Optimal Control Objective Research Articles

Psychoeducative groups help control type 2 diabetes in a primary care setting.

The purpose of this study is to measure the impact of a psychoeducational group intervention in diabetes using glycosylated haemoglobin (HbA1c), the body mass index (BMI) and cardiovascular risk factors (CVRF) compared with conventional educational measures provided individually. A quasi-experimental study (pre/post-intervention) with a non-equivalent control group was conducted, including 72 type 2 individuals with diabetes (mean data: age 63.08 years, HbA1C 6.98%, BMI 30.48 kg/m2).The beneficial effect of psychoeducational group therapy in the study group (PGT) was compared with conventional diabetes education in the control group (CG). The PGT had a higher mean HbA1c reduction (-0.51 ± 1.7 vs. -0.06 ± 0.53%, p 0.003), met the objectives of optimal control of HbA1c to a higher degree (80% vs. 48%, p 0.005) and greater mean weight reduction (-1.93 ± 3.57 vs. 0.52 ± 1.73 kg, p 0002) than the CG.A significant improvement in total cholesterol, LDL cholesterol, triglycerides, systolic and diastolic blood pressure was achieved in PGT (all p < 0.05). PGT patients achieved a significant improvement in HbA1C, BMI and CVRF, and outperformed the conventional diabetes education group in achieving the optimal diabetes control objectives. Structural changes in the assistance programs should be considered to introduce these more efficient therapies for diabetes education in primary care.

Design Application in Suspender Tensile Force of Tied-Arch Bridges Based on Inversion Optimization Method

Taking an actual tied-arch bridge project as the background, this thesis has a system static analysis by building up the finite element analysis model with Midas/civil, a large-scale FEM software, then establishes a mathematical optimization model for the back analysis to suspender tensile forces through two optimization schemes. Scheme one takes the reasonable bending moment distribution and minimum moment stain energy as the optimal control objective function to inverse analysis. Scheme two takes the bending moment distribution of dead load as the control objective of inversion optimization analysis, when the difference of the positive and negative absolute value is the minimum in the moment envelope under the load combination, which thinks about live load on the finished state. Through comparing the analysis results, the thesis demonstrates that the inversion optimization method could fulfill the requirement of factual project and be widely used for determining suspender tensile forces of tie-arch bridges.

Use of optimal control models to predict treatment time for managing tick-borne disease

Tick-borne diseases have been on the rise recently, and correspondingly, there is an increased interest in implementing control measures to decrease the risk. Optimal control provides an ideal tool to identify the best method for reducing risk while accounting for the associated costs. Using a previously published model, a variety of frameworks are assessed to identify the key factors influencing mitigation strategies. The level and duration of tick-reducing efforts are key metrics for understanding the successful reduction in tick-borne disease incidence. The results show that the punctuated nature of the tick's life history plays a critical role in reducing risk without the need for a permanent treatment programme. This work suggests that across a variety of optimal control frameworks and objective functionals within a closed environment, similar strategies are created, all suggesting that the tick-borne disease risk can be reduced to near zero without completely eliminating the tick population.

Energy optimal spacecraft attitude control subject to convergence rate constraints

Attitude control of operational satellites is still predominantly performed by standard controllers such as Proportional plus Derivative (PD) control laws, which are still preferred for implementation to the computationally intensive nonlinear optimal control techniques, representing higher implementation complexity. In this paper, an inverse optimal control approach based on phase space geometry is presented, which is easy to implement and free from numerical and computational issues. The optimal control objective is to minimize a norm of the control torque subject to a rapidity constraint on the convergence rate of a Lyapunov function, under the effect of a benchmark controller. The proposed optimization method is shown to significantly enhance the torque-rapidity trade-off compared to the benchmark controller, chosen to be a PD law then a sliding mode controller. The inverse optimal control scheme is implemented on an air bearing table experimental platform.

