Quadrotor Altitude Control using Recurrent Neural Network PID

The quadrotor is one type of Unmanned Aerial Vehicle (UAV) or unmanned flying vehicle. Quadrotor can be operated by a remote controller or autonomously. Quadrotor control is a challenging problem because it takes into account complex things such as parametric uncertainty, external disturbances, and so on. At the spatial level, three linear degrees of freedom along three axes and three degrees of freedom rotating along three axes are used for the control of a quadrotor. Conventional controls for quadrotors are widely used such as PID, state feedback, and so on. However, because the control is linear, non-linear control has begun to be developed. Some of these controls, for example, use a sliding mode control system, fuzzy methods, and controls by combining linear control with artificial intelligence. This paper will use PID control and an artificial neural network for the quadrotor direction control system. The results of this control test indicate that the combination of PID and RNN on the directional control shows a better response than conventional PID

Improved orthogonal array based simulated annealing for design optimization

Recent research shows that simulated annealing with orthogonal array based neighbourhood functions can help in the search for a solution to a parametrical problem which is closer to an optimum when compared with conventional simulated annealing. Previous studies of simulated annealing analyzed only the main effects of variables of parametrical problems. In fact, both main effects of variables and interactions between variables should be considered, since interactions between variables exist in many parametrical problems. In this paper, an improved orthogonal array based neighbourhood function (IONF) for simulated annealing with the consideration of interaction effects between variables is described. After solving a set of parametrical benchmark function problems where interaction effects between variables exist, results of the benchmark tests show that the proposed simulated annealing algorithm with the IONF outperforms significantly both the simulated annealing algorithms with the existing orthogonal array based neighbourhood functions and the standard neighbourhood functions. Finally, the improved orthogonal array based simulated annealing was applied on the optimization of emulsified dynamite packing-machine design by which the applicability of the algorithm in real world problems can be evaluated and its effectiveness can be further validated

Towards a novel biologically-inspired cloud elasticity framework

With the widespread use of the Internet, the popularity of web applications has significantly increased. Such applications are subject to unpredictable workload conditions that vary from time to time. For example, an e-commerce website may face higher workloads than normal during festivals or promotional schemes. Such applications are critical and performance related issues, or service disruption can result in financial losses. Cloud computing with its attractive feature of dynamic resource provisioning (elasticity) is a perfect match to host such applications. The rapid growth in the usage of cloud computing model, as well as the rise in complexity of the web applications poses new challenges regarding the effective monitoring and management of the underlying cloud computational resources. This thesis investigates the state-of-the-art elastic methods including the models and techniques for the dynamic management and provisioning of cloud resources from a service provider perspective. An elastic controller is responsible to determine the optimal number of cloud resources, required at a particular time to achieve the desired performance demands. Researchers and practitioners have proposed many elastic controllers using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. There exist many issues that have not received much attention from a holistic point of view. Some of these issues include: 1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; 2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and 3) the lack of considering uncertainty aspects while designing auto-scaling solutions. This thesis seeks solutions to address these issues altogether using an integrated approach. Moreover, this thesis aims at the provision of qualitative elasticity rules. This thesis proposes a novel biologically-inspired switched feedback control methodology to address the horizontal elasticity problem. The switched methodology utilises multiple controllers simultaneously, whereas the selection of a suitable controller is realised using an intelligent switching mechanism. Each controller itself depicts a different elasticity policy that can be designed using the principles of fixed gain feedback controller approach. The switching mechanism is implemented using a fuzzy system that determines a suitable controller/- policy at runtime based on the current behaviour of the system. Furthermore, to improve the possibility of bumpless transitions and to avoid the oscillatory behaviour, which is a problem commonly associated with switching based control methodologies, this thesis proposes an alternative soft switching approach. This soft switching approach incorporates a biologically-inspired Basal Ganglia based computational model of action selection. In addition, this thesis formulates the problem of designing the membership functions of the switching mechanism as a multi-objective optimisation problem. The key purpose behind this formulation is to obtain the near optimal (or to fine tune) parameter settings for the membership functions of the fuzzy control system in the absence of domain experts’ knowledge. This problem is addressed by using two different techniques including the commonly used Genetic Algorithm and an alternative less known economic approach called the Taguchi method. Lastly, we identify seven different kinds of real workload patterns, each of which reflects a different set of applications. Six real and one synthetic HTTP traces, one for each pattern, are further identified and utilised to evaluate the performance of the proposed methods against the state-of-the-art approaches

Upravljanje dinamičkim sistemima primenom adaptivnih ortogonalnih neuronskih mreža

The goal of the research in the PhD dissertation is control of dynamical systems by using new types of orthogonal endocrine neural networks, in order to improve their performances. Standard artificial neural networks are described, as well as their historical development and basic types of learning algorithms. Further, possibilities for neural networks applicability within control logic of dynamical systems are presented, as well as the current state of the art of orthogonal and endocrine neural networks. Performance improvement of the laboratory model of a servo system by using a standard neural network with the backpropagation type of learning is analyzed. In addition, a method for selection and optimization of training data, as an efficient way of information preprocessing for the purpose of improving performances of a neural network, is presented. A detailed description of orthogonal functions and implementation methods of endocrine factors inside standard neural networks are provided. By implementation of orthogonal activation functions of neurons, verification of their applicability in control of dynamical systems was performed. The laboratory model of the magnetic levitation system was used to test the designed orthogonal neural network. Furthermore, the endocrine orthogonal neural network based on the biological processes of excitation and inhibition is designed. Network performance checkup is performed by testing its predictive abilities when working with time series data. Final dissertation researches refer to development of hybrid systems. The implemented adaptive endocrine neuro-fuzzy hybrid system is tested through modeling of a laboratory servo system. Other hybrid structure, based on a combination of an orthogonal endocrine neural network and an orthogonal endocrine neuro-fuzzy hybrid system, is designed with the aim to form symbiosis of the positive characteristics of the individual networks. Verification of this structure was performed by using it for PID controller parameters adjustments