NON-LINEAR MODEL PREDICTIVE CONTROL STRATEGIES FOR PROCESS PLANTS USING SOFT COMPUTING APPROACHES

The developments of advanced non-linear control strategies have attracted a considerable research interests over the past decades especially in process control. Rather than an absolute reliance on mathematical models of process plants which often brings discrepancies especially owing to design errors and equipment degradation, non-linear models are however required because they provide improved prediction capabilities but they are very difficult to derive. In addition, the derivation of the global optimal solution gets more difficult especially when multivariable and non-linear systems are involved. Hence, this research investigates soft computing techniques for the implementation of a novel real time constrained non-linear model predictive controller (NMPC). The time-frequency localisation characteristics of wavelet neural network (WNN) were utilised for the non-linear models design using system identification approach from experimental data and improve upon the conventional artificial neural network (ANN) which is prone to low convergence rate and the difficulties in locating the global minimum point during training process. Salient features of particle swarm optimisation and a genetic algorithm (GA) were combined to optimise the network weights. Real time optimisation occurring at every sampling instant is achieved using a GA to deliver results both in simulations and real time implementation on coupled tank systems with further extension to a complex quadruple tank process in simulations. The results show the superiority of the novel WNN-NMPC approach in terms of the average controller energy and mean squared error over the conventional ANN-NMPC strategies and PID control strategy for both SISO and MIMO systemsPetroleum Training Development Fun

Switching control systems and their design automation via genetic algorithms

The objective of this work is to provide a simple and effective nonlinear controller. Our strategy involves switching the underlying strategies in order to maintain a robust control. If a disturbance moves the system outside the region of stability or the domain of attraction, it will be guided back onto the desired course by the application of a different control strategy. In the context of switching control, the common types of controller present in the literature are based either on fuzzy logic or sliding mode. Both of them are easy to implement and provide efficient control for non-linear systems, their actions being based on the observed input/output behaviour of the system. In the field of fuzzy logic control (FLC) using error feedback variables there are two main problems. The first is the poor transient response (jerking) encountered by the conventional 2-dimensional rule-base fuzzy PI controller. Secondly, conventional 3-D rule-base fuzzy PID control design is both computationally intensive and suffers from prolonged design times caused by a large dimensional rule-base. The size of the rule base will increase exponentially with the increase of the number of fuzzy sets used for each input decision variable. Hence, a reduced rule-base is needed for the 3-term fuzzy controller. In this thesis a direct implementation method is developed that allows the size of the rule-base to be reduced exponentially without losing the features of the PID structure. This direct implementation method, when applied to the reduced rule-base fuzzy PI controller, gives a good transient response with no jerking

Simplified Environment Control System For Prototype Vertical Farm

Farming is one of the fundamental inventions that promoted the flourishing or human species on planet earth. Since many centuries have past, this fundamental invention namely the conventional way of it has become reverse which is promoting extinction. Conventional farming has led to many unsustainable acts such as deforestation, heavy use of pesticides and insecticides, feces as fertilizers, open burning and creation of cities. These acts in turn backfired resulting in global warming, extreme climate change (drought, flood, hurricanes etc), exponential rise in human population resulting in consumption of resources over the replenishing rate, pollution of water sources, diseases spreading such as typhoid and cholera. The very main reason such occurrence is because of direct interaction of farming with nature. Farming is not a natural behavior of an ecosystem (Despommier, 2009). It is a creation of human beings to increase its survival rate. Hence in a simple mathematical equation, if we remove farming from nature and put it in a closed system which resembles the nature ecosystem, we can remove the potential unsustainable acts of farming. The concept of vertical farming is to remove the factor of farming from nature to undo the bad deeds before it is too late. This simplistic solution might seem like a dreamer’s solution and appear to be impractical due to its high costs. To prove that argument wrong, this research will show the latest breakthrough in the agriculture industry in growing plants in a building and too how far is a vertical farm concept farfetched? Thus, this research will be proposing to construct simplified miniature 3 storeys vertical farm system that uses hydroponic system and govern by an expert system using the methodology proposed by CLAE

Modified PSO based PID Sliding Mode Control using Improved Reaching Law for Nonlinear systems