Automatic Setting Control of the Raw Slurry Blending Process Based on the Case-Based Reasoning

Raw slurry blending process is the key process in the sintering alumina production. In this blending process, raw materials are alkali powder, red mud, blending ore and limestone, and the product is the raw slurry. The optimal operation control objective of this blending process is to make the quality indices of the raw slurry into their targeted ranges. The key step to realize this control objective is to decide the appropriate set-points of the control loop. An automatic setting control method is proposed in this paper. During the setting process, case-based reasoning is adopted to obtain the appropriate set-points of the control loops according to the process data and state. By using this setting control method, appropriate set-points can be obtained and the operation control objectives can be realized.

Optimal operation control of the raw slurry blending process using the case-based reasoning and neural network

Raw slurry blending process is a key unit in the sintering alumina industry. In this blending process, raw materials are grinded and blended in the mills to produce raw slurry. The optimal operation control objective is to make the quality indices of raw slurry into their targeted ranges. Flow rates of raw materials are the key factors that affect the quality indices of raw slurry. So, in order to realise the optimal operation control objective, right set points of flow rates must be obtained. However, due to the dynamics between quality indices and control loops of flow rates with complex natures, such as strong non-linearity, heavy coupling and difficulty of description by the accurate model, such a control objective is difficult to be realised by existing control methods. An intelligent optimal control method, which is comprised of the setting layer and the loop control layer, is proposed. In the setting layer, case-based reasoning (CBR) and neural network are adopted to obtain right set points of the control loops. In the loop control layer, the actual flow rates of raw materials follow their set points obtained from the setting layer. At last, the results of industry experiments have proven the effectiveness of the proposed method.

Hybrid Intelligent Optimal Control Method for Operation of Complex Industrial Processes

摘要: 工业过程运行的优化控制的目标是将反应产品在加工过程中的质量、效率、消耗的工艺指标控制在目标值范围内. 由于复杂工业过程的工艺指标难于在线测量且与控制回路输出之间的动态特性具有强非线性、强耦合、难以用精确模型描述、随生产边界条件变化而变化的综合复杂性,因此, 难以采用已有优化控制方法, 运行控制只能采用人工设定的控制方式. 由于人工控制不能及时准确地随运行工况调整设定值, 难以将工艺指标控制在目标值范围内, 甚至造成故障工况.本文提出了根据运行工况实时调整控制回路设定值, 通过控制系统跟踪调整后的设定值, 将工艺指标控制在目标值范围内的过程优化运行的混合智能控制方法. 该方法由控制回路预设定模型、前馈补偿与反馈补偿器、工艺指标预报模型、故障工况诊断和容错控制器组成. 在某选矿厂22台竖炉组成的焙烧过程的应用案例, 证明了所提出方法的有效性. 关键词: 复杂工业过程 / 过程运行 / 工艺指标 / 智能优化控制

On the Simulation of Traffic Signals Operation

Traffic lights are available in almost all cities and towns worldwide. They solve many traffic congestion problems, save numerous human lives and avoid frequent automobile accidents. Optimizing the operation of traffic light systems in roads is essential for any city's economy, safety and environment. Simulation technology is probably the best tool to analyze and optimize the operation and design of traffic light systems. The increasing use of automobiles in cities has lead city administrators and local governments to install traffic lights and detectors in large quantities in order to avoid accidents and optimize the movement of automobiles and humans. Although signalization can solve traffic conflicts at the intersections, it causes stoppage. The chief objective of optimal traffic control is to improve system mobility and throughput at reduced delays. Delays and stops are not only irritating and pricey to drivers and passengers, but they also add to air contamination and energy use. In this paper, we analyze and demonstrate how the timings of a traffic light system are calculated in order to determine the optimum and proper timings needed to optimize traffic flow.

