Greenhouse climate fuzzy adaptive control considering energy saving

This paper proposes a fuzzy adaptive control approach to solve greenhouse climate control problem. The aim is to ensure the controlled environmental variables to track their desired trajectories so as to create a favorable environment for crop growth. In this method, a feedback linearization technique is first introduced to derive the control laws of heating, fogging and CO2 injection, then to compensate for the saturation of the actuators, a fuzzy logic system (FLS) is used to approximate the differences between controller outputs and actuator outputs due to actuator constraints. A robust control term is introduced to eliminate the impact of external disturbances and model uncertainty, and finally, Lyapunov stability analysis is performed to guarantee the convergence of the closed-loop system. Taking into account the fact that the crop is usually insensitive to the change of the environment inside the greenhouse during a short time interval, a certain amount of tracking error of the environmental variables is usually acceptable, which means that the environmental variables need only be driven into the corresponding target intervals. In this case, an energy-saving management mechanism is designed to reduce the energy consumption as much as possible. The simulation results illustrate the effectiveness of the proposed control scheme.

     

Multiobjective hierarchical control architecture for greenhouse crop growth

The problem of determining the trajectories to control greenhouse crop growth has traditionally been solved by using constrained optimization or applying artificial intelligence techniques. The economic profit has been used as the main criterion in most research on optimization to obtain adequate climatic control setpoints for the crop growth. This paper addresses the problem of greenhouse crop growth through a hierarchical control architecture governed by a high-level multiobjective optimization approach, where the solution to this problem is to find reference trajectories for diurnal and nocturnal temperatures (climate-related setpoints) and electrical conductivity (fertirrigation-related setpoints). The objectives are to maximize profit, fruit quality, and water-use efficiency, these being currently fostered by international rules. Illustrative results selected from those obtained in an industrial greenhouse during the last eight years are shown and described.     

Hamilton-Jacobi-Bellman formalism for optimal climate control of greenhouse crop

The paper describes a simplified dynamic model of a greenhouse tomato crop, and the optimal control problem related to the seasonal benefit of the grower. A HJB formalism is used and the explicit form of the Krotov-Bellman function is obtained for different growth stages. Simulation results are shown.     

Robust MPC for systems with output feedback and input saturation

In this work, it is proposed an MPC control algorithm with proved robust stability for systems with model uncertainty and output feedback. It is assumed that the operating strategy is such that system inputs may become saturated at transient or steady state. The developed strategy aims at the case in which the controller performs in the output-tracking scheme following an optimal set point that is provided by an upper optimization layer of the plant control structure. In this case, the optimal operating point usually lies at the boundary of the region where the input is defined. Assuming that the system remains stabilizable in the presence of input saturation, the design of the robust controller is performed off-line and an on-line implementation strategy is proposed. At each sampling step, a sub optimal control law is obtained by combining control configurations that correspond to particular subsets of available manipulated inputs. Stability of the closed-loop system is forced by considering in the off-line step of the controller design, a state contracting restriction for the closed-loop system. To produce an offset free controller and to attend the case of unknown steady state, the method is developed for a state-space model in the incremental form. The method is illustrated with simulation examples extracted from the process industry.     

The significance of crop co-states for receding horizon optimal control of greenhouse climate

While a tomato crop grows on the time-scale of weeks, the greenhouse climate changes on a time-scale of minutes. The economic optimal control problem of producing good quality crops against minimum input of resources is tackled by a two time-scale decomposition. First, the sub-problem associated to the slow crop evolution is solved off-line, leading to a seasonal pattern for the co-states of the amount of assimilates produced by photosynthesis, and the fruit and leaf weights. These co-states can be interpreted as the marginal prices of a unit of assimilate, leaf and fruit. Next, they are used in the goal function of an on-line receding horizon control (RHOC) of the greenhouse climate, thus balancing costs of heating and CO₂-dosage against predicted benefits from harvesting, while profiting as much as possible from the available solar radiation. Simulations using the time-varying co-states are compared to experimental results obtained with fixed co-states. It appears that the on-line control is sensitive to the time evolution of the co-states, suggesting that it is advantageous to repeat the seasonal optimisation from time to time to adjust the co-states to the past weather and realised crop state.     

