robustness. Today, this can be seen as a complement to the well-known controller fragility concept. On the basis of this scenario, this paper motivates the analysis of a tuning rule from such a point of view and also emphasizes the benefits that a better process model may provide from such an aspect.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2022.954164</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2022.954164</link>
        <title><![CDATA[Predictor-based phase-lead active disturbance rejection control design for industrial processes with input delay]]></title>
        <pubdate>2022-09-27T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaomeng Li</author><author>Shoulin Hao</author><author>Tao Liu</author><author>Bin Yan</author><author>Yongzhi Zhou</author>
        <description><![CDATA[For industrial processes subject to input delay, a predictor-based phase-lead active disturbance rejection control (ADRC) scheme is proposed in this article for improving disturbance rejection performance by introducing a phase-lead module for feedback control. First, an extended state observer (ESO) in combination with a generalized delay-free output predictor is presented to estimate the delay-free system state together with load disturbance lumped with process uncertainties. To reduce the phase lag caused by not only ESO but also the delay-free output predictor, a phase-lead module is then added into the disturbance observation channel so as to expedite disturbance estimation and thus improve the disturbance rejection performance. Consequently, the ESO gain vector and feedback controller are analytically designed by specifying the desired poles for the observer and the closed-loop system, respectively. Moreover, a digital implementation of the proposed scheme is presented to facilitate the practical applications, followed by a robust stability analysis of the closed-loop system based on the small gain theorem. Illustrative examples from the literature are used to demonstrate the effectiveness and merits of the proposed method over the existing methods.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2022.922308</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2022.922308</link>
        <title><![CDATA[Comparative analysis of different FOPI approximations and number of terms used on simulations of a battery-powered, field-oriented induction motor based electric vehicle traction system]]></title>
        <pubdate>2022-09-16T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mazen Elsaadany</author><author>Muhammad Qasim Elahi</author><author>Faris AtaAllah</author><author>Habibur Rehman</author><author>Shayok Mukhopadhyay</author>
        <description><![CDATA[Because of their enhanced performance, the fractional order proportional-integral (FOPI) controllers are becoming an appealing choice for controlling induction motor speed. To implement FOPI controllers, several fractional order integral approximations are available in the literature. The approximation used, and the order of approximation affects the speed tracking, transient response, and induction motor power consumption. This further affects the energy consumption analysis if simulations are conducted based on such approximations. In this paper an electric vehicle (EV) traction system is simulated to investigate the effect of such approximations on the simulations of a battery powered, induction motor driven EV system. The system consists of an indirect field-oriented induction motor, a lithium-ion battery bank, and a three-phase inverter. This work presents a quantitative analysis of the performance of FOPI controllers using different approximations, and order of approximations is presented. The controllers are evaluated based on speed tracking, transient response, computational time, and power consumption. Both step functions and standard drive cycles are used as the speed reference signal to evaluate the effects of using different approximations and different orders of approximation, when different references are used. This work establishes a reference set of simulations that can be used to infer the amount of error in battery state of charge, and state of health analysis conducted on such an EV system, when dealing with FOPI controllers under different approximations and related settings.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2022.953768</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2022.953768</link>
        <title><![CDATA[Control of dead-time process: From the Smith predictor to general multi-input multi-output dead-time compensators]]></title>
        <pubdate>2022-09-06T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Julio E. Normey-Rico</author><author>Tito L. M. Santos</author><author>Rodolfo C. C. Flesch</author><author>Bismark C. Torrico</author>
        <description><![CDATA[This review paper deals with the analysis, design, and tuning of dead-time compensators for stable and unstable multi-input multi-output (MIMO) processes with multiple time delays. It is well known that, even in the single-input single-output case, processes with significant dead times are difficult to control using standard feedback controllers. For MIMO systems, the study of processes with dead time is more involved, particularly when the process behavior exhibits different dead times in the different input-output relationships. Because of this, much research has been conducted in the last 50 years on this subject, with different approaches and proposals of controllers for covering a variety of objectives. Thus, this paper gives an overview of this important topic, focusing on the solutions derived from the Smith Predictor. First, a historical perspective of the different controllers proposed in the literature is presented. Then, the general solution of the problem is developed, paying particular attention to robustness and disturbance rejection properties, because of their importance and usefulness in industrial processes. All the development is done in the discrete-time case, which allows direct digital implementation. Two simulation case studies are presented to illustrate some of the ideas discussed in the paper, and an experimental case study is used to discuss aspects of practical implementation.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2022.982463</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2022.982463</link>
        <title><![CDATA[How much is enough in watering plants? State-of-the-art in irrigation control: Advances, challenges, and opportunities with respect to precision irrigation]]></title>
        <pubdate>2022-09-02T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Lina Owino</author><author>Dirk Söffker</author>
