Article
Automation & Control Systems
Bismark C. Torrico, Rene D. O. Pereira, Andresa K. R. Sombra, Fabricio G. Nogueira
Summary: This paper proposes a control structure suitable for high-order non-minimum phase processes, with the main advantage being that the primary controller is only a state feedback gain with no integrators. Simulation results show better or equivalent performance compared to other recently published works, while maintaining controller design simplicity.
Article
Automation & Control Systems
P. Princes Sindhuja, V. Vijayan, Rames C. C. Panda
Summary: This paper introduces a compensating tool called Smith-Predictor (SP) for chemical processes, which can improve the performance of processes with integrating-plus-dead-time (IPDT), pure integrating-type, and higher-order-integrating characteristics. The study focuses on controlling the dead-time using an integrating first-order-plus-dead-time (IFOPDT) or first-order-plus-dead-time (FOPDT) process, along with a modified sliding-mode controller (SMC) in the proposed modified-Smith-predictor (MSP) structure. Analytical derivation of a modified novel SMC discontinuous tuning-parameter is performed to achieve desired performance criteria. The MSP-SMC structure is implemented in various processes, including integrating processes and a nonlinear system, as well as a real-time lab setup of a neutralisation process. Robustness and invariance properties of the SMC are analyzed against parametric uncertainty, and the proposed design is tested in nonlinear and Multi-Input-Multi-Output processes. Performance metrics, such as IAEs and peak overshoots, are compared with similar works using MATLAB.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Automation & Control Systems
Peter Seiler, Raghu Venkataraman
Summary: This article investigates the robustness of an uncertain nonlinear system by approximating the system with a linear time-varying system and describing the perturbation with integral quadratic constraints. The analysis provides a computational method for bounding the worst-case performance without using heuristics like time gridding.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Daniel L. Amaral, Bismark C. Torrico, Fabricio G. Nogueira, Rene D. O. Pereira, Tito L. M. Santos
Summary: This paper proposes a new unified tuning methodology for the simplified filtered Smith predictor, which can be applied to single input single-output high-order processes with delay and square multi-input multi-output high-order processes with multiple delays. The strategy preserves the properties of the filtered Smith predictor and simplifies the controller tuning process.
CONTROL ENGINEERING PRACTICE
(2023)
Article
Automation & Control Systems
Zhiwang Feng, Rafael Pena-Alzola, Mazheruddin H. Syed, Patrick J. Norman, Graeme M. Burt
Summary: The stability and accuracy of power hardware-in-the-loop (PHIL) setups are affected and degraded by the dynamics and nonideal characteristics of power interfaces. A compensation scheme using a Smith predictor compensator is proposed to address the impact of time delay on PHIL stability. An online system impedance identification technique is also utilized to enhance the robustness of the compensator and adapt to impedance variation. Analytical assessment, simulation results, and experimental validation demonstrate the effectiveness of the proposed compensation scheme in enabling robust and stable testing of novel power technologies.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2023)
Article
Automation & Control Systems
G. Lloyds Raja, Ahmad Ali
Summary: This work proposes an enhanced control approach combining cascaded control and Smith Predictor, utilizing outer-loop decomposition. Controller parameters are obtained through moment matching to achieve expected maximum sensitivity. The method exhibits remarkable improvement in closed-loop response compared to other strategies.
Article
Engineering, Multidisciplinary
Jorge Espin, Fabio Castrillon, Hugo Leiva, Oscar Camacho
Summary: This paper proposes a modified hybrid robust Smith predictor controller design for integrating processes with long dead time, combining sliding mode control and Smith Predictor concepts. The resulting controller shows enhanced performance and robustness.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Automation & Control Systems
Florentina Nicolau, Witold Respondek, Shunjie LI
Summary: This paper studies feedback linearization of multi-input control-affine systems using a particular class of nonregular feedback transformations, and provides geometric necessary and sufficient conditions to describe these systems.
