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The control performance of a passive dynamic vibration absorber (DVA) is known to be sensitive to its frequency deviation. This study upgrades the conventional device and proposes an adaptive-passive variable frequency gas-spring DVA (APVF-GSDVA) for controlling the structural seismic response, while an adaptive control algorithm is developed that requires only a single input signal. The DVA’s vibration frequency is precisely modulated by adjusting the gas pressure in the gas-spring, aligning it with the inherent frequency of the multi-degree-of-freedom (MDOF) structure. The working principle and control algorithm of the APVF system is introduced and derived in detail, the corresponding gas-spring DVA and implementation are designed, and the configuration of the entire control system is realized. An adaptive variable frequency test scheme is designed and the frequency retuning function of the prototype APVF-GSDVA device installed on an MDOF structure is experimentally validated. The seismic response control effectiveness of the retuned DVA is demonstrated by comparing the detuned DVA, highlighting the necessity of implementing adaptive frequency adjustment of the gas-spring DVA. Experimental findings demonstrate that APVF-GSDVA accurately identifies the inherent frequency of the MDOF structure in ambient excitation, it then adjusts the pressure of the gas-spring according to the frequency-pressure relationship, optimally retuning the DVA. A retuned GSDVA can effectively suppress the structural seismic responses, compared to a detuned DVA, its control effectiveness on peak and RMS responses can be improved by a maximum of nearly 13.5% and 53.3% at a frequency deviation of 10%. Furthermore, results from two “detuning-retuning” test paths with distinct seismic excitations indicate that the retuned DVA exhibits superior control effectiveness, underscoring the significance of the adaptive frequency adjustment function in passive DVAs. [ABSTRACT FROM AUTHOR]

In recent years, microgrid technology has been widely studied and applied. However, with times developing, the installed capacity of distributed power generation devices has been improved, and work is being carried out in increasingly complex situations, resulting in a decline in the control performance of microgrids. In view of this, to effectively improve inverter’s control performance, research is conducted on the fusion of Narendra model and adaptive control strategies for real-time voltage correction and compensation in complex situations. Compared to traditional inverters, inverters under research methods have faster voltage recovery speed when encountering load switching, and can recover in about one cycle, with good control performance. In the comparison between the improved inverter adaptive control system and the inverter adaptive system, the improved inverter voltage recovery speed is faster, can be restored within one cycle, and the control effect of the inverter is better. The harmonic rate of the port voltage has decreased from 10.43 to 1.92%. The applicability of the research method was verified. It indicats that the research method can improve inverter’s control effect and solve problems such as voltage deviation, three-phase asymmetry, harmonic pollution, etc. that are easily generated by the output terminal voltage. Simultaneously, research has provided theoretical basis and data support for the research of microgrids. [ABSTRACT FROM AUTHOR]

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Abstract In recent years, microgrid technology has been widely studied and applied. However, with times developing, the installed capacity of distributed power generation devices has been improved, and work is being carried out in increasingly complex situations, resulting in a decline in the control performance of microgrids. In view of this, to effectively improve inverter’s control performance, research is conducted on the fusion of Narendra model and adaptive control strategies for real-time voltage correction and compensation in complex situations. Compared to traditional inverters, inverters under research methods have faster voltage recovery speed when encountering load switching, and can recover in about one cycle, with good control performance. In the comparison between the improved inverter adaptive control system and the inverter adaptive system, the improved inverter voltage recovery speed is faster, can be restored within one cycle, and the control effect of the inverter is better. The harmonic rate of the port voltage has decreased from 10.43 to 1.92%. The applicability of the research method was verified. It indicats that the research method can improve inverter’s control effect and solve problems such as voltage deviation, three-phase asymmetry, harmonic pollution, etc. that are easily generated by the output terminal voltage. Simultaneously, research has provided theoretical basis and data support for the research of microgrids.

The US Environmental Protection Agency (USEPA) considers sediment toxicity tests as conditional registration requirements for pesticides with soil Kd ≥50 L/kg‐solid, Koc ≥1000 L/kg‐organic carbon, or log Kow ≥3. The hydrophobicity of these compounds often necessitates use of solvents to ensure accurate and homogeneous dosing of spiked‐sediment studies. For sediment tests, a volatile solvent (e.g., acetone) is generally used as a transient carrier. Due to low water solubility, test material is dissolved in a volatile solvent to create stock solutions. A measured aliquot of stock solution is then mixed with sand substrate, after which the solvent is evaporated. This spiking process results in negligible solvent exposure to organisms. In 2016, USEPA released final ecotoxicity test guidelines for subchronic freshwater (850.1735) and marine (850.1740) sediment test. These methods provide an option for conducting experiments with only a solvent control and no negative control. To adopt this testing strategy, functional equivalency between the negative and solvent control must be demonstrated. These test guidelines describe specific factors that should be considered for evaluating functional equivalency, including (a) the concentration of solvent in the test sediment after evaporation, (b) the levels of solvent that are known to affect organism health, (c) the known impurities in the solvent and their potential impact on organism health, and (d) the historical organism performance of solvent versus negative controls. Our analysis considers these factors and overall supports the elimination of the negative control requirement because this change is unlikely to impact the robustness or interpretability of spiked‐sediment toxicity tests. Environ Toxicol Chem 2024;43:1740–1746. © 2023 CropLife America. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. [ABSTRACT FROM AUTHOR]

