Energy from organic sources

Elevated environmental awareness and exhaustion of resources are leading to develop substitute fuel from renewable resources that can be environmentally friendly. Bio-diesel fuel is a substitute to petrol base fuels currently being obtained from vegetable oils, animal fat, utilized oils from restaurants etc. Pet animal fats represent large wastes in tanneries. With a high energetically worth, animal fats may make up an energetical source with significant capital opportunities as raw substance (used with high precautionary measure) or also as oils acquired from ester interchange with alcohol (biodiesel) with higher facets being used as fuel at diesel engines

Optimized Neural Networks-PID Controller with Wind Rejection Strategy for a Quad-Rotor

In this paper a full approach of modeling and intelligent control of a four rotor unmanned air vehicle (UAV) known as quad-rotor aircraft is presented. In fact, a PID on-line optimized Neural Networks Approach (PID-NN) is developed to be applied to angular trajectories control of a quad-rotor. Whereas, PID classical controllers are dedicated for the positions, altitude and speed control. The goal of this work is to concept a smart Self-Tuning PID controller, for attitude angles control, based on neural networks able to supervise the quad-rotor for an optimized behavior while tracking a desired trajectory.  Many challenges could arise if the quad-rotor is navigating in hostile environments presenting irregular disturbances in the form of wind modeled and applied to the overall system. The quad-rotor has to quickly perform tasks while ensuring stability and accuracy and must behave rapidly with regards to decision making facing disturbances. This technique offers some advantages over conventional control methods such as PID controller. Simulation results are founded on a comparative study between PID and PID-NN controllers based on wind disturbances. These later are applied with several degrees of strength to test the quad-rotor behavior and stability. These simulation results are satisfactory and have demonstrated the effectiveness of the proposed PD-NN approach. In fact, the proposed controller has relatively smaller errors than the PD controller and has a better capability to reject disturbances. In addition, it has proven to be highly robust and efficient face to turbulences in the form of wind disturbances

Predictive Coding: a Theoretical and Experimental Review

Predictive coding offers a potentially unifying account of cortical function -- postulating that the core function of the brain is to minimize prediction errors with respect to a generative model of the world. The theory is closely related to the Bayesian brain framework and, over the last two decades, has gained substantial influence in the fields of theoretical and cognitive neuroscience. A large body of research has arisen based on both empirically testing improved and extended theoretical and mathematical models of predictive coding, as well as in evaluating their potential biological plausibility for implementation in the brain and the concrete neurophysiological and psychological predictions made by the theory. Despite this enduring popularity, however, no comprehensive review of predictive coding theory, and especially of recent developments in this field, exists. Here, we provide a comprehensive review both of the core mathematical structure and logic of predictive coding, thus complementing recent tutorials in the literature. We also review a wide range of classic and recent work within the framework, ranging from the neurobiologically realistic microcircuits that could implement predictive coding, to the close relationship between predictive coding and the widely-used backpropagation of error algorithm, as well as surveying the close relationships between predictive coding and modern machine learning techniques.Comment: 27/07/21 initial upload; 14/01/22 maths fix; 05/07/22 maths fix; 12/07/22 text fixe

Design of a cognitive neural predictive controller for mobile robot

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityIn this thesis, a cognitive neural predictive controller system has been designed to guide a nonholonomic wheeled mobile robot during continuous and non-continuous trajectory tracking and to navigate through static obstacles with collision-free and minimum tracking error. The structure of the controller consists of two layers; the first layer is a neural network system that controls the mobile robot actuators in order to track a desired path. The second layer of the controller is cognitive layer that collects information from the environment and plans the optimal path. In addition to this, it detects if there is any obstacle in the path so it can be avoided by re-planning the trajectory using particle swarm optimisation (PSO) technique. Two neural networks models are used: the first model is modified Elman recurrent neural network model that describes the kinematic and dynamic model of the mobile robot and it is trained off-line and on-line stages to guarantee that the outputs of the model will accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The second model is feedforward multi-layer perceptron neural network that describes a feedforward neural controller and it is trained off-line and its weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index predictive optimisation algorithm for N step-ahead prediction in order to find the optimal torque action in the transient to stabilise the tracking error of the mobile robot system when the trajectory of the robot is drifted from the desired path during transient state. Three controller methodologies were developed: the first is the feedback neural controller; the second is the nonlinear PID neural feedback controller and the third is nonlinear inverse dynamic neural feedback controller, based on the back-stepping method and Lyapunov criterion. The main advantages of the presented approaches are to plan an optimal path for itself avoiding obstructions by using intelligent (PSO) technique as well as the analytically derived control law, which has significantly high computational accuracy with predictive optimisation technique to obtain the optimal torques control action and lead to minimum tracking error of the mobile robot for different types of trajectories. The proposed control algorithm has been applied to monitor a nonholonomic wheeled mobile robot, has demonstrated the capability of tracking different trajectories with continuous gradients (lemniscates and circular) or non-continuous gradients (square) with bounded external disturbances and static obstacles. Simulations results and experimental work showed the effectiveness of the proposed cognitive neural predictive control algorithm; this is demonstrated by the minimised tracking error to less than (1 cm) and obtained smoothness of the torque control signal less than maximum torque (0.236 N.m), especially when external disturbances are applied and navigating through static obstacles. Results show that the five steps-ahead prediction algorithm has better performance compared to one step-ahead for all the control methodologies because of a more complex control structure and taking into account future values of the desired one, not only the current value, as with one step-ahead method. The mean-square error method is used for each component of the state error vector to compare between each of the performance control methodologies in order to give better control results