        <description><![CDATA[With a rapidly expanding global population placing an ever growing demand on freshwater resources, an increased focus on irrigation techniques tailored to the specific needs of plant appears as one solution to minimize overall freshwater consumption. Precision irrigation methods seek to realize an acceptable compromise between yield and irrigation water consumption through control of the timing and quantity of water supplied to plants. The goal is to maintain the water content of the soil, achieve specific water use efficiency with regard to yield or maintain the physiological response of the plant to water stress within predetermined limits. Reliance on soil moisture measurements to establish irrigation water demand inadequately addresses heterogenous distribution of water in soil. Growing research interest is observed detailing the determination of plant water status directly from physiological responses. This paper reviews irrigation control approaches based on different plant water status assessment techniques. A distinct focus is made on application scale of the discussed control approaches, an aspect that has not been considered intensively enough in previous discussions of irrigation control approaches. A discussion of the observed strengths and shortcomings and technological advances supporting the various methods used to quantify plant water status extends the review. Emerging trends that are likely to have an impact on plant water status determination and optimal timing and quantification of irrigation water requirements are integrated to show latest results. A peek into the future of precision irrigation foresees greater reliance on plant-based signals, both in characterization of the control variable, namely the plant water status, and in generation of controller outputs in terms of quantity and timing.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2022.804549</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2022.804549</link>
        <title><![CDATA[Conversion From Unstructured LTI Controllers to Observer-Structured Ones for LPV Systems]]></title>
        <pubdate>2022-08-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Masayuki Sato</author><author>Noboru Sebe</author>
        <description><![CDATA[This paper considers the conversion problem from unstructured linear time-invariant (LTI) controllers to observer-structured LTI controllers, whose structure is similar to but not exactly the same as the so-called “Luenberger observer–based controllers,” for linear parameter-varying (LPV) plant systems. In contrast to Luenberger observer–based controllers, observer-structured LTI controllers can be defined and constructed even if the plant systems are given as LPV systems. In the conversion problem, the state-space matrices of the observer-structured LTI controller are parameterized with those of the given unstructured LTI controller, one free matrix, and a state transformation matrix. We also show a method to obtain the optimal state transformation matrix with respect to the convergence of the discrepancy between the plant state and the observer-structured controller state for a stochastically defined non-zero initial plant state. Several toy examples are included to illustrate the effectiveness and the usefulness of observer-structured LTI controllers, and the utility of the proposed conversion parametrization.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2022.953477</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2022.953477</link>
        <title><![CDATA[Design and implementation of high-order PID for second-order processes with time delay]]></title>
        <pubdate>2022-08-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Wenjie Han</author><author>Xingqi Hu</author><author>Ulemj Damiran</author><author>Wen Tan</author>
        <description><![CDATA[In this study, a state-space pole placement approach is first proposed to design high-order PID controllers for high-order processes. The method makes use of a single parameter to determine the locations of closed-loop poles; thus, a high-order PID controller can be tuned with this parameter. To implement the high-order PID controller in practice, an observer-based PID structure is proposed. The structure utilizes a model-free observer to estimate the plant output and its derivatives, thus retaining the high-order PID structure but can filter the measurement noise and make the high-order derivatives of the plant output available for control. The proposed method is applied to design high-order PID controllers for second-order processes with time delay. Simulation results show that high-order PID can indeed improve the performance of conventional PID controllers for second-order processes with time delay in disturbance rejection and robustness.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2022.954858</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2022.954858</link>
        <title><![CDATA[Automatic generation and updating of process industrial digital twins for estimation and control - A review]]></title>
        <pubdate>2022-08-25T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Wolfgang Birk</author><author>Roland Hostettler</author><author>Maryam Razi</author><author>Khalid Atta</author><author>Rasmus Tammia</author>
        <description><![CDATA[This review aims at assessing the opportunities and challenges of creating and using digital twins for process industrial systems over their life-cycle in the context of estimation and control. The scope is, therefore, to provide a survey on mechanisms to generate models for process industrial systems using machine learning (purely data-driven) and automated equation-based modeling. In particular, we consider learning, validation, and updating of large-scale (i.e., plant-wide or plant-stage but not component-wide) equation-based process models. These aspects are discussed in relation to typical application cases for the digital twins creating value for users both on the operational and planning level for process industrial systems. These application cases are also connected to the needed technologies and the maturity of those as given by the state of the art. Combining all aspects, a way forward to enable the automatic generation and updating of digital twins is proposed, outlining the required research and development activities. The paper is the outcome of the research project AutoTwin-PRE funded by Strategic Innovation Program PiiA within the Swedish Innovation Agency VINNOVA and the academic version of an industry report prior published by PiiA.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fcteg.2022.970136</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fcteg.2022.970136</link>
        <title><![CDATA[Editorial: Linear Parameter Varying Systems Modeling, Identification and Control]]></title>
        <pubdate>2022-08-18T00:00:00Z</pubdate>
        <category>Editorial</category>
        <author>Paulo Lopes Dos Santos</author><author>Teresa Azevedo Perdicoulis</author><author>José A. Ramos</author><author>Fernando A. C. C. Fontes</author><author>Olivier Sename</author>
        <description></description>
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