A novel and effective particle swarm optimization like algorithm with extrapolation technique

A novel competitive approach to particle swarm optimization (PSO) algorithms is proposed in this paper. The proposed method uses extrapolation technique with PSO (ePSO) for solving optimization problems. By considering the basics of the PSO algorithm, the current particle position is updated by extrapolating the global best particle position and the current particle positions in the search space. The position equation is formulated with the global best (gbest) position, local best position (pbest) and the current position of the particle. The proposed method is tested with a set of 13 standard optimization benchmark problems and the results are compared with those obtained through two existing PSO algorithms, the canonical PSO (cPSO), the Global-Local best PSO ( GLBest PSO). The cPSO includes a time-varying inertia weight (TVIW) and time-varying acceleration co-efficients (TVAC) while the GLBest PSO consists of Global-Local best inertia weight ( GLBest IW) with Global-Local best acceleration co-efficient ( GLBestAC). The simulation results clearly elucidate that the proposed method produces the near global optimal solution. It is also observed from the comparison of the proposed method with cPSO and GLBest PSO, the ePSO is capable of producing a quality of optimal solution with faster convergence rate. To strengthen the comparison and prove the efficacy of the proposed method a real time application of steel annealing processing (SAP) is also considered. The optimal control objectives of SAP are computed through the above said three PSO algorithms and also through two versions of genetic algorithms (GA), namely, real coded genetic algorithm (RCGA) and hybrid real coded genetic algorithm (HRCGA) and the results are analyzed with the proposed method. From the results obtained through benchmark problems and the real time application of SAP, it is clearly seen that the proposed ePSO method is competitive to the existing PSO algorithms and also to GAs.

Hybrid Intelligent Control for Optimal Operation of Shaft Furnace Process

The extensively used shaft furnace in the ore concentration industry is an important facility that turns the weak-magnetic low-grade hematite ore into strong-magnetic one. During the operation of the shaft furnace roasting process, the optimal control objective is to control the technique indices, namely the magnetic tube recovery ratio (MTRR) that represents the quality, the efficiency, and the consumption of the product processing, into its targeted ranges. However, due to the complex dynamics between the MTRR and the control loops, such a control objective is by far difficult to achieve by the existing control methods, thus only manual control is adopted. In this paper, a hybrid intelligent control method for the optimal process operation is proposed with the purpose of controlling the technique indices into the desired range by on-line adjusting the set-points of the control loops. The proposed method was applied to the roasting process undertaken by 22 shaft furnaces in the ore concentration plant of Jiuquan Steel & Iron Ltd in China. The application results show that the MTRR is controlled to the targeted range with 2% increase; the faulty working-conditions are eliminated, which boosts the equipment operation ratio by 2.98%, resulting in a raise of 0.57% in the concentrated grade and 2.01% in the metal recovery ratio.

Decision making in fuzzy discrete event systems

The primary goal of the study presented in this paper is to develop a novel and comprehensive approach to decision making using fuzzy discrete event systems (FDES) and to apply such an approach to real-world problems. At the theoretical front, we develop a new control architecture of FDES as a way of decision making, which includes a FDES decision model, a fuzzy objective generator for generating optimal control objectives, and a control scheme using both disablement and enforcement. We develop an online approach to dealing with the optimal control problem efficiently. As an application, we apply the approach to HIV/AIDS treatment planning, a technical challenge since AIDS is one of the most complex diseases to treat. We build a FDES decision model for HIV/AIDS treatment based on expert’s knowledge, treatment guidelines, clinic trials, patient database statistics, and other available information. Our preliminary retrospective evaluation shows that the approach is capable of generating optimal control objectives for real patients in our AIDS clinic database and is able to apply our online approach to deciding an optimal treatment regimen for each patient. In the process, we have developed methods to resolve the following two new theoretical issues that have not been addressed in the literature: (1) the optimal control problem has state dependent performance index and hence it is not monotonic, (2) the state space of a FDES is infinite.

