Mass balance control of crushing circuits

This article is devoted to the analysis of the need to organize and implement measures of public control over the activities, acts and decisions of regional operators and owners of special accounts. The paper examines the role and place of the institution of public control in the system of legal guarantees for the implementation and protection of the constitutional principles of democracy and the participation of society in the management of state affairs. The necessity of including the activities, acts and decisions of regional operators and owners of special accounts that carry out work within the framework of the organization and functioning of capital repair funds in the Russian Federation into the objects of public control is substantiated. The article uses a number of methods of scientific research, in particular: analysis; synthesis; formal-logical; classification; comparative-legal; historical-legal; statistical; sociological, etc. The paper formalizes and explores the main problems that hinder the organization and implementation of public control measures in relation to the activities, acts and decisions of regional operators and owners of special accounts, due to the need for further development of legislation regulating both the institution of public control and the organization, as well as the activities of capital repair funds in Russia, regional operators and owners of special accounts. In this regard, the authors have developed and substantiated a system of measures to resolve these problems, including by making appropriate amendments and additions to the Constitution of the Russian Federation, the Housing Code of the Russian Federation, as well as legislation on public control in the Russian Federation.

The application of the Smith predictor in the control system with a constant speed actuator is considered. In order to make it possible to use the Smith predictor a PID-controller with autotuning is used, the autotuning provides the control object model using a self-oscillation mode. The control object has a second order transfer function with a transport delay. The performance of the system was analyzed on an experimental workbench with the industrial constant speed actuator and a physical model of the control object. Recommendations are given, how to correct the PID-law parameters in order to improve the system performance.

This paper proposes an optimal fuzzy-based coordination approach for thermal generator and solar photovoltaic (PV) plant in a two-area power system. The proposed intelligent coordinator is designed for valid adjustment of integral gain of secondary control loop in thermal generator and the proportional gain in PV plant. The classic controllers are generally applied with a constant gain for nominal operation state of the system. However, the proposed fuzzy method illustrates a favorable performance in load perturbations by fine tuning of classic conventional controllers’ gains. In this study, particle swarm optimization algorithm is used to optimize the fuzzy controller’s scaling factors. To demonstrate the effectiveness of the proposed optimal fuzzy controller, it is applied on a two-area power system with different generation unit in each area. Three scenarios of variations in load and solar radiation are implemented to illustrate the adequate performance of the controller in disturbances. A comparison with two optimization methods, known as firefly algorithm and genetic algorithm, is presented to approve the superiority of the controller.

In quantum optimal control theory, kinematic bounds are the minimum and maximum values of the control objective achievable for any physically realizable system dynamics. For a given initial state of the system, these bounds depend on the nature and state of the controller. We consider a general situation where the controlled quantum system is coupled to both an external classical field (referred to as a classical controller) and an auxiliary quantum system (referred to as a quantum controller). In this general situation, the kinematic bound is between the classical kinematic bound (CKB), corresponding to the case when only the classical controller is available, and the quantum kinematic bound (QKB), corresponding to the ultimate physical limit of the objective's value. Specifically, when the control objective is the expectation value of a quantum observable (a Hermitian operator on the system's Hilbert space), the QKBs are the minimum and maximum eigenvalues of this operator. We present, both qualitatively and quantitatively, the necessary and sufficient conditions for surpassing the CKB and reaching the QKB, through the use of a quantum controller. The general conditions are illustrated by examples in which the system and controller are initially in thermal states. The obtained results provide a basis for the design of quantum controllers capable of maximizing the control yield and reaching the ultimate physical limit.

An analytical framework for knowledge-based tuning of servo controllers

Some advanced control strategies for modern machine tools

Level systems are commonly found in the industrial field, presenting many times, a reasonable difficulty to be tuned by classical control techniques. Showing that controller tuning is still very interesting and important, the Bee Swarm Optimization (BSO) comes as a way for solving such difficulties. The BSO is a metaheuristic method that guides the search process, which is inspired by the great organizational skills of bees to collect food. Applying it to control theory, this method uses the resulting control system of reference by a PI controller. Thus, this paper deals with determining the values of the proportional (K p ) and integral (Ki) gain of the controller for a level system by the BSO, considering Integral of Time-weighted Absolute Error (ITAE) as an objective function. The experimental results in a SMAR didactic plant were obtained to compare a BSO tuning method with a classical control method widely used in industry (Rivera IMC). Thus the effectiveness of BSO has been validated, showing it's an interesting technique to be used in the industrial field for this kind of process.