智能控制的模拟自然环境研究

创建一种合理的温室智能控制拓扑结构,应用智能控制技术和先进的算法,根据作物生长的数据库,模拟自然环境,实现对温室环境类自然的最优控制,为实现应用于植物生长箱和人工气候室的环境控制提供了可行性依据。     

On the spectrum-determined growth condition of a vibration cablewith a tip mass

We show that the spectrum-determined growth condition holds for the closed-loop system of a vibration cable with a tip mass and linear boundary feedback control. The optimal decay rate of the energy for a case left unsolved previously is determined, and the asymptotic expansion of the associated semigroup is obtained. As a consequence of the approach, we show, in a different point of view, the lack of uniform exponential stability of the system with only boundary velocity feedback control     

Discrete-time optimal fuzzy controller design: global conceptapproach

Proposes a systematic and theoretically sound way to design a global optimal discrete-time fuzzy controller to control and stabilize a nonlinear discrete-time fuzzy system with finite or infinite horizon (time). A linear-like global system representation of a discrete-time fuzzy system is first proposed by viewing such a system in a global concept and unifying the individual matrices into synthetic matrices. Then, based on this kind of system representation, a discrete-time optimal fuzzy control law which can achieve a global minimum effect is developed theoretically. A nonlinear two-point boundary-value-problem (TPBVP) is derived as a necessary and sufficient condition for the nonlinear quadratic optimal control problem. To simplify the computation, a multi-stage decomposition of the optimization scheme is proposed, and then a segmental recursive Riccati-like equation is derived. Moreover, in the case of time-invariant fuzzy systems, we show that the optimal controller can be obtained by just solving discrete-time algebraic Riccati-like equations. Based on this, several fascinating characteristics of the resultant closed-loop fuzzy system can easily be elicited. The stability of the closed-loop fuzzy system can be ensured by the designed optimal fuzzy controller. The optimal closed-loop fuzzy system can not only be guaranteed to be exponentially stable, but also stabilized to any desired degree. Also, the total energy of system output is absolutely finite. Moreover, the resultant closed-loop fuzzy system possesses an infinite gain margin, i.e. its stability is guaranteed no matter how large the feedback gain becomes. An example is given to illustrate the proposed optimal fuzzy controller design approach and to demonstrate the proven stability properties     

基于生长模型的温室虚拟黄瓜构建研究*

在温室内对黄瓜植株生长进行实验观测,以生育期内累积生长度日为参数分别建立黄瓜的形态发生与生理功能模型.采用类封装技术对植株生长单元及器官类进行定义,建立植株拓扑结构与器官形态变化的规则化描述模型,通过信息融合与重构处理构建植株生长的虚拟模型.实验表明该方法在虚拟植株生长方面具有一定的可行性和有效性,为动态掌握和预测适宜...     

农业智慧大屏监控系统的设计与实现

当前智慧温室大棚系统过程数据的综合利用率低,种植经验与果蔬生长过程数据无法实时监控,导致无法更好的控制果蔬的生长过程环境的数据。该文通过智慧大屏的监控系统实现对大棚农作物生长过程环境参数进行实时跟踪,实现温室大棚果蔬的种植环境的过程优化。实验证实,该方法能够有效地提升对大棚作物的监控,对果蔬的种植管理有较好的改进。     

温室环境温度智能控制算法研究

智能控制算法是自动控制系统的关键技术，运用智能控制理论-模糊控制的理论，基于作物生长环境的需求，提出了温室环境温度的模糊控制算法-可调因子多模型模糊控制算法，并对模糊控制器进行了参数优化。在作物生长的三个时区进行了仿真，仿真结果验证了所提出的算法有效性。     

An extremum seeking approach via variable-structure control for fed-batch bioreactors with uncertain growth rate

In this paper, we present an extremum-seeking scheme based on an approach to variable structure control for fed-batch bioreactors. The proposed scheme deals with uncertainty on the specific growth rate without assuming an explicit mathematical expression. The control approach exploits the inhibitory effect of the substrate concentration on the growth rate, in such a manner that the closed-loop system reaches the sliding regime on an optimal switching manifold, which is defined by maximizing biomass production. The control scheme comprises an estimation scheme consisting of a high-gain observer and a discrete gradient estimator which computes the unknown terms. The practical stabilizability for the closed-loop system around an unknown optimal set-point is analyzed. Numerical experiments illustrate the effectiveness of the proposed approach.     

二次型最优离散系统的两个必要条件

本文给出了线性定常二次型最优离散系统的两个必要条件,得到了单输入单输出系统开环特征多项式系数a_i和最优闭环特征多项式系数b_i与权阵Q之间的直接关系,所得结论有益于设计一个具有指定闭环极点的最优控制系统。     

General receding horizon control for linear time-delay systems

A general receding horizon control (RHC), or model predictive control (MPC), for time-delay systems is proposed. The proposed RHC is obtained by minimizing a new cost function that includes two terminal weighting terms, which are closely related to the closed-loop stability. The general solution of the proposed RHC is derived using the generalized Riccati method. Furthermore, an explicit solution is obtained for the case where the horizon length is less than or equal to the delay size. A linear matrix inequality (LMI) condition on the terminal weighting matrices is proposed, under which the optimal cost is guaranteed to be monotonically non-increasing. It is shown that the monotonic condition of the optimal cost guarantees closed-loop stability of the RHC. Simulations demonstrate that the proposed RHC effectively stabilizes time-delay systems.     

