10 research outputs found
Neural Model-Based Advanced Control of Chylla-Haase Reactor
The objective of this thesis is to develop advanced control method and to design advanced control system for the polymerization reactor (Chylla-Haase) to maintain the high accurate reactor temperature. The first stage of this research start with the development of mathematical model of the process. The sub-models for monomer concentration, polymerization rate, reactor temperature and jacket outlet/inlet temperature are developed and implemented in Matlab/Simulink.
Four conventional control methods were applied to the reactor: a Proportional –Integral-Derivative (PID), Cascade control (CCs), Linear-Quadratic-Regulator (LQR), and Linear model predictive control (LMPC). The simulation results show that the PID controller is unable to perform satisfactorily due to the change of physical properties unless constant re-tuning takes place. Also, Cascade Control the most common control method used in such processes cannot guarantee a robust performance under varying disturbance and system uncertainty. In addition, LQR and linear MPC methods lead to better results compared with the previous two methods. But it is still under an assumption of the linearized plant.
Three advanced neural network based control schemes are also proposed in this thesis: radial basis function RBF neural network inverse model based feedforward-feedback control scheme, RBF based model predictive control and multi-layer perception (MLP) based model predictive control. The major objective of these control schemes is to maintain the reactor temperature within its tolerance range under disturbances and system uncertainty. Satisfactory control performance in terms of effective regulation and robustness to disturbance have been achieved.
In the feedforward-feedback control scheme, a neural network model is used to predict reactor temperature. Then, a neural network inverse model is used to estimate the valve position of the reactor, the manipulated variable. This method can identify the
controlled system with the RBF neural network identifier. A PID controller is used in the feedback control to regulate the actual temperature by compensating the neural network inverse model output. Simulation results show that the proposed control has strong adaptability, robustness and satisfactory control performance. These advanced methods achieved the much improved control performance compared with conventional control schemes.
The main contribution of this research lies in the following aspects. The MPC theory is realised to control Chylla-Haase polymerization reactor. Two adaptive reactor models including the RBF network model and MLP model are developed to predict the multiple-step-ahead values of the reactor output. Their modelling ability is compared with that of the models with fixed parameters and proven to be better. The RBF neural network and the MLP is trained by the recursive Least Squares (RLS) algorithm and is used to model parameter uncertainty in nonlinear dynamics of the Chylla-Haase reactor. The predictive control strategy based on the RBF neural network is applied to achieve set-point tracking of the reactor output against disturbances. The result shows that the RBF based model predictive control gives reliable result in the presence of some disturbances and keeps the reactor temperature within a tight tolerance range around the specified reaction temperature.
Moreover, RBF neural network based model predictive control strategy has also been used to reduce the batch time in order to shorten the reaction period. RBF neural network is considered as a prediction model for control purpose which is based to minimize a cost function in order to determine an optimal sequence of control moves. The result shows that the RBF based model predictive control gives reliable result in the presence of variation of monomer and presence of some disturbances for keeping the reactor temperature within a tight tolerance range around the specified reaction temperature without harming the quality of the temperature control
Adaptive sliding mode attitude control of 2-degrees-of-freedom helicopter system with actuator saturation and disturbances
The modelling uncertainties, external disturbance and actuator saturation issues will degrade the performance and even the safety of flight. To improve control performance, this study proposes an adaptive U-model based double sliding control (UDSMC) algorithm combined with a radial basis function neural network (RBFNN) for a nonlinear two-degrees-of-freedom (2-DOF) helicopter system. Firstly, the adaptive RBFNN is designed to approximate the system dynamics with unknown uncertainties. Furthermore, two adaptive laws are designed to deal with unknown external disturbances and actuator saturation errors. The global stability of the proposed helicopter control system is rigorously guaranteed by the Lyapunov stability analysis, realizing precise attitude tracking control. Finally, the comparative experiments with conventional SMC and adaptive SMC algorithms conducted on the Quanser Aero2 platform demonstrate the effectiveness and feasibility of the proposed 2-DOF helicopter control algorithm
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Arquiteturas de hardware para aceleração de algoritmos de controle preditivo não-linear
Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2018.O Controle Preditivo Baseado em Modelos (MPC) é uma técnica avançada de controle que vem
ganhando espaço tanto na academia quanto na indústria ao longo das últimas décadas. O fato de
incorporar restrições em sua lei de controle e de poder ser aplicada tanto para sistemas lineares
simples quanto para sistemas não-lineares complexos com múltiplas entradas e múltiplas saídas
tornam seu emprego bastante atraente. Porém, seu alto custo computacional muitas vezes impede
sua aplicação a sistemas com dinâmicas rápidas, principalmente a sistemas não-lineares embarcados
onde há restrições computacionais e de consumo de energia. Baseado nisso, este trabalho
se propõe a desenvolver algoritmos e arquiteturas em hardware capazes de viabilizar a aplicação
do Controle Preditivo Não-Linear (NMPC) para sistemas embarcados.