Observer with evolutionary-programming-based optimal digital tracker for analog neutral multiple time-delay systems

In this paper, the finite-time optimal hybrid tracking control problem for analog neutral systems with multiple discrete and distributed time delays is formulated and studied. A novel approach that combining the evolutionary programming (EP) algorithm and the high-gain property for analog neutral multiple time-delay systems is proposed to achieve the “best” tracking performance for the first time. First, the Newton center interpolation formula and the linear interpolation formula, together with a modified rectangular rule and an augmented state vector, are utilized to construct the equivalent digital linear models. Next, according to the separation principle, the control gains of the digital tracker and the digital observer are designed separately by the discrete-time optimal control theory and prediction-based digital redesign method, and then combined together to form an observer-based optimal time-varying digital tracker. Eventually, the process is to combine the high-gain property with EP algorithm to search the optimal weighting matrices in the performance index to form an observer with EP-based low-gain digital tracker to achieve the objective of optimal tracking control. An example is given for illustrating the effectiveness of the new tracker design method.

Optimal Control of Continuous Infrared Dryers

In this work, the optimal control policies of radiant temperature versus dryer length are determined for the continuous, steady-state operation of infrared dryers. The optimal control objective is to minimize the product humidity subject to a mathematical model of infrared dryers as well as a set of process inequality constraints. A robust optimal control method based on genetic algorithms is applied. Multiple air injections are also considered. The optimal results show considerable reduction in product humidity. Comparisons with corresponding base cases (using maximum possible uniform radiant temperatures) indicate improvements greater than 50% with the application of optimal control policies.

Optimal control determination of MMA polymerization in non-isothermal batch reactor using bifunctional initiator

In this work, we determine the optimal control for free-radical methyl methacrylate polymerization using a bifunctional initiator in a non-isothermal batch reactor. A detailed unsteady-state model of the process is employed. Four different optimal control objectives are realized, each of which optimizes a given variable simultaneously with the specification of another. The first two objectives involve the maximization of monomer conversion in a specified operation time, and the minimization of operation time for a specified, final monomer conversion. The last two objectives involve the maximization of monomer conversion for specified, final number and weight average polymer molecular weights. The temperature of heat-exchange fluid inside reactor jacket is considered as a control function of an independent variable. To meet the specification of an optimization variable other than time, the differential model of batch process is derived in the range of specified variable. Equations are provided for Jacobian evaluations to help in the accurate solution of process model. A genetic algorithms-based optimal control method is applied to realize the four optimal control objectives. The results show that optimal control can significantly enhance the performance of the batch polymerization process.

Optimal Control of Batch MMA Polymerization with Specified Time, Monomer Conversion, and Average Polymer Molecular Weights

AbstractSummary: In this paper, the optimal control policies are determined for the free‐radical polymerization of methyl methacrylate (MMA) in a non‐isothermal batch reactor. The temperature of the fluid inside reactor‐jacket is used as the control function to realize four different optimal control objectives. Each objective is formulated to optimize a given variable simultaneously specifying another. The first two objectives target the maximization of monomer conversion in a specified operation time, and the minimization of operation time for a specified, final monomer conversion. The last two objectives target the maximization of monomer conversion for specified, final number‐ and weight‐average polymer molecular weights. The realization of these objectives is expected to be very useful for the batch production of polymers. To meet the specification of an optimization variable other than time, the differential model of batch process is expressed and utilized in the range of specified variable. Equations are provided for Jacobian evaluations to help in the accurate solution of process model. A genetic algorithms‐based optimal control method is applied to realize the four optimal control objectives. The results of this application show considerable improvements in the performance of batch MMA polymerization. magnified image