In this paper, a new model based nonlinear control technique, called PID (Proportional-Integral-Derivative) type sliding surface based sliding mode control is designed using improved reaching law. To improve the performance of the second order nonlinear differential equations with unknown parameters modified particle swarm intelligent optimization (MPSO) is used for the optimized parameters. This paper throws light on the sliding surface design, on the proposed power rate exponential reaching law, parameters optimization using modified particle swarm optimization and highlights the important features of adding an integral term in the sliding mode such as robustness and higher convergence, through extensive mathematical modeling. Siding mode control law is derived using Lyapunov stability approach and its asymptotic stability is proved mathematically and simulations showing its validity. MPSO PID-type Sliding mode control will stabilize the highly nonlinear systems, will compensate disturbances and uncertainty and reduces tracking errors. Simulations and experimental application is done on the non-linear systems and are presented to make a quantitative comparison.Comment: arXiv admin note: substantial text overlap with arXiv:2207.1112

Design of Sliding Mode PID Controller with Improved reaching laws for Nonlinear Systems

In this thesis, advanced design technique in sliding mode control (SMC) is presented with focus on PID (Proportional-Integral-Derivative) type Sliding surfaces based Sliding mode control with improved power rate exponential reaching law for Non-linear systems using Modified Particle Swarm Optimization (MPSO). To handle large non-linearities directly, sliding mode controller based on PID-type sliding surface has been designed in this work, where Integral term ensures fast finite convergence time. The controller parameter for various modified structures can be estimated using Modified PSO, which is used as an offline optimization technique. Various reaching law were implemented leading to the proposed improved exponential power rate reaching law, which also improves the finite convergence time. To implement the proposed algorithm, nonlinear mathematical model has to be decrypted without linearizing, and used for the simulation purposes. Their performance is studied using simulations to prove the proposed behavior. The problem of chattering has been overcome by using boundary method and also second order sliding mode method. PI-type sliding surface based second order sliding mode controller with PD surface based SMC compensation is also proposed and implemented. The proposed algorithms have been analyzed using Lyapunov stability criteria. The robustness of the method is provided using simulation results including disturbance and 10% variation in system parameters. Finally process control based hardware is implemented (conical tank system)

Using traditional modelling approaches for a MBR system to investigate alternate approaches based on system identification procedures for improved design and control of a wastewater treatment process.

The specific research work described in this thesis forms part of a much larger research project that was funded by the Technology Programme of the UK Government. This larger project considered improving the design and efficiency of membrane bioreactor (MBR) plant by using modelling, simulation and laboratory methods. This research work uses phenomenological mechanistic models based on MBR filtration and biochemical processes to measure the effectiveness of alternative behavioural models based upon input-output system identification methods. Both model types are calibrated and validated using similar plant layouts and data sets derived for this purpose. Results prove that although both approaches have their advantages, they also have specific disadvantages as well. In conclusion, the MBR plant designer and/or operator who wishes to use good quality, calibrated models to gain a better understanding of their process, should carefully consider which model type is selected based upon on what their initial modelling objectives are (e.g. using either a physically mechanistic model or an input-output behaviourial model). Each situation usually proves unique. In this regard, this research work creates a "Model Conceptualisation Procedure" for a typical MBR which can be used by future researchers as a theoretical framework which underpins any newly created model type. There has been insufficient work completed to date on using a times series input-output approach in the model development of a wastewater treatment plant, so only general conclusions can be made from this research work. However, it can be stated that this novel approach seems to be applicable for a membrane filtration model if care it taken to select appropriate input-output model structures, such as those suggested in the "Model Conceptualisation Procedure". In the case of the development of a MBR biological model, it is thought that a conventional Activated Sludge model produced by the IWA could be coupled to a input-output model structure as suggested by this report to give a hybrid model structure that may have the advantages of both model types. Further research work is needed in this area. Future work that should follow on from this research study should focus on whether these input-output models could be used for predictive control purposes, whether an integrated model could be created, and whether a benchmark could be created for the three main MBR configurations.Technology Programme of the TSB (Technology Strategy Board) TP/3/DSM/6/I/1512

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Linearization and analysis of level as well as thermal process using labview

