公民反应在急救医疗服务体系的地位和作用

公民反应是急救医疗服务体系中的重要环节。本文介绍了公民反应的地位和作用,公民反应的内容,应该遵循的基本原则以及面临的障碍,并提出了提高公民反应的质量需解决的三个问题。对政府决策部门与急救专业人员,均有借鉴和参考价值。     

Robust model predictive control for uncertain step response

The design of robust model predictive control for handling bounded uncertainties in step response of unconstrained MIMO processes is considered. The control law is obtained by minimizing an upper bound of the objective function and it consists of an optimal state feedback gain and a robust state observer. The separation principle is found to be applicable between the state feedback gain and the robust state observer. Simulation results show that the proposed algorithm can provide an improved handling of the uncertainty in step response as compared with a nominal MPC algorithm. Copyright © 2000 John Wiley & Sons, Ltd.     

Optimal power consumption for demand response of thermostatically controlled loads

We consider the problem of determining the optimal aggregate power consumption of a population of thermostatically controlled loads such as air conditioners. This is motivated by the need to synthesize the demand response for a load serving entity (LSE) catering a population of such customers. We show how the LSE can opportunistically design the aggregate reference consumption to minimize its energy procurement cost, given day‐ahead price, load forecast, and ambient temperature forecast, while respecting each individual load's comfort range constraints. The resulting synthesis problem is intractable when posed as a direct optimization problem after Euler discretization of the dynamics, since it results in a mixed‐integer linear programming problem with number of variables typically of the order of millions. In contrast, in this paper, we show that the problem is amenable to continuous‐time optimal control techniques. Numerical simulations elucidate how the LSE can use the optimal aggregate power consumption trajectory thus computed, for the purpose of demand response.     

Online adaptive algorithm for optimal control of structures subjected to travelling loads

The problem of adaptive optimal semiactive control of a structure subjected to a moving load is studied. The control is realised by a change of damping of the structure's supports. The results presented in the previous works of the authors demonstrate that switched optimal controls can be very efficient at reducing the vibration levels of the structure. On the other hand, these controls exhibit a high sensitivity to changes of the speed of the travelling load. The aim of this paper is to develop an algorithm that enables real‐time adaptation of the optimal controls according to both the measured speed of the travelling load and the estimated state of the structure. The control objective is to provide smooth passage for the vehicles and reduce the material stresses on the carrying structures. The designed adaptive algorithm uses reference optimal controls computed for constant speeds and a set of functions describing the sensitivity of the system dynamics to the measured parameters. The convergence of the algorithm, as well as aspects of its implementation, is studied. The performance of the proposed method is validated by means of numerical simulations conducted for different travelling speed scenarios. In the assumed objective functional, the proposed adaptive controller can outperform the reference optimal solutions by over 50%. The practicality of the proposed method should attract the attention of practising engineers.     

Computationally efficient integrated design and predictive control of flexible energy systems using multi-fidelity simulation-based Bayesian optimization

We present a multi-fidelity black-box optimization approach for integrated design and control (IDC) of constrained nonlinear systems in the presence of uncertainty. The IDC framework is becoming increasingly important for the systematic design of next-generation (flexible) manufacturing and energy systems. However, identifying optimal solutions to realistic IDC problems is intractable when (i) the dynamics occur on much shorter timescales than the system lifetime, (ii) the uncertainties are described by continuous random variables with high variance, and (iii) operational decisions involve a mixture of discrete and continuous variables. Instead of aggressively simplifying the problem to improve tractability, we develop a simulation-based optimization procedure using high-quality decision rules that map information that can be measured online to optimal control actions. In particular, we rely on the Bayesian optimization (BO) framework that has been shown to perform very well on noisy and expensive-to-evaluate objective functions. We also discuss how BO can be extended to take advantage of computationally cheaper low-fidelity approximations to the high-fidelity IDC cost function. Three major low-fidelity approximation strategies are described in this work, which are related to the simplification of the system simulator, decision rule solution method, and time grid. Lastly, we demonstrate the advantages of multi-fidelity BO on the design of a solar-powered building heating/cooling system (with battery and grid support) under uncertain weather and demand conditions with hourly variation over a year-long planning horizon.     