Single-stage grid-connected photovoltaic (PV) systems face significant challenges in maintaining voltage stability and meeting accurate power quality (PQ) standards during rapidly fluctuating irradiance, load variations, and grid disturbances. This study presents a Digital Twin (DT) architecture that enables real-time voltage regulation and PQ optimization by digitally replicating the physical PV system for predictive analysis, controller tuning, and operational decision-making. The proposed framework integrates a highly accurate physical model, analytical computations, and adaptive control algorithms driven by data to minimize total harmonic distortion (THD), maintain voltage within allowable limits, and comply with IEEE 1547 and IEC 61727 standards. The DT synchronizes real-time data from voltage and current sensors to capture dynamic system behavior, allowing for pre-deployment testing of voltage regulation strategies, including classical, adaptive, and AI-assisted methods. The architecture incorporates enhanced filtering and stability assessment techniques based on classical control and predictive & nonlinear control techniques, ensuring robustness under weak grid conditions. Simulation and hardware-in-the-loop (HIL) evaluations demonstrate that the DT-assisted control framework reduces THD by more than 40%, maintains voltage within ± 1 %, and improves transient settling times compared to conventional control methods. Future work will explore the integration of DTs with distributed energy resource (DER) coordination, cyberphysical security mechanisms, and self-learning controllers to enable fully autonomous, grid-supportive PV operations in nextgeneration smart grids.

Managers understand the need to have information aligned with their businesses - they see and feel its consequences every day. They also understand the goal of technology-based information systems (IS) delivers the right information to the right people at the right time so that both strategic and operational decisions can be made properly and quickly. This was the approach followed by the American subsidiary of a Japanese motor company. The firm implemented a single, unified, Web-based interface to centralize and synchronize data access to 12 stand-alone dealership sales applications. The implementation took less than six months and less than $500,000 was expended. Immediate results were that the sales managers became significantly more efficient with their time, the automobile division recognized a cost savings of $1.4 million through a more efficient use of employee resources, and there was an estimated $10 million savings corporate wide as a result of employee efficiency gains throughout the organization. Despite all of these benefits, the automobile division’s chief information officer was faced with the decision of whether or not to return to fix the data integration and timeliness problems that remained among the division’s autonomous sales applications. This case study series highlights the objectives, outcomes, and challenges that managers must address while implementing Webbased portals. This case series also provides a better understanding for identifying, leveraging, and improving operational efficiency within a firm.

Enterprise network architecture and management have followed the Internet's design principles despite different requirements and characteristics: enterprise hosts are administered by a single authority, which intrinsically assigns different values to traffic from different business applications.We advocate a new approach where hosts are no longer relegated to the network's periphery, but actively participate in network-related decisions. To enable host participation, network information, such as dynamic network topology and per-link characteristics and costs, is exposed to the hosts, and network administrators specify conditions on the propagated network information that trigger actions to be performed while a condition holds. The combination of a condition and its actions embodies the concept of the network exception handler, defined analogous to a program exception handler. Conceptually, network exception handlers execute on hosts with actions parameterized by network and host state.Network exception handlers allow hosts to participate in network management, traffic engineering and other operational decisions by explicitly controlling host traffic under predefined conditions. This flexibility improves overall performance by allowing efficient use of network resources. We outline several sample network exception handlers, present an architecture to support them, and evaluate them using data collected from our own enterprise network.

A system that utilizes an ensemble of streamflow forecasts as input to the Truckee-Carson RiverWare © operational forecast model has been developed to provide operators and stakeholders with an ensemble of operational forecasts. This ensemble of operational forecasts allows users to estimate uncertainty and perform risk-based analyses to assist with operational decisions. The ensemble of streamflow forecasts are provided by the River Forecast Center's Ensemble Streamflow Prediction program which generates the forecasts based upon current hydrologic conditions and historic meteorology records. The streamflow forecasts are input hydrographs for the operational forecast model. The operational forecast model is run once for each trace of the ensemble streamflow forecast. During each run the model simulates daily operation of the river according to current basin policy. Select output is then collected from each run to be analyzed statistically, providing estimates of risk and uncertainty in the operational forecast. The RiverWare © Multiple Run Management tool is employed to facilitate the efficient use of the ensemble forecasts for daily operational decisions. This tool provides an automated process for performing multiple model runs with varying input and compiling select output within one file. An overview of the process and a sample application on Lake Tahoe precautionary release operations will be presented.

This paper proposes a control strategy applied to a three-phase induction motor (TIM) subject to parametric uncertainties, perturbations, and input time delay, whose primary objective is to achieve high-performance speed–torque control. The control design involves a predictive sliding mode observer (P-SMO) and a robust proportional–integral sliding mode control (PISM), aimed at reducing the detrimental effects of time delay and perturbations. The proposed control strategy’s effectiveness is investigated through computational simulations, carried out for different scenarios, whose distinctions focus on the consideration of delay in the feedback signals, the predictive character of the sliding mode observer, and the type of controller used. The presented results show the superior performance of the PISM controller compared with the classic PI controller for all tested scenarios. In the test scenario that considers the transport delay in the feedback signals, the sliding mode observer (SMO) without prediction does not stabilize the system, requiring the application of P-SMO to ensure stability and accurate tracking of the TIM speed reference.