The processes encountered in the real world are usually multiple input multiple output (MIMO) systems. Systems with more than one input and/or more than one output are called MIMO system. The MIMO system can either be interacting or non-interacting. If one output is affected by only one input, then it is called non-interacting system, otherwise it is called interacting system. The control of interacting system is more complex than the control of non-interacting system. The output of MIMO system can either be linear or non-linear. In process industries,the control of level, temperature,pressure and flow are important in many process applications.In this work, the interacting non -linear MIMO systems (i.e. level process and thermal process) are discussed. The process industries require liquids to be pumped as well as stored in tanks and then pumped to another tank. Most of the time the liquid will be processed by chemical or mixing treatment in the tanks, but the level and temperature of the liquid in tank to be controlled at some desired value and the flow between tanks must be regulated.The interactions existing between loops make the process more difficult to design PI/PID controllers for MIMO processes than that for single input single output (SISO) ones and have attracted attention of many researcher in recent years.In case of level process, the level of liquid in the tank is controlled according to the input flow into the tank. Two input two output (TITO) process and four input four outputs (FIFO) process are described in the thesis work. The aim of the process is to keep the liquid levels in the tanks at the desired values. The output of the level process is non-linear and it is converted into the linear form by using Taylor series method. By using Taylor series method in the non-linear equation, the converted linear equation for the MIMO process is obtained. The objective of the thermal process is to cool a hot process liquid.The dynamic behaviour of a thermal process is understood by analysing the features of the solutions of the mathematical models. The mathematical model of the thermal process is obtained from the energy balance equation. The nonlinear equation is linearized by using Taylor series. The responses of the higher-order thermal process (3 x 2 and 3 x 3) are obtained and analysed. Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW) is used to communicate with hardware such as data acquisition, instrument control and industrial automation. Hence LabVIEW is used to simulate the MIMO system

Development of FPGA based Standalone Tunable Fuzzy Logic Controllers

Soft computing techniques differ from conventional (hard) computing, in that unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind and its ability to address day-to-day problems. The principal constituents of Soft Computing (SC) are Fuzzy Logic (FL), Evolutionary Computation (EC), Machine Learning (ML) and Artificial Neural Networks (ANNs). This thesis presents a generic hardware architecture for type-I and type-II standalone tunable Fuzzy Logic Controllers (FLCs) in Field Programmable Gate Array (FPGA). The designed FLC system can be remotely configured or tuned according to expert operated knowledge and deployed in different applications to replace traditional Proportional Integral Derivative (PID) controllers. This re-configurability is added as a feature to existing FLCs in literature. The FLC parameters which are needed for tuning purpose are mainly input range, output range, number of inputs, number of outputs, the parameters of the membership functions like slope and center points, and an If-Else rule base for the fuzzy inference process. Online tuning enables users to change these FLC parameters in real-time and eliminate repeated hardware programming whenever there is a need to change. Realization of these systems in real-time is difficult as the computational complexity increases exponentially with an increase in the number of inputs. Hence, the challenge lies in reducing the rule base significantly such that the inference time and the throughput time is perceivable for real-time applications. To achieve these objectives, Modified Rule Active 2 Overlap Membership Function (MRA2-OMF), Modified Rule Active 3 Overlap Membership Function (MRA3-OMF), Modified Rule Active 4 Overlap Membership Function (MRA4-OMF), and Genetic Algorithm (GA) base rule optimization methods are proposed and implemented. These methods reduce the effective rules without compromising system accuracy and improve the cycle time in terms of Fuzzy Logic Inferences Per Second (FLIPS). In the proposed system architecture, the FLC is segmented into three independent modules, fuzzifier, inference engine with rule base, and defuzzifier. Fuzzy systems employ fuzzifier to convert the real world crisp input into the fuzzy output. In type 2 fuzzy systems there are two fuzzifications happen simultaneously from upper and lower membership functions (UMF and LMF) with subtractions and divisions. Non-restoring, very high radix, and newton raphson approximation are most widely used division algorithms in hardware implementations. However, these prevalent methods have a cost of more latency. In order to overcome this problem, a successive approximation division algorithm based type 2 fuzzifier is introduced. It has been observed that successive approximation based fuzzifier computation is faster than the other type 2 fuzzifier. A hardware-software co-design is established on Virtex 5 LX110T FPGA board. The MATLAB Graphical User Interface (GUI) acquires the fuzzy (type 1 or type 2) parameters from users and a Universal Asynchronous Receiver/Transmitter (UART) is dedicated to data communication between the hardware and the fuzzy toolbox. This GUI is provided to initiate control, input, rule transfer, and then to observe the crisp output on the computer. A proposed method which can support canonical fuzzy IF-THEN rules, which includes special cases of the fuzzy rule base is included in Digital Fuzzy Logic Controller (DFLC) architecture. For this purpose, a mealy state machine is incorporated into the design. The proposed FLCs are implemented on Xilinx Virtex-5 LX110T. DFLC peripheral integration with Micro-Blaze (MB) processor through Processor Logic Bus (PLB) is established for Intellectual Property (IP) core validation. The performance of the proposed systems are compared to Fuzzy Toolbox of MATLAB. Analysis of these designs is carried out by using Hardware-In-Loop (HIL) test to control various plant models in MATLAB/Simulink environments