A periodic adaptive mechanism is proposed for a class of discrete‐time linear periodic systems with parametric uncertainties in this paper. Based on the periodic system model, the adaptive mechanism uses the weight function to update the controller parameters in real time, which reduces the influence of system parameter uncertainty on the controller performance and makes the system parameters converge to the ideal values. Finally, numerical examples are used to demonstrate the effectiveness and superiority of the adaptive mechanism in improving control performance and reference tracking performance. [ABSTRACT FROM AUTHOR]

Two different methods of Fuzzy-control and Machine-learning are proposed to control the vehicle's semiactive suspensions. To control performance of semiactive suspensions using machine learning (SS-ML) and semiactive suspensions using fuzzy control (SS-FC), a half-vehicle model has been established to calculate and simulate vibration equations under random road surfaces from ISO A-class to F-class. Via map of control rule data in SS-FC established at roads ISO A-class, B-class, ..., and F-class, SS-ML's Neuro-Adaptive-Learning has been trained for learning these control rules. The results obtained indicate that under the same road surface excitation of ISO C-class, the control performance of SS-FC and SS-ML is equivalent., and the vehicle's comfort level using both SS-FC and SS-ML is very well improved in comparison with passive suspensions without control (PS-WC) of the vehicle. Under a mixed road surface from ISO A-class to F-class and a change range of the vehicle's moving velocity from 2.5 m s−1 to 35m s−1 used for simulation, the vehicle's comfort level using SS-ML is better than vehicle's comfort level using SS-FC. Especially, the root mean square values of displacements and accelerations in vertical and pitch directions of the vehicle body with SS-ML are smaller than that of SS-FC by 13.4%, 23.2%, 20.7%, and 14.3%, respectively. Therefore, the control performance of SS-ML is better than SS-FC, and it should be used to control the vehicle's semiactive suspensions for enhancing the comfort level. [ABSTRACT FROM AUTHOR]

. The goal of the work is to create a new method for automatic re-tuning of PI controllers. It is achieved by solving the following problems. The first problem is to develop a method to identify a FOPDT estimation model by analyzing the dynamics of a control loop with a PI controller. The second problem is to demonstrate that the use of the estimated model allows to obtain better processes in control systems with PI controllers by comparing the resulting FOPDT model with a number of reduced models. The third problem is to develop a five-stage algorithm for automatic re-tuning of PI controllers. The fourth problem is to verify the software implementation of the developed algorithm. The most significant result was that the basis for estimating the FOPDT model in a loop with a Ziegler-Nichols tuned PI controller was the estimation the ratio of the delay to the time constant of the FOPDT model based on an assessment of the process overshoot. When the real control plant dynamics did not match with the dynamics of the FOPDT model this way was found effective for finding the optimal tuning of the PI controller. The significance of the results obtained was a structural robustness in case of estimated FOPDT model usage. The effectiveness of the procedure has been demonstrated on both linear and nonlinear models. The developed procedure is proposed to be used to automate the procedure of re-tuning SISO control systems when the control loop performance degradation has been detected.

The drum water level plays a crucial role in the safety and economy of heat recovery boilers. However, the control of the drum water level faces many challenges, such as external disturbances and system uncertainties. To enhance the control performance of the drum water level, a modified active disturbance rejection control (MADRC) optimized with sensitivity constraint is proposed in this paper. Firstly, the control structure of the three-element control system for the drum water level is introduced and analyzed. Based on the regular active disturbance rejection control (ADRC) structure, the structure of the MADRC is introduced and the convergence of the proposed MADRC is proven. Then a modified whale optimization algorithm (MWOA) with sensitivity constraint is applied to optimize the parameters of the MADRC. With different sensitivity constraints, the parameters of the MADRC and comparative controllers are obtained, and their control performance for tracking and disturbance rejection abilities is compared. Moreover, the ability to handle system uncertainties is analyzed. Simulation results and performance indexes show that the proposed MADRC can obtain the best tracking and disturbance rejection abilities with satisfactory robustness. The satisfactory control performance shows that the proposed MADRC has wide application potential for heat recovery boilers and other industrial processes. [ABSTRACT FROM AUTHOR]

