scholarly journals An Optimal Control Framework for Resources Management in Agriculture

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Fernando Lobo Pereira ◽  
Fernando Arménio Fontes ◽  
Maria Margarida Ferreira ◽  
Maria do Rosário Pinho ◽  
Vilma Alves Oliveira ◽  
...  
Keyword(s):  
Optimal Control ◽  
Infinite Horizon ◽  
Past Research ◽  
Resources Management ◽  
Optimization Framework ◽  
Control Framework ◽  
Control Scheme ◽  
Wide Range ◽  
And Control ◽  
The Pontryagin Maximum Principle

An optimal control framework to support the management and control of resources in a wide range of problems arising in agriculture is discussed. Lessons extracted from past research on the weed control problem and a survey of a vast body of pertinent literature led to the specification of key requirements to be met by a suitable optimization framework. The proposed layered control structure—including planning, coordination, and execution layers—relies on a set of nested optimization processes of which an “infinite horizon” Model Predictive Control scheme plays a key role in planning and coordination. Some challenges and recent results on the Pontryagin Maximum Principle for infinite horizon optimal control are also discussed.

scholarly journals An Optimal-Control Scheme for Coordinated Surplus-Heat Exchange in Industry Clusters

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1877 ◽  
Author(s):  
Brage Rugstad Knudsen ◽  
Hanne Kauko ◽  
Trond Andresen
Keyword(s):  
Optimal Control ◽  
Heat Exchange ◽  
Industrial Clusters ◽  
Industry Clusters ◽  
Industry Cluster ◽  
Heat Demand ◽  
Control Scheme ◽  
Optimal Control Scheme ◽  
And Control

Industrial plants organized in clusters may improve their economics and energy efficiency by exchanging and utilizing surplus heat. However, integrating inherently dynamic processes and highly time-varying surplus-heat supplies and demands is challenging. To this end, a structured optimization and control framework may significantly improve inter-plant surplus-heat valorization. We present a Modelica-based systems model and optimal-control scheme for surplus-heat exchange in industrial clusters. An industry-cluster operator is assumed to coordinate and control the surplus-heat exchange infrastructure and responsible for handling the surplus heat and satisfy the sink plants’ heat demands. As a case study, we use an industry cluster consisting of two plants with surplus heat available and two plants with heat demand. The total surplus heat and heat demand are equal, but the availability and demand are highly asynchronous. By optimally utilizing demand predictions and a thermal energy storage (TES) unit, the operator is able to supply more than 98% of the deficit heat as surplus heat from the plants in the industry cluster, while only 77% in a corresponding case without TES. We argue that the proposed framework and case study illustrates a direction for increasing inter-plant surplus-heat utilization in industry clusters with reduced use of peak heating, often associated with high costs or emissions.

ENSURING REACH ABILITY AND STABILITY IN THE SYNTHESIS OF ROBUST DISCRETE MODEL PREDICTIVE CONTROL IN CONDITIONS OF INCOMPLETE INFORMATION

2020 ◽  
Vol 7 (2) ◽  
pp. 29-33
Author(s):  
NGUYEN KHAC TUNG ◽  
◽  
ANTON ZHILENKOV ◽  
DANG BINH KHAC ◽  
Keyword(s):  
Optimal Control ◽  
Current Control ◽  
Initial State ◽  
Modeling And Control ◽  
Nanoscale Structures ◽  
Current State ◽  
Control Scheme ◽  
Methods Of Synthesis ◽  
And Control ◽  
Time Systems

Methods of synthesis of control of multiscale processes with predictive models for linear discrete time systems are considered. A description is given of a control scheme in which the current control action is obtained by solving at each instant of the sample the optimal control problem with a finite horizon without feedback and using the current state of the object as an initial state. An optimization problem is described that gives an optimal control sequence when the control obtained for the first step of the subsequent sequence is applied to the object. The analysis of the reachability and stability problems of synthesized controls with a predictive model under conditions of disturbances and uncertainties is given. As well as the problems of providing preset indicators of the quality of management and comparing indicators in the management of MPC in open and closed systems. The urgent issues requiring research in the framework of the considered management system are identified. The proposed solutions are extremely relevant to the problems of modeling and control of technological processes of growing nanoscale structures.

scholarly journals Quantum demolition filtering and optimal control of unstable systems

2012 ◽  
Vol 370 (1979) ◽  
pp. 5396-5407 ◽  
Author(s):  
V. P. Belavkin
Keyword(s):  
Optimal Control ◽  
Feedback Control ◽  
Open Loop ◽  
Information Dynamics ◽  
Unstable Systems ◽  
Dissipative Term ◽  
Control Scheme ◽  
Hamilton Jacobi Bellman ◽  
Classical Control ◽  
And Control

A brief account of the quantum information dynamics and dynamical programming methods for optimal control of quantum unstable systems is given to both open loop and feedback control schemes corresponding respectively to deterministic and stochastic semi-Markov dynamics of stable or unstable systems. For the quantum feedback control scheme, we exploit the separation theorem of filtering and control aspects as in the usual case of quantum stable systems with non-demolition observation. This allows us to start with the Belavkin quantum filtering equation generalized to demolition observations and derive the generalized Hamilton–Jacobi–Bellman equation using standard arguments of classical control theory. This is equivalent to a Hamilton–Jacobi equation with an extra linear dissipative term if the control is restricted to Hamiltonian terms in the filtering equation. An unstable controlled qubit is considered as an example throughout the development of the formalism. Finally, we discuss optimum observation strategies to obtain a pure quantum qubit state from a mixed one.