SIAM JOURNAL ON CONTROL AND OPTIMIZATION
(2022)
Article
Engineering, Marine
Juan Li, Zhenyang Tian, Honghan Zhang, Wenbo Li
Summary: This paper addresses the problem of finite-time formation control for a multi-AUV formation under unknown perturbations with prescribed performance. The nonlinear AUV model is transformed into a second-order integral model using feedback linearization. Prescribed performance functions are utilized to limit the control errors and an error-conversion function is introduced to convert AUV tracking errors. Finite-time sliding-mode disturbance observers are designed to accurately estimate unknown disturbances in the ocean. Based on these observations and unconstrained tracking errors, a fast terminal sliding-mode formation controller is proposed to achieve finite-time convergence of the multi-AUV formation. Simulation experimental results demonstrate the effectiveness of the proposed method in canceling unknown disturbances and improving the robustness of the formation control.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Weiyu Wang, Xin Yin, Lin Jiang, Yijia Cao, Yong Li
Summary: The VSC-MTDC system with offshore wind farms faces uncertainties and nonlinearities that could impact its performance and stability. This paper proposes a PONC approach to improve the robustness of the system by estimating perturbations and designing controllers to compensate for them, achieving robust tracking performance without requiring an accurate system model. The effectiveness of PONC is demonstrated through a 5-terminal VSC-MTDC system.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2022)
Article
Automation & Control Systems
Xueliang Liu, Wei Lin
Summary: In this article, a predictor-based linearization approach is proposed to solve the local asymptotic stabilization problem of time-invariant nonlinear systems with a large constant input delay. It is proven that LAS can be achieved using predictor-based state or output feedback controllers designed based on the linearization, given the stabilizability and detectability conditions of the linearized time-delay system. This technique expands the class of nonlinear systems with input delay and improves upon previous work by removing certain technical requirements.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Balazs A. Kovacs, Tamas Insperger
Summary: This study analyzed the critical length for stabilizability in delayed PDA feedback and predictor feedback for the inverted pendulum paradigm. It was found that the relation between the critical length and reaction delay remained quadratic in the presence of perturbations on control gains. Predictor feedback outperformed PDA feedback in terms of critical length and was more sensitive to changes in feedback delay compared to parameter uncertainties.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Engineering, Multidisciplinary
Carlos Mejia, Estefania Salazar, Oscar Camacho
Summary: This paper emphasizes a comparative experimental evaluation of three Smith predictor configurations. The Mejia et al. approach presented a better overall performance than the other two. Finally, the results showed that Smith Predictor is suitable for thermal processes with elevated dead time.
ALEXANDRIA ENGINEERING JOURNAL
(2022)
Article
Chemistry, Analytical
Algirdas Baskys
Summary: This article proposes a modification of the Smith predictor for systems with response-delay asymmetry. It is applied to frequency converters controlling the speed of AC induction motor drives in water and liquefied petroleum gas supply systems. The modification switches the value of the response delay in the plant model used for the Smith predictor. The proposed switched-delay Smith predictor, combined with a proportional-integral controller, is analyzed through simulation and experimental tests in a water supply system, demonstrating its advantages.
Article
Mathematics
Mikulas Huba, Pavol Bistak, Damir Vrancic
Summary: The article provides a brief overview of two-degree-of-freedom (2-DoF) internal model control (IMC) and 2-DoF Smith-Predictor-based (SP) control for unstable systems. It discusses the limitations of control actions as a key reason for distinguishing between these approaches, and raises awareness about the potential structural instability hidden in seemingly lucrative dynamics of transients that may manifest over time. The article also introduces two-step IMC and filtered Smith predictor (FSP) design as reliable alternatives for unstable first-order time-delayed (UFOTD) systems, showing slightly slower but more robust transients and increased stability and robustness in the long run.
Article
Automation & Control Systems
Tito L. M. Santos, Julio E. Normey-Rico
Summary: This paper presents a Generalised Dynamic Matrix Control (GDMC) algorithm for controlling open-loop unstable processes. Unlike Dynamic Matrix Control (DMC), GDMC can provide internally stable predictions through a generalised filtered approach. The paper shows the conditions for achieving internal stability and proposes a new data-driven filter design procedure. Two simulation case studies are presented to demonstrate the usefulness of GDMC.
Article
Thermodynamics
Igor M. L. Pataro, Juan D. Gil, Jose L. Guzman, Manuel Berenguel, Joao M. Lemos
Summary: This work proposes a hierarchical framework for controlling a solar thermal facility to provide operating conditions for an absorption chiller machine. A case study of the CIESOL thermal plant is conducted with verified subsystems and valves in a simulation environment. Three different models are used for absorption chiller modeling, and a hybrid nonlinear predictive controller is formulated for hierarchical control. A lower layer with PI controllers is designed to handle valve nonlinear dynamics and disturbance rejection. Results show that the hierarchical structure extends the operating time of the solar-powered absorption chiller by approximately 115 minutes compared to conventional operation, with reduced fossil fuel usage.
