[...] In practice, conventional controllers were used to control the system however their
parameters are empirically adjusted. Besides, the operation of these controllers relies on the
measurements provided by sensors located inside and near the greenhouse. If the
information provided by one or several of these sensors is erroneous, the controllers will not operate properly. Similarly, failure of one or several of the actuators to function
properly will impair the greenhouse operation. Therefore, an automatic diagnosis system of
failures in greenhouses is proposed. The diagnosis system is based on deviations observed
between measurements performed in the system and the predictions of a model of the
failure-free system. This comparison is done through a bank of fuzzy observers, where each
observer becomes active to a specific failure signature and inactive to the other failures.
Neural networks are used to develop a model for the failure-free greenhouse.
The main objective of this thesis is to explore and develop intelligent control schemes
for adjusting the climate inside a greenhouse. The thesis employs the conventional Pseudo-
Derivative Feedback (PDF) Controller. It develops the fuzzy PDF controller (FPDF). The
thesis also, develops two genetic algorithm (GA) based climatic control schemes, one is
genetic PDF (GPDF) and the other is genetic FPDF (GFPDF). The former uses GA to
adjust the gains of the Pseudo-Derivative Feedback Controller (GPDF) and the later uses
genetic algorithm to optimize the FPDF controller parameters (i.e., scale factors and/or
parameters of the membership functions). Finally, the thesis develops a fuzzy neural fault
detection and isolation system (FNFDIS), in which a bank of fuzzy observers are designed
to detect faults that may occur in the greenhouse end items (e.g.., sensors and actuators).
Simulation experiments are performed to test the soundness and capabilities of the
developed control schemes for controlling the greenhouse climate. The proposed schemes
are tested through two experiments, setpoint tracking test and regulatory control test. Also,
the proposed diagnostic system was tested through four experiments. Compared with the
results obtained using the conventional controllers, best results have been achieved using
the proposed control schemes.
Table of Contents
1. Introduction
1.1. Preliminaries
1.2. The greenhouse climate: characteristics and determinism
1.3. Research objectives
1.4. Outline of the thesis
2. Background to fuzzy logic, Neural Networks, Optimizers, Greenhouses and Fault Detection/Isolation Systems
2.1. Preliminaries
2.2. Fuzzy Logic Systems and their Applications
2.2.1. Fuzzy sets and fuzzy logic
2.2.2. Architecture of fuzzy logic systems
2.2.2.1. Fuzzification interface
2.2.2.2. Knowledge base
2.2.2.3. Fuzzy approximate reasoning
2.2.2.4. Defuzzification interface
2.2.3. Fuzzy logic systems in control
2.2.3.1. Static fuzzy logic systems
2.2.3.2. Adaptive fuzzy logic systems
2.2.3.3. Features and applications of fuzzy logic systems
2.3. Adaptive control
2.4. Feedforward neural networks
2.4.1. Multi-layer perceptron
2.4.2. Learning in Neural Networks
2.4.2.1. Supervised learning
2.4.2.2. Reinforcement learning
2.4.2.3. Unsupervised learning
2.4.3. Applications of feedforward neural networks
2.5. Modern Optimization Techniques
2.5.1. Genetic algorithms
2.5.2. Principal attractions of genetic algorithms
2.5.3. Construction of Genetic Algorithms
2.5.3.1. Solution representation
2.5.3.2. Data structure
2.5.3.3. Reproduction
2.5.3.4. Crossover
2.5.3.5. Mutation
2.6. Greenhouses
2.7. Fault detection and isolation systems
2.8. Summary
3. Mathematical models of Greenhouse
3.1. Preliminaries
3.2. Hierarchical decomposition of greenhouse climate management
3.3. Greenhouse crop production process
3.4. Greenhouse dynamical model
3.5. Control of the greenhouse ventilation model
3.5.1. Control model
3.5.2. Feedback/feedforward linearization and decoupling
3.6. Modeling with neural networks
3.6.1. Multi-layer perceptron
3.6.2. Radial basis function networks
3.6.3. Including prior knowledge: hybrid modeling
3.7. Summary
4. Greenhouse climate controls
4.1. Preliminaries
4.2. Pseudo-derivative feedback controller
4.2.1. Controller structure
4.2.2. Optimization approaches
4.3. Simulation experiments
4.3.1. Setpoint tracking test
4.3.2. Disturbance rejection test
4.4. Fuzzy logic control
4.4.1. Controller structure
4.4.2. Fuzzy PI controller
4.4.3. Fuzzy PDF controller
4.4.4. GA-based Fuzzy controller
4.5. Simulation experiments
4.5.1. Setpoint tracking test
4.5.2. Disturbance rejection test
4.6. Summary
5. Fault diagnosis and its application on greenhouses
5.1. Preliminaries
5.2. Robust detection and isolation
5.2.1. Residual generation
5.2.2. Residual interpretation
5.3. Adopted approach and limitations
5.4. Greenhouse climate modeling
5.5. Fuzzy neural failure detection and isolation
5.6. Simulation results
5.7. Summary
6. Conclusions and Further Work
6.1. Preliminaries
6.2. Contributions and conclusions
6.3. Future work
Research Objectives and Scope
The primary objective of this thesis is to explore and develop intelligent control schemes to maintain optimal climate setpoints within greenhouses, while effectively managing the continuously changing environmental conditions. The research investigates the use of conventional Pseudo-Derivative Feedback (PDF) controllers and enhances them through fuzzy logic and genetic algorithm (GA) optimization to improve load handling and robustness. Furthermore, the work addresses the critical need for system reliability by proposing a hybrid fuzzy neural fault detection and isolation (FNFDI) system to identify and isolate sensor and actuator failures, thereby minimizing crop production losses.