Assessing the structure of classical molecular optimal control landscapes

The controlled atmospheric re-entry associated with the precision soft-landing of Reusable Launch Vehicles (RLVs) on Earth is very challenging as it depends on multiple parameters [1]. Over the last decade, the cost-effectiveness of such a technology has been finally demonstrated with the successful recoveries of SpaceX’s Falcon 9 first-stage rocket first [2], then followed by other companies such as the Rocket Lab’s Electron micro-launcher [3]. This breakthrough has been made possible by the development of advanced and robust computational methods able to generate in real time the flight conditions and to command the optimal vehicle's deflections accordingly to achieve a safe pinpoint landing. Indeed, during an Earth atmospheric re-entry, the vehicle is subjected to fast system dynamics changes partly induced by external loads associated with the terrestrial environment (e.g., lift, drag, wind and gusts), but also by the actuation commands to answer the landing constraints satisfaction and the vehicle integrity preservation. All those involve uncertainties and nonlinearities, which lead to vehicle’s instability and therefore give reason why for a highly performant Guidance, Navigation and Control system implementation [4]. More particularly, one of the critical aspects is the design of a robust control strategy capable of counteracting the previously defined disturbances and uncertainties while satisfying the strict accuracy requirements associated with the pinpoint landing [5]. As demonstrated by the current state-of-the-art on control design for launchers [6-7], the classical linear control theory represents a rich heritage with a lot of applications. This choice was motivated by its relative easiness of implementation and the possibility to use gain-scheduling techniques to adapt to nonlinear systems. Nevertheless, these techniques are well-adapted to the control system design of single-input single-output systems, such as for example a reusable rocket using a Thrust Vector Control (TVC) system as the unique actuator. The implementation of multiple-input multiple-output control systems becomes then complex since every channel is addressed in a single-loop fashion. This capability is however required for the future generation of reusable rocket, using also aerodynamic steering based on fins in addition to the TVC system for enhancing control authorities. Moreover, model uncertainties are not accurately considered in the design process, developed only with nominal conditions and stability margin requirements. For all these reasons, it results in an extensive (both in terms of time and cost) Verification and Validation campaign with many iterations and Monte-Carlo analyses to assess the performance and robustness of the control system. To overcome these drawbacks, the H-infinity family of methods, introduced a few years ago [8], provides with a powerful solution for robust control design. It relies on defining the control requirements in the frequency domain in terms of weighting functions and minimising the maximum gain of the resulting weighted system from the exogenous inputs to the outputs to be controlled. Moreover, the control-plant interaction is modelled through a Linear Fractional Transformation (LFT) representing the feedback action. Finally, the structured H-infinity method [9] allows to directly impose a specific control structure – like a Proportional-Integral-Derivative (PID), enabling the re-use of gain-scheduling techniques – and to consider parametric uncertainties for enhanced robustness. This paper studies the synthesis of a robust control system via structured H-infinity for the RLV atmospheric re-entry problem. First, the nonlinear 6-Degree-of-Freedom (6-DoF) RLV re-entry dynamics are simplified into a linear model and then linearised along a reference trajectory to get the nominal LFT of the system, then augmented with parametric uncertainties. The model covers the atmospheric re-entry and vertical landing of a first-stage rocket equipped with a TVC system and steerable planar fins. The controllers are built at different points of the re-entry trajectory, using the structured H-infinity framework through PID-like structures. Weighting functions considering the control objectives and requirements of a realistic RLV re-entry scenario are implemented. A robust stability analysis of the obtained system is performed via classical stability margins and structured singular value. Finally, the controllers are gain-scheduled and validated via Monte-Carlo analyses, using a nonlinear 6-DoF RLV re-entry dynamics simulator equipped with successive convex optimisation guidance. The performance of the resulting guidance and control architecture is compared with the baseline developed in previous works [10], using a TVC system only and classical linear feedback control through gain-scheduled PID controllers. This study lies within the ASCenSIon (Advancing Space Access Capabilities - Reusability and Multiple Satellite Injection) project, an innovative training network funded within H2020.

Adaptive control algorithm is considered as one of the effective solutions of the load frequency control issue. It gives a solution of problems of fixed parameters conventional controllers by achieving an online tuning of the gains of the controllers. A Jaya approach as a recent optimization method has been used in this paper to do this online tuning of the classical PID controller. Matlab/Simulink has been utilized to make a digital simulation. Digital results prove that, the microgrid power system that uses the proposed adaptive control method gives more robust frequency response comparing with the system that uses the fixed parameters control method in case of step load change and random load variation.