scholarly journals A Driving and Control Scheme of High Power Piezoelectric Systems over a Wide Operating Range

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4401
Author(s):  
Tianyue Yang ◽  
Yuanfei Zhu ◽  
Zhiwei Fang ◽  
Haoyu Wu ◽  
Wanlu Jiang ◽  
...  
Keyword(s):  
High Power ◽  
Vibration Amplitude ◽  
Operating Conditions ◽  
Phase Detector ◽  
Displacement Sensor ◽  
Operating Range ◽  
Control Scheme ◽  
Wide Range ◽  
And Control ◽  
Wide Operating

Significant variation in impedance under a wide range of loads increases the difficulty of frequency tracking and vibration control in high-power piezoelectric systems (HPPSs). This paper proposed a wide operating range driving and control scheme for HPPSs. We systematically analyzed the impedance characteristics and deduced the load optimization frequency. In order to provide sufficient drive capability, the inverter combined with an LC matching circuit is configured. With the aid of a transformer ratio arm bridge (TRAB) combined with a proposed pulse-based phase detector (PBPD), the proposed scheme can control the vibration amplitude and keep parallel resonance status under a wide range of loads. Experiments conducted under actual operating conditions verify the feasibility of the proposed scheme under the modal resistance range from 7.40 to 500 Ω and the vibration range from 20% to 100%. Moreover, with the aid of a laser displacement sensor, our scheme is verified to have a vibration amplitude control accuracy better than 2% over a tenfold load variation. This research could be helpful for the driving and control of HPPSs operating in a wide range.

scholarly journals Suboptimal Control Strategies for Finite-Time Nonlinear Processes with Input Constraints

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Pablo S. Rivadeneira ◽  
Eduardo J. Adam
Keyword(s):  
Optimal Control ◽  
Finite Time ◽  
Control Strategies ◽  
Batch Reactor ◽  
Infinite Horizon ◽  
Optimization Method ◽  
Suboptimal Control ◽  
Linear Matrix ◽  
Input Constraints ◽  
And Control

Novel techniques for the optimization and control of finite-time processes in real-time are pursued. These are developed in the framework of the Hamiltonian optimal control. Two methods are designed. The first one constructs the reference control trajectory as an approximation of the optimal control via the Riccati equations in an adaptive fashion based on the solutions of a set of partial differential equations called the α and β matrices. These allow calculating the Riccati gain for a range of the duration of the process T and the final penalization S. The second method introduces input constraints to the general optimization formulation. The notions of linear matrix inequalities allow us to recuperate the Riccati gain as in the first method, but using an infinite horizon optimization method. Finally, the performance of the proposed strategies is illustrated through numerical simulations applied to a batch reactor and a penicillin fed-batch reactor.

scholarly journals A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4191
Author(s):  
Sean Williams ◽  
Michael Short ◽  
Tracey Crosbie ◽  
Maryam Shadman-Pajouh
Keyword(s):  
Active Control ◽  
Demand Response ◽  
Control Strategy ◽  
Demand Forecasting ◽  
Time Operation ◽  
Control Framework ◽  
Control Scheme ◽  
Optimization And Control ◽  
And Control ◽  
Active Control Strategy

This paper presents a decentralized informatics, optimization, and control framework to enable demand response (DR) in small or rural decentralized community power systems, including geographical islands. The framework consists of a simplified lumped model for electrical demand forecasting, a scheduling subsystem that optimizes the utility of energy storage assets, and an active/pro-active control subsystem. The active control strategy provides secondary DR services, through optimizing a multi-objective cost function formulated using a weight-based routing algorithm. In this context, the total weight of each edge between any two consecutive nodes is calculated as a function of thermal comfort, cost (tariff), and the rate at which electricity is consumed over a short future time horizon. The pro-active control strategy provides primary DR services. Furthermore, tertiary DR services can be processed to initiate a sequence of operations that enables the continuity of applied electrical services for the duration of the demand side event. Computer simulations and a case study using hardware-in-the-loop testing is used to evaluate the optimization and control module. The main conclusion drawn from this research shows the real-time operation of the proposed optimization and control scheme, operating on a prototype platform, underpinned by the effectiveness of the new methods and approach for tackling the optimization problem. This research recommends deployment of the optimization and control scheme, at scale, for decentralized community energy management. The paper concludes with a short discussion of business aspects and outlines areas for future work.

Design and control of an automatic high-viscosity sealant filling system

2020 ◽  
pp. 095965182097308
Author(s):  
Dongzuo Tian ◽  
Xingyong Song
Keyword(s):  
Vision System ◽  
High Viscosity ◽  
Adhesive Properties ◽  
Tracking Errors ◽  
Filling System ◽  
Control Framework ◽  
Control Scheme ◽  
Filling Method ◽  
And Control ◽  
Vision Feedback

In this article, a novel vision-based automatic liquid filling control system is proposed. The method can be used for many high-viscosity liquid filling applications, especially for packaging of fuel tank sealant in the aerospace industry. Due to the high viscosity and highly adhesive properties, filling and packaging this type of liquid can have challenges in terms of time and cost. A new control scheme based on motion synchronization with vision feedback is proposed in this article, which enables a new filling method for this type of liquid. The method can effectively resolve the challenges and enable an efficient filling process. Considering the time delay caused by image processing of the vision system, the Smith delay compensator is employed with an iterative learning control framework to improve the reliability of the feedback control under disturbances. The hardware implementation and experimental results demonstrate the convergence of tracking errors over the learning iterations, proving the effectiveness of the proposed control algorithm.

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