- Development of intelligent control schemes including Fuzzy PDF (FPDF) and Genetic Algorithm-based FPDF (GFPDF).
- Mathematical modeling of greenhouse climate dynamics, accounting for non-linear interactions and plant responses.
- Tuning and optimization of controller parameters using binary and real-valued genetic algorithms.
- Implementation of a fuzzy neural network-based fault detection and isolation scheme for greenhouse automation.
- Validation through simulation experiments covering setpoint tracking and regulatory control under various external disturbances and uncertainties.
Book Excerpt
3.5.2. Feedback-Feedforward linearization and decoupling
There are two methods that can be applied in the present case of greenhouse climate control, the first method was described in [Albright et al., 2001] and the second method was described in details in the reference [Pasgianos et al., 2003].
It is well known that affine nonlinear systems may be globally linearized and decoupled by nonlinear feedback. This is just the scheme of inverse dynamic control. The extension of this scheme to more complex cases, such as the one represented by equation (3.4), is some times feasible, since the disturbance variables of the greenhouse heating-cooling ventilating model can be readily measured. Furthermore, the complexity of such systems may be eased by the fact that the system states changes slowly and some state-dependent parameters (i.e., βT) can be considered constant (i.e., quasi-static system operation). Therefore, in the present case, a combined scheme of feedback with simultaneous feedforward linearization is plausible.
To this end, consider the system (3.4) to be linearized and decoupled, having the form:
x1(t) = -(UA / ρCpV) x1(t) + K_tilde_T u_tilde_T(t)
x2(t) = -( βT / ρV) x2(t) + K_tilde_w u_tilde_w(t)
Summary of Chapters
1. Introduction: Provides an overview of the greenhouse system as a non-linear time-variant process and outlines the research objectives and thesis structure.
2. Background to fuzzy logic, Neural Networks, Optimizers, Greenhouses and Fault Detection/Isolation Systems: Reviews essential theoretical foundations including fuzzy logic architecture, adaptive control, neural networks, and modern optimization techniques like genetic algorithms.
3. Mathematical models of Greenhouse: Details the non-linear dynamic models of greenhouse climate and explores hierarchical control strategies and the use of hybrid modeling approaches.
4. Greenhouse climate controls: Proposes and evaluates advanced control schemes including PDF, FPDF, and GA-optimized controllers for precise climate setpoint management.
5. Fault diagnosis and its application on greenhouses: Introduces a hybrid fuzzy neural fault detection and isolation system designed to enhance the safety and operational reliability of greenhouse climate controls.
6. Conclusions and Further Work: Summarizes the primary contributions of the thesis, specifically highlighting the advantages of the proposed GFPDF and fault diagnosis schemes, and suggests future research directions.
Keywords
Greenhouse climate control, Intelligent control systems, Fuzzy logic, Neural networks, Genetic algorithms, Fault detection and isolation, Pseudo-derivative feedback, Adaptive control, Nonlinear system modeling, Setpoint tracking, Disturbance rejection, Optimization, Humidity control, Temperature control, Automation.
Frequently Asked Questions
What is the core focus of this thesis?
The thesis focuses on developing intelligent control algorithms to optimize greenhouse climate management, specifically temperature and humidity, while incorporating robust fault detection mechanisms.
Which specific control strategies are explored?
The research explores Pseudo-Derivative Feedback (PDF) control, Fuzzy PDF controllers, and GA-optimized versions of these schemes (GPDF, GFPDF) to handle non-linear system dynamics.
What is the main research question or goal?
The primary goal is to develop control schemes capable of maintaining optimal climate conditions despite continuously changing external disturbances and to isolate sensor/actuator faults for improved system reliability.
What scientific methods are utilized in the work?
The work employs non-linear system modeling, fuzzy set theory, feedforward neural networks, genetic algorithms for parameter optimization, and observer-based failure diagnosis.
What is the focus of the main body of the work?
The main body treats the mathematical modeling of the greenhouse environment, the implementation of various advanced control laws, and the design of a hybrid fuzzy neural fault detection system.
What are the primary keywords characterizing the research?
The research is characterized by terms like greenhouse climate control, fuzzy logic, genetic algorithms, neural networks, and fault detection and isolation.
How does the thesis handle the coupling between temperature and humidity?
The thesis utilizes feedback/feedforward linearization and decoupling methods to manage the non-linear interaction between temperature and humidity control loops.
What role do Genetic Algorithms play in the proposed controllers?
Genetic Algorithms are used to automatically tune and optimize the gains and membership function parameters of the controllers, ensuring superior performance in varying weather conditions.
- Quote paper
- Ibrahim A. Hameed (Author), 2005, Environmental Control for Plants using Intelligent Control Systems, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/190479