Load frequency predictive control for power systems concerning wind turbine and communication delay

This article addresses a model predictive control (MPC) technique for load frequency control (LFC) system in the presence of wind power, communication delay, and denial-of-service (DoS) attack. In this article, communication delay is incorporated into a single area control error transmission for simplicity, wind power and load disturbance are regarded as Lipschitz nonlinear terms, as for the randomly occurring DoS attack, it is modeled as Bernoulli processes with known conditional probability. Thinking all these adverse factors to stability and the limitation of input constraint synthetically, the stability of LFC system can be guaranteed by delay-dependent Lyapunov function lemma and a state feedback MPC controller is designed to solve the LFC problems by minimizing the infinite-horizon objective function. Although some scholars have studied the performance degradation and instability of LFC system caused by cyber attack and/or communication delay and some very nice results have been addressed, limited works have considered the MPC approach to deal with both the problems of cyber attack and communication delay which explicitly considers the physical constraints. In addition, the delay-dependent Lyapunov function is adopted to deal with the problem of communication delay, which results in less conservatism of the presented method. Finally, the optimization problem with input constraint is solved and proven to be recursive feasibility, and the closed-loop system turns out to be stable. The reasonability and validity of the provided strategy is verified through several groups of simulation experiments. It illustrates that the proposed control method can keep the system frequency steady in the standard range in spite of various attack conditions.     

LMI-based cooperative distributed model predictive control for Lipschitz nonlinear systems

In this paper, a distributed model predictive control is proposed to control Lipschitz nonlinear systems. The cooperative distributed scheme is considered where a global infinite horizon objective function is optimized for each subsystem, exploiting the state and input information of other subsystems. Thus, each control law is obtained separately as a state feedback of all system's states by solving a set of linear matrix inequalities. Due to convexity of the design, convergence properties at each iteration are established. Additionally, the proposed algorithm is modified to optimize only one control input at a time, which leads to a further reduction in the computation load. Finally, two application cases are studied to show the effectiveness of the proposed method.     

Improving the grid frequency by optimal design of model predictive control with energy storage devices

This paper proposes the optimal design of model predictive control (MPC) with energy storage devices by the bat‐inspired algorithm (BIA) as a new artificial intelligence technique. Bat‐inspired algorithm‐based coordinated design of MPCs with superconducting magnetic energy storage (SMES) and capacitive energy storage (CES) is proposed for load frequency control. Three‐area hydrothermal interconnected power system installed with MPC and SMES is considered to carry out this study. The proposed design procedure can account for generation rate constraints and governor dead bands. Transport time delays imposed by governors, thermodynamic processes, and communication telemetry can be captured as well. In recent papers, the parameters of MPC with SMES and CES units are typically set by trial and error or by the designer's expertise. This problem is solved here by applying BIA to tune the parameters of MPC with SMES and CES units simultaneously to minimize the deviations of frequency and tie line powers against load perturbations. Simulation results are carried out to emphasize the superiority of the proposed coordinated design as compared with conventional proportional‐integral controller and with BIA‐based MPC without SMES and CES units.     

Economic model predictive control for time-varying system: Performance and stability results

We consider economic model predictive control (MPC) without terminal conditions for time-varying optimal control problems. Under appropriate conditions, we prove that MPC yields initial pieces of approximately infinite horizon optimal trajectories and that the optimal infinite horizon trajectory is practically asymptotically stable. The results are illustrated by numerical examples motivated by energy-efficient heating and cooling of a building.     

Multicriteria optimal operation of a microgrid considering risk analysis,renewable resources,and model predictive control

This paper proposes an optimal power dispatch by taking into account risk management and renewable resources. In particular, it examines how control engineering and risk management techniques can be applied in the field of power systems through their use in the design of risk-based model predictive controllers. To this end, this paper proposes a two-layer control scheme for microgrid management where both levels are based on model predictive control (MPC): the higher level is devoted to risk management while the lower layer is dedicated to power dispatching. In particular, the high-level controller is based on a risk-based approach where potential risks have been identified and evaluated. Mitigation actions are the decision variables to be optimized to reduce the consequences of risks and costs. The MPC-based algorithm decides the appropriate frequency of mitigation actions such as changes in references, constraints, and insurance contracting, by relying on a model that includes integer variables, identifiable risks, their costs, and the cost/benefit assessment of mitigating actions. On the other hand, the low-level controller drives the plant to suitable values to satisfy demands. A series of simulations on a nonlinear model of a real laboratory-scale power plant located in the facilities of the University of Seville are conducted under varying conditions to demonstrate the effectiveness of the algorithm when risks are explicitly considered.     