Optimal control and simulation of multidimensional crystallization processes

The optimal batch control of a multidimensional crystallization process is investigated. A high resolution algorithm is used to simulate the multidimensional crystal size distribution under the operations defined by two optimal control trajectories. It is shown that a subtle change in the optimal control objective can have a very large effect on the crystal size and shape distribution of the product crystals. The effect of spatial variation is investigated using a compartmental model. The effect of differing numbers of compartments on the size and shape distribution of the product crystals is investigated. It is shown that the crystal size distribution can be very different along the height of the crystallizer and that a solution concentration gradient exists due to imperfect mixing. The nucleation rate can be significantly larger at the bottom of the crystallizer and the growth rate can be much larger at the top. The high resolution method provides high simulation accuracy and fast speed, with the ability to solve large numbers of highly nonlinear coupled multidimensional partial differential equations over a wide range of length scales. A parallel programming implementation results in simulation times that are short enough for using the simulation program to compute optimal control trajectories.

On constrained infinite-time nonlinear optimal control

In this work, we consider the infinite-time optimal control of input affine nonlinear systems subject to point-wise in time inequality constraints on both the process inputs and outputs. Fundamental to solving this constrained infinite-time nonlinear optimal control (CITNOC) problem is the ability to calculate the value function of it's unconstrained counterpart, the infinite-time nonlinear optimal control (ITNOC) problem. Unfortunately, the traditional ITNOC solution procedure of specifying an objective function and then solving for the optimal control policy and corresponding value function is computationally intractable in all but the simplest of examples. However, in many cases one can easily identify a stabilizing feedback for near operating point regulation. Building from this local policy, the proposed method is to construct a meaningful optimal control objective function as well as its corresponding value function. These functions are then used to analyze the closed-loop stability of the proposed policy. Upon return to the constrained case the constructed objective and value functions are again used to develop a self-consistent constrained finite-time scheme that will, for the first time, provide an exact solution to the CITNOC problem. The mechanics of the proposed method are then illustrated by an example from chemical reactor control.

STUDY ON THE OPTIMAL FIELD PATTERN CONTROL TO THE FIELD CONJUGATE DIRECT SYNTHESIS OF PHASED-ARRAY ULTRASOUND HYPERTHERMIA

This paper mainly deals with problems of optimal field pattern control to the field conjugate direct synthesis of phased-array ultrasound hyperthermia. First,the rational optimal objective function for acoustic field pattern is put forward. Then, two optimal algorithms for global optimization are proposed. One is the genetic algorithm for constructing acoustic field pattern control and the other is the eigenvector algorithm for the same purpose. The simulation is performed with these two algorithms, genetic algorithm and eigenvector algorithm. Eventually, the objective of optimal field pattern control is achieved successfully. The genetic algorithm has better characteristics as compared with the eigenvector algorithm.

Optimal control problems in the theory of smart structures

The methods of optimal control are applied to the problems of the theory of smart structures. Two optimization problems in the theory of smart structures are formulated and solved. These problems are concerned with the damping of mechanical oscillations by controlling the rigidity of the structure and applied force. The optimal controls are obtained in the form of optimal syntheses. In the first problem, the optimization criterion is to minimize the time required to transfer the system to a required state. The recurrent formula for the segments constituting the switching curve is derived. The second optimization problem is concerned with reduction of energy of oscillations by controlling the coefficient of rigidity of the structure. The objective of optimal control is to transfer the system to a given lower energy level in a minimal time. An explicit expression for all the segments of the switching curve is obtained.

Generation of optimal control policy for flexible manufacturing cells: A Petri net approach

For flexible manufacturing cells (FMCs), the major control problem is to determine the optimal part routeing and machines dispatching policy. In this research, we present a methodology for generating optimal operating strategies for FMCs. The methodology is built on two techniques: stochastic timed Petri net (STPN) and Markov decision process (MDP). The operating strategy devised with the proposed methodology is deadlock-free and provides the best performance given a set of system parameters (machines, parts, machining times, etc.). Generation of programmable logic controller (PLC) codes is greatly facilitated as the generated strategy can be mapped directly to a ladder diagram representing the PLC code. The objective of real-time optimal control could then be achieved with this type of controller. An example is provided to illustrate the methodology.