Article
Green & Sustainable Science & Technology
Diogo Ortiz Machado, William D. Chicaiza, Juan M. Escano, Antonio J. Gallego, Gustavo A. de Andrade, Julio E. Normey-Rico, Carlos Bordons, Eduardo F. Camacho
Summary: The aim of this study is to create a digital twin of a commercial absorption chiller using Adaptive Neuro-fuzzy Inference System (ANFIS) for control and optimization purposes. The ANFIS models show good accuracy and precision, outperforming literature models in terms of Mean Absolute Percentage Error (MAPE). The resulting digital twin is suitable for Model Predictive Control applications and fast what-if analysis and optimization.
Article
Mathematics
Pablo Otalora, Jose Luis Guzman, Manuel Berenguel, Francisco Gabriel Acien
Summary: The industrial production of microalgae is a sustainable and interesting process, especially in terms of its applications in wastewater treatment. Neural network models have been developed to optimize the process and characterize the pH dynamics in different raceway reactors. These models are able to predict pH profiles using available measurable process data and demonstrate the potential of artificial neural networks in modeling continuous dynamic systems in the industry.
Article
Computer Science, Interdisciplinary Applications
Joao Bernardo Aranha Ribeiro, Jose Dolores Vergara Dietrich, Julio Elias Normey-Rico
Summary: We propose a simplified economic model predictive controller (EMPC) for supervisory control of an offshore petroleum production network. The controller mimics a nonlinear EMPC while solving quadratic programming and enhances feasibility using an estimator and slack variables. We provide tips for design improvement and compare our method with other controllers in various scenarios, showing its economic optimality and robustness.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Automation & Control Systems
Angeles Hoyo, Tore Hagglund, Jose Luis Guzman, Jose Carlos Moreno
Summary: This paper addresses the issue of control signal saturation caused by feedforward control from measurable load disturbances. An efficient feedforward compensator can generate significant peaks in the control signal during fast changes in the load disturbance, potentially causing the signal to reach saturation. The authors propose reducing the gain of the feedforward compensator during saturation periods to overcome this problem, presenting a method to calculate this gain reduction. Simulation examples demonstrate the significant performance improvement achieved by implementing this idea in cases of saturation problems. The proposed algorithm is also tested on a lab-scale temperature control system to demonstrate its practical capabilities.
CONTROL ENGINEERING PRACTICE
(2023)
Article
Automation & Control Systems
Igor M. L. Pataro, Juan D. Gil, Jose L. Guzman, Manuel Berenguel, Joao M. Lemos
Summary: This article presents a study on the pH control of raceway photobioreactors (PBRs) using a learning-based model predictive control (LBMPC) approach. The LBMPC demonstrates satisfactory results and outperforms the conventional nominal MPC strategy, achieving up to four times superior performance in terms of the average error index. The results highlight the importance of employing robust adaptive control strategies for highly nonlinear and multi-disturbed systems like the variant biological-chemical microalgae process in PBRs.