Duas abordagens são desenvolvidas ao longo do trabalho. A primeira aplica técnicas de aprendizado
de máquina utilizando Redes Neurais Artificiais (RNAs) e Máquinas de Vetor de Suporte
(SVMs) para criar soluções que aproximam o comportamento do NMPC em hardware. Neste
caso, técnicas para o treinamento das RNAs e SVMs são exploradas com o intuito de generalizar
uma solução capaz de lidar com uma ampla faixa de referências de controle. Em seguida, arquiteturas
de hardware em ponto-flutuante para a implementação de RNAs do tipo RBF (Radial Basis
Functions) e SVMs são desenvolvidas juntamente com configurador automático capaz de gerar os
códigos VHDL (VHSIC Hardware Description Language) das respectivas arquiteturas baseado
nos resultados de treinamento e sua topologia. As arquiteturas resultantes são testadas em um
FPGA (Field-Programmable Gate Array) de baixo custo e são capazes de computar soluções em
menos de 1 s.
Na segunda abordagem, o algoritmo heurístico de Otimização por Enxame de Partículas
(PSO), é estudado e adaptado para etapa de busca da sequência de controle ótima do NMPC.
Dentre as modificações estão incluídas a adição de funções de penalização para obedecer às
restrições de estados do sistema, o aprimoramento da técnica KPSO (Knowledge-Based PSO),
denominada KPSO+SS, onde resultados de períodos de soluções de períodos amostragem anteriores
são combinados com informações sobre o sinal de controle em estado estacionário e seus
valores máximos e mínimos para agilizar a busca pela solução ótima. Mais uma vez, arquiteturas
de hardware em ponto-flutuante são desenvolvidas para viabilizar a aplicação do controlador
NMPC-PSO a sistemas embarcados. Um gerador de códigos da solução NMPC-PSO é proposto
para permitir a aplicação da mesma arquitetura a outros sistemas. Em seguida, a solução é testada
para o procedimento de swing-up do pêndulo invertido utilizando uma plataforma hardware-inthe-
loop (HIL) e apresentou bom desempenho em tempo-real calculando a solução em menos de
3 ms. Finalmente, a solução NMPC-PSO é validada em um sistema de pêndulos gêmeos e outro
sistema de controle de atitudes de um satélite.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) e Decanato de Pesquisa e Inovação -(DPI/ UnB).Model-based Predictive Control (MPC) is an advanced control technique that has been gaining
adoption in industry and the academy along the last few decades. Its ability to incorporate system
constraints in the control law and be applied from simple linear systems up to more complex
nonlinear systems with multiple inputs and outputs attracts its usage. However, the high computational
cost associated with this technique often hinders its use, especially in embedded nonlinear
systems with fast dynamics with computational and restrictions. Based on these facts, this work
aims to study and develop algorithms and hardware architectures that can enable the application
of Nonlinear Model Predictive Control (NMPC) on embedded systems.
Two approaches are developed throughout this work. The first one applies machine learning
techniques using Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) to
create solutions that approximate the NMPC behavior in hardware. In this case, ANN and SVM
training techniques are explored with the aim to generalize the control solution and work on a
large range of reference control inputs. Next, floating-point hardware architectures to implement
Radial Basis Function ANNs and SVM solutions are developed along with an automatic
architectural configuration too, capable of generating the VHDL (VHSIC Hardware Description
Language) codes based on the training results and its topology. Resulting architectures are tested
on a low-cost FPGA (Field-Programmable Gate Array) and are capable of computing the solution
in under 1 s.
In a second approach, the Particle Swarm Optimization (PSO), which is a heuristic algorithm,
is studied and adapted to perform the optimal control sequence search phase of the NMPC.
Among the main optimizations performed are the addition of penalty functions to address the controlled
system state constraints, an improved KPSO (Knowledge-Based PSO) technique named
KPSO+SS, where results from previous sampling periods are combined with steady-state control
information to speed-up the optimal solution search. Hardware architectures with floating-point
arithmetic to enable the application of the NMPC-PSO solution on embedded systems are developed.
Once again, a hardware description configuration tool is created to allow the architecture
to be applied to multiple systems. Then, the solution is applied to a real-time inverted pendulum
swing-up procedure tested on a hardware-in-the-loop (HIL) platform. The experiment yielding
good performance and control results and was able to compute the solutions in under 3 ms. Finally,
the NMPC-PSO solution is further validated performing a swing-up procedure on a Twin
Pendulum system and then on a satellite control platform, a system with multiple inputs and
output
メタヒューリスティクスおよび機械学習を用いた建物・地域エネルギーシステムの最適化に関する研究
学位の種別: 課程博士審査委員会委員 : (主査)東京大学教授 大岡 龍三, 東京大学教授 加藤 信介, 東京大学教授 赤司 泰義, 東京大学教授 合原 一幸, 東京大学講師 菊本 英紀University of Tokyo(東京大学
Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm
Abstract— Online transportation has become a basic
requirement of the general public in support of all activities to go
to work, school or vacation to the sights. Public transportation
services compete to provide the best service so that consumers
feel comfortable using the services offered, so that all activities
are noticed, one of them is the search for the shortest route in
picking the buyer or delivering to the destination. Node
Combination method can minimize memory usage and this
methode is more optimal when compared to A* and Ant Colony
in the shortest route search like Dijkstra algorithm, but can’t
store the history node that has been passed. Therefore, using
node combination algorithm is very good in searching the
shortest distance is not the shortest route. This paper is
structured to modify the node combination algorithm to solve the
problem of finding the shortest route at the dynamic location
obtained from the transport fleet by displaying the nodes that
have the shortest distance and will be implemented in the
geographic information system in the form of map to facilitate
the use of the system.
Keywords— Shortest Path, Algorithm Dijkstra, Node
Combination, Dynamic Location (key words