Development of Advanced Verification and Validation Procedures and Tools for the Certification of Learning Systems in Aerospace Applications

Adaptive control technologies that incorporate learning algorithms have been proposed to enable automatic flight control and vehicle recovery, autonomous flight, and to maintain vehicle performance in the face of unknown, changing, or poorly defined operating environments. In order for adaptive control systems to be used in safety-critical aerospace applications, they must be proven to be highly safe and reliable. Rigorous methods for adaptive software verification and validation must be developed to ensure that control system software failures will not occur. Of central importance in this regard is the need to establish reliable methods that guarantee convergent learning, rapid convergence (learning) rate, and algorithm stability. This paper presents the major problems of adaptive control systems that use learning to improve performance. The paper then presents the major procedures and tools presently developed or currently being developed to enable the verification, validation, and ultimate certification of these adaptive control systems. These technologies include the application of automated program analysis methods, techniques to improve the learning process, analytical methods to verify stability, methods to automatically synthesize code, simulation and test methods, and tools to provide on-line software assurance

Bim: the setback or solution to project cost issues in Malaysia construction industry?

Malaysia is progressing into Industry Revolution (IR) 4.0 which emphasizes more onto digital, data and artificial intelligence where everything is expected to be automated. However, cost tends to be a major issue at the pioneer stage of embracing technology where Building Information Modelling (BIM) for example tends to be a cost tussle for the current construction industry. Yet, research has shown that BIM is arguably one of the technology platforms in combating the costing issue considering that BIM enables 3D model elements to link to cost and auto-generate quantities which potentially achieve cost-effective project. Due to the conflicting perspectives of how BIM affects project cost issues, it is imperative to investigate the cost-related issues in implementing BIM in the project and to determine how BIM in general positively influences the overall project cost. Qualitative research is adopted in this study. A semi-structured interview was conducted among four professionals who employs BIM in their project. They consist of the assistant manager, senior manager and chief executive officer. The data collected is analysed by utilising Matrix Table for better organisation. The scope of the study is in the Selangor state in which the local construction industry had applied BIM in their construction industry up to the 3D stage. The results showed that the BIM implementation cost is not too burdensome as it is only a one-time cost and does not vary throughout the project period. In addition, the BIM influence on the overall cost of the project is beneficial to the industry. It improves workflow and cost management. In conclusion, BIM is beneficial to the construction industry in the long term. It is important to resolve the costrelated issues for implement BIM and hence, encourage the usage of BIM, especially in the IR 4.0 ecosyste

Design of One Dimensional Adjustment Platform Servo Control System Based on Neural Network

This paper designed a one dimensional adjustment of high precision servo control system, in order to provide individual comprehensive combat system high precision gun visual Angle. In servo control system hardware design based on DSP digital signal processing (DSP) chip as the CPU control circuit, in regard to algorithm, using the three layers BP neural network algorithm for PID integral gain and differential gain and intelligently adjusting proportion gain. On this basis, also analyzes the advantages and disadvantages of the traditional BP neural network algorithm, carries on the improvement. Vector using adaptive control, numerical optimization and introducing the steepness factor method, solve the contradiction between the stability and learning time, greatly improving the convergence speed and stability of the system performance, the static stability of the turntable accuracy is less than 3″, indicators reached the design requirements

Process control of a laboratory combustor using neural networks

Active feedback and feedforward-feedback control systems based on static-trained feedforward multi-layer-perceptron (FMLP) neural networks were designed and demonstrated, by experiment and simulation, for selected species from a laboratory two stage combustor. These virtual controllers functioned through a Visual Basic platform. A proportional neural network controller (PNNC) was developed for a monotonic control problem - the variation of outlet oxygen level with overall equivalence ratio (Φ0). The FMLP neural network maps the control variable to the manipulated variable. This information is in turn transferred to a proportional controller, through the variable control bias value. The proposed feedback control methodology is robust and effective to improve control performance of the conventional control system without drastic changes in the control structure. A detailed case study in which two clusters of FMLP neural networks were applied to a non-monotonic control problem - the variation of outlet nitric oxide level with first-stage equivalence ratio (Φ0) - was demonstrated. The two clusters were used in the feedforward-feedback control scheme. The key novelty is the functionalities of these two network clusters. The first cluster is a neural network-based model-predictive controller (NMPC). It identifies the process disturbance and adjusts the manipulated variables. The second cluster is a neural network-based Smith time-delay compensator (NSTC) and is used to reduce the impact of the long sampling/analysis lags in the process. Unlike other neural network controllers reported in the control field, NMPC and NSTC are efficiently simple in terms of the network structure and training algorithm. With the pre-filtered steady-state training data, the neural networks converged rapidly. The network transient response was originally designed and enabled here using additional tools \u27and mathematical functions in the Visual Basic program. The controller based on NMPC/NSTC showed a superior performance over the conventional proportional-integral derivative (PID) controller. The control systems developed in this study are not limited to the combustion process. With sufficient steady-state training data, the proposed control systems can be applied to control applications in other engineering fields