Worldwide, food scarcity is becoming a debatable concern among the scientific fraternity due to the increased populace, leading to decreased arable land. This has compelled us to explore various innovative and technological solutions, for example, large‐scale greenhouse farming, to meet the surging demand for field production. In this context, research efforts have been continually made by various scientists and researchers to explore more control strategies/algorithms for keeping the indoor climate comfortable and enhancing the greenhouse's energy effectiveness. Considering this, an initiative was made to summarize the documented research findings in the last decade focusing on energy‐efficient greenhouse‐based crop cultivation. The findings of some studies considering selective parametric conditions have been presented in graphs/tables for reader clarity and discussion. Initially, the studies on existing energy efficient strategies, parameters, monitoring systems, sensing networks, and control algorithms have been discussed. A state of the art review found that control strategies are essential in low‐energy greenhouses since they influence crop yield and cost. It was observed that advanced control algorithms and energy conservation in greenhouses received more attention due to wide spread application, high compatibility, low‐cost, and user‐friendly operations. In terms of future perspectives, it is anticipated that the development of machine learning, big data, and artificial intelligence, combining these technologies with traditional and advanced control strategies would lead to a revolution in the management of greenhouse energy. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel method for enhancing the control performance of a degraded robot manipulator by leveraging digital twins and proximal policy optimization, a specific deep reinforcement learning algorithm. Recently, various robotic technologies with high levels of controllability, safety, and reliability that incorporate the fourth industrial technology have been developed. Nevertheless, repairs or replacements owing to the performance degradation of sophisticated robot hardware or control systems are still time-, cost-, and manpower-consuming. To address these challenges, we propose a new strategy:1) approximate the unstable dynamic characteristics of six-degree-of-freedom low-performance robot manipulators to a digital twin with parameter tuning of a physics engine; 2) improve the accuracy and stability of reaching target points under diverse conditions through deep reinforcement learning using the domain randomization method; and 3) deploy a trained policy on an actual robot manipulator with degraded capabilities to validate the control performance improvement. Our method reduced the position error of a real robot manipulator by 63.0% and 39.0% compared to the built-in control method and the proportional-integral-derivative control method, respectively. Randomizing parameters in the physics engine of the digital twin during training allowed the method to simulate the imprecise motions of an actual degraded robot manipulator, facilitating the development of a more robust policy. Notably, our method has the potential to be applicable to all types of articulated robots, and presents a promising solution for maintaining performance while reducing long-term costs.

The drum water level plays a crucial role in the safety and economy of heat recovery boilers. However, the control of the drum water level faces many challenges, such as external disturbances and system uncertainties. To enhance the control performance of the drum water level, a modified active disturbance rejection control (MADRC) optimized with sensitivity constraint is proposed in this paper. Firstly, the control structure of the three-element control system for the drum water level is introduced and analyzed. Based on the regular active disturbance rejection control (ADRC) structure, the structure of the MADRC is introduced and the convergence of the proposed MADRC is proven. Then a modified whale optimization algorithm (MWOA) with sensitivity constraint is applied to optimize the parameters of the MADRC. With different sensitivity constraints, the parameters of the MADRC and comparative controllers are obtained, and their control performance for tracking and disturbance rejection abilities is compared. Moreover, the ability to handle system uncertainties is analyzed. Simulation results and performance indexes show that the proposed MADRC can obtain the best tracking and disturbance rejection abilities with satisfactory robustness. The satisfactory control performance shows that the proposed MADRC has wide application potential for heat recovery boilers and other industrial processes.

The tunable approximated explicit model predictive control (MPC) comes with the benefits of real-time tunability without the necessity of solving the optimization problem online. This paper provides a novel self-tunable control policy that does not require any interventions of the control engineer during operation in order to retune the controller subject to the changed working conditions. Based on the current operating conditions, the autonomous tuning parameter scales the control input using linear interpolation between the boundary optimal control actions. The adjustment of the tuning parameter depends on the current reference value, which makes this strategy suitable for reference tracking problems. Furthermore, a novel technique for scaling the tuning parameter is proposed. This extension provides to exploit different ranges of the tuning parameter assigned to specified operating conditions. The self-tunable explicit MPC was implemented on a laboratory heat exchanger with nonlinear and asymmetric behavior. The asymmetric behavior of the plant was compensated by tuning the controller's aggressiveness, as the negative or positive sign of reference change was considered in the tuning procedure. The designed self-tunable controller improved control performance by decreasing sum-of-squared control error, maximal overshoots/undershoots, and settling time compared to the conventional control strategy based on a single (non-tunable) controller. • Self-tunable control technique does not need any intervention during control. • Tuning based on operating conditions compensates for the nonlinear plant behavior. • Implementation of the proposed tuning method on a laboratory heat exchanger. • Control performance improved compared to utilizing explicit MPC with constant setup. [ABSTRACT FROM AUTHOR]