Finite horizon quadratic optimal control and a separation principle for Markovian jump linear systems

In this note, we consider the finite-horizon quadratic optimal control problem of discrete-time Markovian jump linear systems driven by a wide sense white noise sequence. We assume that the output variable and the jump parameters are available to the controller. It is desired to design a dynamic Markovian jump controller such that the closed-loop system minimizes the quadratic functional cost of the system over a finite horizon period of time. As in the case with no jumps, we show that an optimal controller can be obtained from two coupled Riccati difference equations, one associated to the optimal control problem when the state variable is available, and the other one associated to the optimal filtering problem. This is a principle of separation for the finite horizon quadratic optimal control problem for discrete-time Markovian jump linear systems. When there is only one mode of operation our results coincide with the traditional separation principle for the linear quadratic Gaussian control of discrete-time linear systems.     

A design method for indirect iterative learning control based on two-dimensional generalized predictive control algorithm

Indirect iterative learning control (ILC) facilitates the application of learning-type control strategies to the repetitive/batch/periodic processes with local feedback control already. Based on the two-dimensional generalized predictive control (2D-GPC) algorithm, a new design method is proposed in this paper for an indirect ILC system which consists of a model predictive control (MPC) in the inner loop and a simple ILC in the outer loop. The major advantage of the proposed design method is realizing an integrated optimization for the parameters of existing feedback controller and design of a simple iterative learning controller, and then ensuring the optimal control performance of the whole system in sense of 2D-GPC. From the analysis of the control law, it is found that the proposed indirect ILC law can be directly obtained from a standard GPC law and the stability and convergence of the closed-loop control system can be analyzed by a simple criterion. It is an applicable and effective solution for the application of ILC scheme to the industry processes, which can be seen clearly from the numerical simulations as well as the comparisons with the other solutions.     

On the open-loop solution of linear stochastic optimal control problems

We consider open-loop solutions of linear stochastic optimal control problems with constraints on control variables and probabilistic constraints on state variables. It is shown that this problem reduces to an equivalent linear deterministic optimal control problem with similar constraints and with a new criterion to minimize. Concavity or convexity is preserved. Hence, the machinery available for solving deterministic optimal control problems can be used to get an open-loop solution of the stochastic problem. The convex case is investigated and a bound on the difference between closed-loop and open-loop optimal costs is given.     

一种新的最优极点配置方法

本文从LQ逆问题着眼提出了一种新的最优极点配置方法,推导了加权矩阵Q和R与开环特征多项式、最优闭环特征多项式之间的关系。只要给定一组期望的闭环极点,即可确定与之对应的加权矩阵Q和R,从而得到一个具有指定极点的最优控制系统。     

Synthesis of sub-optimal feedback controls for a class of distributed parameter systems†

Technique are derived for the synthesis of sub-optimal feedback controls for parabolic and first-order hyperbolic systems. An explicit result for the time-invariant gain of a specified controller is obtained by a least square approximation of the closed-loop control to the optimal open-loop control. If a least square approximation of the state trajectories is used, a parameter search is shown to give the time-invariant gain. A time-varying gain can be obtained by a re-definition of the original optimal control problem, again with the controller functionality specified. The only require-mont in the closed-loop synthesis is that an optimal open-loop solution exists and is computable.     

Parallel Optimal Control for Weakly Coupled Nonlinear Systems Using Successive Galerkin Approximation

This technical note presents a new algorithm for the closed-loop parallel optimal control of weakly coupled nonlinear systems with respect to performance criteria using the successive Galerkin approximation (SGA). By using the weak coupling theory, the optimal control law can be obtained from two reduced-order optimal control problems in parallel, but the resulting problem is difficult to solve for nonlinear systems. In order to overcome the difficulties inherent in the nonlinear optimal control problem, the parallel optimal control laws are constructed in terms of the approximated solutions to two independent Hamilton-Jacobi-Bellman equations using the SGA method. One of the purposes of this note is to design the closed-loop parallel optimal control law for the weakly coupled nonlinear systems using the SGA method. The second is to reduce the computational complexity when the SGA method is applied to the high-order weakly coupled systems.