Desenvolvimento de Automatismos e Algoritmos de Controlo Inteligente, para Monitorização e Controlo de Componentes Habitacionais.

Mestrado em Engenharia MecânicaAccording to data provided by PORDATA, there was a sharp increase in domestic energy consumption in the last decade. In the last 3 to 4 years, this energy consumption has reached a stagnation point or even a slight decrease. One possible factor for this could be related to the introduction of smart systems and low-power equipment in our houses. In a context of reducing the energy consumption and increasing the comfort in a domestic environment, urges the need to develop a smart system, fully autonomous, capable of control and monitoring all housing equipment. In this dissertation, it was developed an algorithm capable of automatically control multiple variables. These variables were: heating or cooling temperatures, either for the air inside the house or its sanitary water, the optimal speed of the ventilator fans and the water pumps, and the air renovations needed in the different house divisions. This control was made according to internal variables related to the house itself: ambient temperatures, exterior temperatures, geothermal temperatures, air flows, etc.. For such a development, it was done, in the first line of work, a study of all the existent algorithms and its theoretical foundations. After that, it was made a performance test, so it could be chosen which studied algorithm was the best in an overall perspective. The test consisted of a self-tuning process of the parameters of a classical PID algorithm, so it could be controlled or regulated the temperature of an oven resistance when applied a PWM signal to it. The criteria used when choosing the best algorithm was the comparison between the performance values of the solutions (e.g. overshoot, rise time and steady-state error), plus the difficulty in its implementation. After choosing the best algorithm, the last part of this dissertation was developing an algorithm that would adapt to the exposed problem, predicting the optimal values for the aforementioned variables. The results obtained were congruent to what were expected, reaching all the pretended objectives for this dissertation. It was concluded that, even though the algorithm needs a lot of training data to achieve good results, the neural network achieved and predicted good results, based on a small database with previously taken results.Segundo dados da PORDATA, na última década tem existido um acréscimo acentuado no consumo energético doméstico. Nos últimos 3 a 4 anos, no entanto, tem existido uma estagnação, ou até mesmo uma diminuição deste mesmo consumo. Um dos possíveis fatores para esta estagnação, pode estar relacionado com a introdução de sistemas inteligentes e equipamentos de baixo consumo. No âmbito de uma redução dos cunsumos energéticos e um aumento do conforto num ambiente doméstico, surge a necessidade do desenvolvimento de um sistema inteligente e completamente autónomo, que controle todos os equipamentos habitacionais. Nesta dissertação foi desenvolvido um algoritmo capaz de controlar automaticamente múltiplas variáveis, sendo estas: Temperaturas de aquecimento e arrefecimento do ar interior da casa e das àguas sanitárias, Velocidades ótimas das ventoinhas dos ventiladores e das bombas de àgua e as renovações de ar necessárias em cada divisão da casa. Este controlo foi conseguido, através das várias variáveis inerentes à casa: temperaturas ambientes, exteriores, geotérmicas, fluxos de ar e àgua, etc.. Para tal desenvolvimento, foi realizado numa primeira fase, o estudo de todos os algoritmos existentes e os seus fundamentos teóricos, e, numa fase posterior, foi realizado um teste de performance a todos estes algoritmos, para que fosse escolhido o melhor algoritmo numa situação geral. O teste consistiu no selftuning dos parâmetros de um PID clássico, para controlar a temperatura de uma resistência elétrica quando aplicado um sinal PWM. Os critérios de escolha do algoritmo, basearam-se na comparação dos resultados do teste de performace (overshoot, tempo de subida e erro em estado-estacionário) e na dificuldade de implementação dos mesmos. Após a escolha do algoritmo, a última parte desta dissertação consistiu no desenvolvimento de uma rede neuronal capaz de se adaptar ao problema em questão, prevendo os valores ótimos para cada uma das váriaveis acima mencionadas. Os resultados obtidos foram congruentes com aquilo que se esperava, conseguindo assim atingir todos os objetivos pretendidos nesta dissertação. Concluiu-se ainda que, mesmo que o algoritmo tenha necessitado de muitos valores de treino para se obterem bons resultados, a rede neuronal previu bons resultados, baseado numa pequena base de dados com dados previamente retirados