A Two-Level optimal final-value control system for non-linear plants realized with mini/micro computers

A two-level approach for optimal final-value control of non-linear systems is considered. On the higher level, an on-line optimization (using a Ritz parameterization of the control functions¹) is performed during each major sampling interval in order to compute the control functions for the next intervals. On the lower level, an explicit model-following control is used with a minor sampling interval. This allows, even for heavily disturbed systems, sufficiently long major intervals for the on-line optimization. An ammonia reactor² is used as an example, and the application of this procedure is discussed. A realization of the controller using a hierarchical computer configuration gives an indication that, with a reasonable amount of hardware, sampling intervals of about one minute for the on-line optimization and well below one second for the model-following control may be obtained.     

Experimental results of a control time delay system using optimal control

The optimal control for a temperature system with time delay is considered. Experimental results of the control system are presented in this contribution. The integral term in the controller is approximated by a quadrature method. Experimental results obtained demonstrate the effectiveness of the approximation method. By a simple analysis in time domain, we demonstrate the robustness of the optimal controller. We compare the optimal control's performance with an industrial PID controller. This controller was robustly tuned. The experiments indicate the correct optimization of the plant when the optimal control was employed, despite limitations in the sensor, actuators, non‐modeled dynamics, and uncertain parameters of the process. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimal control of HVAC systems using DDP and NLP techniques

The objective of this study is to apply the differential dynamic programming (DDP) technique of optimal control to heating, ventilating and air-conditioning (HVAC) systems and to compare its performance with a non-linear programming (NLP) technique using the sequential quadratic programming method. The DDP technique is briefly described and studied. Limitations of the technique are noted. Three cases of a system that has been treated previously in the literature are optimized by the two techniques and the computational times compared. The study shows DDP to be efficient compared with NLP for the example problems. NLP is, however, more robust and general and can treat constraints on the state variables directly. Further investigation is needed for larger-scale problems to fully explore the features of the two methods.     

Optimization design of RBF‐ARX model and application research on flatness control system

The radial basis function (RBF) network and autoregressive exogenous (ARX) model are combined to form the structure of the RBF‐ARX model. The RBF‐ARX model can describe the global nonlinear dynamic process of the object, and its function coefficients are approximated by data‐driven method. The structured nonlinear parameters optimization method (SNPOM) is generally used to optimize model parameters, but this method is very complicated and hard to be mastered by engineers. However, genetic algorithm (GA) is simple and widely used. So the thought of GA optimizing RBF‐ARX is generated, called GA‐ARX‐RBF, and applied to nonlinear dynamic flatness control system. In this article, the recursive least squares method to optimize linear weights of RBF is also used to improve the SNPOM, which reduces the complexity and storage capacity of data processing. Meanwhile, GA to optimize all the parameters of the RBF‐ARX model replaces SNPOM completely. A GA‐RBF‐ARX modeling and optimizing method is proposed. In order to prove the efficiency of GA‐RBF‐ARX, it is applied into flatness control system, which has the characters of nonlinear, multivariable, and multi‐disturbance. The flatness recognition model and flatness predictive model are established. A predictive controller based on GA‐RBF‐ARX is designed for 900HC reversible cold rolling mill. The simulation results demonstrate that the flatness control system based on GA‐RBF‐ARX is effective and has a better precision. The method is easily mastered by engineers and helps to promote the practical value of RBF‐ARX. Copyright © 2016 John Wiley & Sons, Ltd.     

Discrete mechanics and optimal control for constrained systems

The equations of motion of a controlled mechanical system subject to holonomic constraints may be formulated in terms of the states and controls by applying a constrained version of the Lagrange‐d'Alembert principle. This paper derives a structure‐preserving scheme for the optimal control of such systems using, as one of the key ingredients, a discrete analogue of that principle. This property is inherited when the system is reduced to its minimal dimension by the discrete null space method. Together with initial and final conditions on the configuration and conjugate momentum, the reduced discrete equations serve as nonlinear equality constraints for the minimization of a given objective functional. The algorithm yields a sequence of discrete configurations together with a sequence of actuating forces, optimally guiding the system from the initial to the desired final state. In particular, for the optimal control of multibody systems, a force formulation consistent with the joint constraints is introduced. This enables one to prove the consistency of the evolution of momentum maps. Using a two‐link pendulum, the method is compared with existing methods. Further, it is applied to a satellite reorientation maneuver and a biomotion problem. Copyright © 2009 John Wiley & Sons, Ltd.     