CONTROL ENGINEERING PRACTICE
(2023)
Article
Automation & Control Systems
Igor M. L. Pataro, Rita Cunha, Juan D. Gil, Jose L. Guzman, Manuel Berenguel, Joao M. Lemos
Summary: This study introduces an adaptive optimal model-free controller for solar collector fields (SCFs) that overcomes the challenges of using high-complex models. The proposed controller is based on the Reinforcement Q-Learning algorithm and achieves optimal performance using only plant measurements. It outperforms model-based controllers by handling nonlinearities, time-varying model parameters, and computational costs associated with nonlinear models. Simulations using actual data from a thermal plant demonstrate the effectiveness of the model-free controller, as the Q-Learning algorithm converges to the optimal gains of the Linear Quadratic Tracking (LQT) controller.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Automation & Control Systems
Igor M. L. Pataro, Juan D. Gil, Marcus V. Americano da Costa, Lidia Roca, Jose L. Guzman, Manuel Berenguel
Summary: Improving temperature reference tracking is crucial for enhancing the performance of solar thermal plants. This study proposes two control strategies, lead-lag and nonlinear reference feedforwards, to achieve low rise time and no overshoot in temperature reference tracking. Simulation experiments and real-world testing in a solar plant validate the effectiveness of these strategies under different operating conditions.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Automation & Control Systems
Igor M. L. Pataro, Juan D. Gil, Marcus V. Americano da Costa, Lidia Roca, Jose L. Guzman, Manuel Berenguel
Summary: This study proposes a stochastic model predictive control (MPC) based on a chance-constraint formulation for controlling a real solar thermal plant. The controller, named CC practical nonlinear MPC (CC-PNMPC), is implemented in the AQUASOL-II facility to validate and demonstrate the advantages of the proposed control approach. The results show that the stochastic strategy can account for disturbance uncertainties and improve the control system's performance.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Meeting Abstract
Zoology
Emma Timmins-Schiffman, Jennifer Telish, Chris Monson, Chelsea Field, Jose Guzman, Kristy Forsgren, Graham Young
INTEGRATIVE AND COMPARATIVE BIOLOGY
(2023)
Article
Automation & Control Systems
Francisco Garcia-Manas, Francisco Rodriguez, Manuel Berenguel, Jose Maria Maestre
Summary: This paper presents a stochastic model predictive control (SMPC) strategy to maximize the economic profit of a greenhouse crop production. The SMPC strategy considers the uncertainty of market price by using its historical evolution per year as multiple price scenarios in the cost function. The results show that MS-MPC can improve economic profits compared to the use of an average price scenario for the MPC calculations.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Review
Automation & Control Systems
Igor M. L. Pataro, Juan D. Gil, Jose L. Guzman, Manuel Berenguel, Inmaculada Canadas
Summary: This paper provides a comprehensive study of four predictive control strategies for solar furnaces and evaluates them using real data and simulation. The practical NMPC strategy proves to be the most promising, striking a balance between control performance and computational cost. It is successfully implemented in a solar furnace facility and demonstrates effective control and performance.
JOURNAL OF PROCESS CONTROL
(2023)
Article
Automation & Control Systems
Subhashis Nandy
Summary: This research focuses on the design and stability analysis of nonlinear controllers for an electrically driven marine cycloidal propeller, along with estimating various parameters using the Extended Kalman Filter. The controller is defined using an efficient physics-based model and is able to accurately process multiple control signals. The robustness of the controller is assessed using Monte Carlo simulation, and its performance is evaluated through validation investigations.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Lucas C. Borin, Guilherme Hollweg, Caio R. D. Osorio, Fernanda M. Carnielutti, Ricardo C. L. F. Oliveira, Vinicius F. Montagner
Summary: This work presents a new automated test-driven design procedure for robust and optimized current controllers applied to LCL-filtered grid-tied inverters. The design of control gains is guided by high-fidelity simulations and particle swarm optimization algorithm, considering various normal and abnormal operating conditions. The proposed design ensures superior performance compared with other current control designs.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Wei He, Xiang Wang, Mohammad Masoud Namazi, Wangping Zhou, Josep M. Guerrero
Summary: The main objective of this paper is to develop a reduced-order adaptive state observer for a large class of DC-DC converters with constant power load, in order to estimate their unavailable states and unknown parameter and achieve an output feedback control scheme. The observer is designed using a generalized parameter estimation based observer technique and dynamic regressor extension and mixing method. The comparison study shows that the observer has the advantage of verifying the observability of the systems for exponential convergence without any extra excitation condition.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Te Zhang, Bo Zhu, Lei Zhang, Qingrui Zhang, Tianjiang Hu