Discrete-time optimal control of an economic system using different objective functions

In most applications of optimal control of econometric models, the objective (welfare cost) functions employed are quadratic, although some authors have suggested linear functions. One reason for choosing the quadratic objective function is that with linear system dynamics, the optimal trajectories can easily be computed by using the Riccati equation approach. With a linear objective function and with constraints on the control and/or the state variables, large-scale linear programming techniques can be used instead of the Riccati equation to obtain optimal trajectories. In this paper, not only piecewise quadratic but also piecewise linear minimax-and entropy-type objective functions are considered. As an illustration, the 35-equation CLEAR model of Canada is optimized with various of these objective functions and the results are then compared. The relative merits of the different approaches are discussed from an economic policy point of view. Algorithmic and numerical solution aspects are also addressed.     

Existence of optimal control for an integro-differential Bolza problem

In the article, we derive an existence theorem for a Bolza problem described by a nonlinear integro-differential system of Volterra type. We use an approach based on the lower closure theorem for orientor fields due to Cesari and measurable selection theorem of Fillipov type due to Rockafellar. Paper is a continuation of [D. Idczak, S. Walczak, Necessary optimality conditions for an integro-differential Bolza problem via Dubovitskii-Miljutin method, DCDS-B 24 (5) (2019), 2281-2292; DOI:10.3934/dcdsb.2019095].     

On fusion of PCA and a physical model‐based predictive control strategy for efficient load‐cycling operation of a thermal power plant

Controlling a thermal power plant optimally during load‐cycling operation is a very challenging control problem. The control complexity is enhanced further by the possibility of simultaneous occurrence of sensor malfunctions and a plethora of system disturbances. This paper proposes and evaluates the effectiveness of a sensor validation and reconstruction approach using principal component analysis (PCA) in conjunction with a physical plant model. For optimal control under severe operating conditions in the presence of possible sensor malfunctions, a predictive control strategy is devised by appropriate fusion of the PCA‐based sensor validation and reconstruction approach and a constrained model predictive control (MPC) technique. As a case study, the control strategy is applied for thermal power plant control in the presence of a single sensor malfunction. In particular, it is applied to investigate the effectiveness and relative advantage of applying rate constraints on main steam temperature and heat‐exchanger tube‐wall temperature, so that faster load cycling operation is achieved without causing excessive thermal stresses in heat‐exchanger tubes. In order to account for unstable and non‐minimum phase boiler–turbine dynamics, the MPC technique applied is an infinite horizon non‐linear physical model‐based state‐space MPC strategy, which guarantees asymptotic stability and feasibility in the presence of output and state constraints. Copyright © 2007 John Wiley & Sons, Ltd.     

Optimal control of a revenue management system with dynamic pricing facing linear demand

This paper considers a dynamic pricing problem over a finite horizon where demand for a product is a time‐varying linear function of price. It is assumed that at the start of the horizon there is a fixed amount of the product available. The decision problem is to determine the optimal price at each time period in order to maximize the total revenue generated from the sale of the product. In order to obtain structural results we formulate the decision problem as an optimal control problem and solve it using Pontryagin's principle. For those problems which are not easily solvable when formulated as an optimal control problem, we present a simple convergent algorithm based on Pontryagin's principle that involves solving a sequence of very small quadratic programming (QP) problems. We also consider the case where the initial inventory of the product is a decision variable. We then analyse the two‐product version of the problem where the linear demand functions are defined in the sense of Bertrand and we again solve the problem using Pontryagin's principle. A special case of the optimal control problem is solved by transforming it into a linear complementarity problem. For the two‐product problem we again present a simple algorithm that involves solving a sequence of small QP problems and also consider the case where the initial inventory levels are decision variables. Copyright © 2006 John Wiley & Sons, Ltd.     

Energy‐optimal control of a particle in a dielectrophoretic system

Dielectrophoresis is an electrokinetics‐based phenomenon that involves the motion of a particle due to interaction between its dipole moment and an applied nonuniform electric field. This technique is very effective in particle manipulation and separation. In this paper, we consider an energy optimal transfer of a particle in a dielectrophoretic system from one specified location to another desired location. The system is described by a set of ordinary differential equations with a quadratic term in the control variable (the control being the applied voltage on the electrodes which induces the electric field), making the system nonaffine. Copyright © 2011 John Wiley & Sons, Ltd.