Summary: This paper introduces a control technique called time-varying uncertainty and disturbance estimator (TV-UDE) which extends the classic UDE approach to handle more complicated issues. By combining TV-UDE with a nominal dynamic output-feedback controller, robust control for uncertain second-order attitude control systems without velocity measurements is achieved. Numerical simulations and physical experiments on a 2-DOF AERO attitude helicopter platform demonstrate the effectiveness of the proposed design in reducing steady-state errors and avoiding issues caused by high-gain estimation.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Kanishke Gamagedara, Taeyoung Lee, Murray Snyder
Summary: This paper presents the developments of flight hardware and software for a multirotor unmanned aerial vehicle capable of autonomously taking off and landing on a moving vessel in ocean environments. The flight hardware consists of a general-purpose computing module connected to a low-cost inertial measurement unit, real-time kinematics GPS, motor speed controller, and a camera through a custom-made printed circuit board. The flight software is developed in C++ with multi-threading to execute control, estimation, and communication tasks simultaneously. The proposed flight system is verified through autonomous flight experiments on a research vessel in Chesapeake Bay, utilizing real-time kinematics GPS for relative positioning and vision-based autonomous flight for shipboard launch and landing.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Yun Zhu, Kangkang Zhang, Yucai Zhu, Pengfei Jiang, Jinming Zhou
Summary: In this study, a three-term Dynamic Matrix Control (DMC) algorithm using quadratic programming is developed and compared with the traditional two-term DMC algorithm. Simulation studies and real-life tests show that the three-term DMC algorithm outperforms the two-term DMC algorithm in control effectiveness.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Jayu Kim, Taehoon Lee, Cheol-Joong Kim, Kyongsu Yi
Summary: This paper presents a data-based model predictive control method for a semi-active suspension system. The method utilizes a continuous damping controller and a stiffness controller to improve ride comfort and reduce vehicle pitch motion. Gaussian process regression is also used to compensate for model parameter uncertainties. The algorithm has been verified through computer simulations and vehicle tests, demonstrating its effectiveness and robustness.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Kunpeng Zhang, Jikang Gao, Zongqi Xu, Hui Yang, Ming Jiang, Rui Liu
Summary: A improved dynamic programming model is proposed in this paper for joint operation optimization of virtual coupling of heavy-haul trains. By simultaneously optimizing the headway and energy savings, as well as performing locomotive engineering advisory analysis, significant improvements in train performance can be achieved.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Demian Garcia-Violini, Yerai Pena-Sanchez, Nicolas Faedo, Fernando Bianchi, John V. Ringwood
Summary: This study presents a model invalidation methodology for wave energy converters (WECs) that can effectively handle dynamic uncertainty and external noise. The results indicate that neglecting dynamic uncertainty can lead to overestimation of performance, highlighting the importance of accurate dynamic description for estimating control performance.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Shengyang Lu, Yue Jiang, Xiaojun Xu, Hanxiang Qian, Weijie Zhang
Summary: This paper proposes an adaptive heading tracking control strategy based on wheelbase changes for unmanned ground vehicles (UGVs) with variable configuration. The strategy adjusts the wheelbase according to different working conditions to optimize driving performance. The impact of changing wheelbase on sideslip angle and heading angle is analyzed, and a robust-active disturbance rejection control method is developed to achieve desired front-wheel steering angle. A torque distribution method based on tire load rate and real-time load is applied to enhance longitudinal stability.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Domenico Dona, Basilio Lenzo, Paolo Boscariol, Giulio Rosati
Summary: This paper proposes a new method for designing minimum energy trajectories for servo-actuated systems and demonstrates its accuracy and effectiveness through numerical comparisons and experimental validation.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Haolin Wang, Luyao Zhang, Yao Mao, Qiliang Bao
Summary: This paper proposes a method of transforming the core element of ADRC, ESO, into a novel fuzzy self-tuning observer structure to improve the stability of LOS in the electro-optical tracking system. It effectively solves the conflict between disturbance rejection ability and noise attenuation ability in traditional ESO.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Alejandro Toro-Ossaba, Juan C. Tejada, Santiago Rua, Juan David Nunez, Alejandro Pena
Summary: This work presents the development of a myoelectric Model Reference Adaptive Controller (MRAC) with an Adaptive Kalman Filter for controlling a cable driven soft elbow exoskeleton. The proposed MRAC controller is effective in both passive and active control modes, showing good adaptability and control capabilities.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Mehrad Jaloli, Marzia Cescon
Summary: This study presents an advanced multi-agent reinforcement learning (RL) strategy for personalized glucose regulation, which is shown to improve glucose regulation and reduce the risk of severe hyperglycemia compared to traditional therapy.
CONTROL ENGINEERING PRACTICE
(2024)
Article
Automation & Control Systems
Yingming Tian, Kenan Du, Jianfeng Qu, Li Feng, Yi Chai
Summary: This paper investigates the control strategy for PMSM with position sensor fault in railway. A learning observer-based control strategy is proposed, which achieves high-precision estimation of electromotive force and accelerates speed response.
CONTROL ENGINEERING PRACTICE
(2024)
