System Identification of multi-rotor UAVs using echo state networks

Controller design for aircraft with unusual configurations presents unique challenges, particularly in extracting valid mathematical models of the MRUAVs behaviour. System Identification is a collection of techniques for extracting an accurate mathematical model of a dynamic system from experimental input-output data. This can entail parameter identification only (known as grey-box modelling) or more generally full parameter/structural identification of the nonlinear mapping (known as black-box). In this paper we propose a new method for black-box identification of the non-linear dynamic model of a small MRUAV using Echo State Networks (ESN), a novel approach to train Recurrent Neural Networks (RNN)

Control of Complex Economy through Fiscal Variables. Economics & Complexity - Spring - 1998 - Vol2 N1

The aim of this work is that of exemplifying some applications of the modern theory of the complexity to the economic sector; we will highlight some of the possibilities of control of chaotic systems and some of that possibilities which are opened by the study of such systems. Remembering how a simple traditional macroeconomic model can give place to deterministic chaotic phenomena we will highlight: a) how it is possible to control such a system using opportune values of the fiscal variables; b) how it is possible to foresee the trend of the objective variable through a neural network, and, therefore, subsequently to control it on the basis of the value instruments chosen by the neural network. This will be done either in the presence of casual noises or in the case of a completely deterministic model; c) finally a different and more recent method of controlling chaotic systems will be indicated.Public Finance, Complexity, Control of Economics, Macroeconomics

Multi particle swarm optimisation algorithm applied to supervisory power control systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPower quality problems come in numerous forms (commonly spikes, surges, sags, outages and harmonics) and their resolution can cost from a few hundred to millions of pounds, depending on the size and type of problem experienced by the power network. They are commonly experienced as burnt-out motors, corrupt data on hard drives, unnecessary downtime and increased maintenance costs. In order to minimise such events, the network can be monitored and controlled with a specific control regime to deal with particular faults. This study developed a control and Optimisation system and applied it to the stability of electrical power networks using artificial intelligence techniques. An intelligent controller was designed to control and optimise simulated models for electrical system power stability. Fuzzy logic controller controlled the power generation, while particle swarm Optimisation (PSO) techniques optimised the system’s power quality in normal operation conditions and after faults. Different types of PSO were tested, then a multi-swarm (M-PSO) system was developed to give better Optimisation results in terms of accuracy and convergence speed.. The developed Optimisation algorithm was tested on seven benchmarks and compared to the other types of single PSOs. The developed controller and Optimisation algorithm was applied to power system stability control. Two power electrical network models were used (with two and four generators), controlled by fuzzy logic controllers tuned using the Optimisation algorithm. The system selected the optimal controller parameters automatically for normal and fault conditions during the operation of the power network. Multi objective cost function was used based on minimising the recovery time, overshoot, and steady state error. A supervisory control layer was introduced to detect and diagnose faults then apply the correct controller parameters. Different fault scenarios were used to test the system performance. The results indicate the great potential of the proposed power system stabiliser as a superior tool compared to conventional control systems

Chaotic exploration and learning of locomotor behaviours

Recent developments in the embodied approach to understanding the generation of adaptive behaviour, suggests that the design of adaptive neural circuits for rhythmic motor patterns should not be done in isolation from an appreciation, and indeed exploitation, of neural-body-environment interactions. Utilising spontaneous mutual entrainment between neural systems and physical bodies provides a useful passage to the regions of phase space which are naturally structured by the neuralbody- environmental interactions. A growing body of work has provided evidence that chaotic dynamics can be useful in allowing embodied systems to spontaneously explore potentially useful motor patterns. However, up until now there has been no general integrated neural system that allows goal-directed, online, realtime exploration and capture of motor patterns without recourse to external monitoring, evaluation or training methods. For the first time, we introduce such a system in the form of a fully dynamic neural system, exploiting intrinsic chaotic dynamics, for the exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modelled as a network of neural oscillators which are coupled only through physical embodiment, and goal directed exploration of coordinated motor patterns is achieved by a chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organised dynamics each of which is a candidate for a locomotion behaviour. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states using its intrinsic chaotic dynamics as a driving force and stabilises the system on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced which results in an increased diversity of motor outputs, thus achieving multi-scale exploration. A rhythmic pattern discovered by this process is memorised and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronisation method. The dynamical nature of the weak coupling through physical embodiment allows this adaptive weight learning to be easily integrated, thus forming a continuous exploration-learning system. Our result shows that the novel neuro-robotic system is able to create and learn a number of emergent locomotion behaviours for a wide range of body configurations and physical environment, and can re-adapt after sustaining damage. The implications and analyses of these results for investigating the generality and limitations of the proposed system are discussed

An Improved Bees Algorithm for Training Deep Recurrent Networks for Sentiment Classification

Recurrent neural networks (RNNs) are powerful tools for learning information from temporal sequences. Designing an optimum deep RNN is difficult due to configuration and training issues, such as vanishing and exploding gradients. In this paper, a novel metaheuristic optimisation approach is proposed for training deep RNNs for the sentiment classification task. The approach employs an enhanced Ternary Bees Algorithm (BA-3+), which operates for large dataset classification problems by considering only three individual solutions in each iteration. BA-3+ combines the collaborative search of three bees to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. Local learning with exploitative search utilises the greedy selection strategy. Stochastic gradient descent (SGD) learning with singular value decomposition (SVD) aims to handle vanishing and exploding gradients of the decision parameters with the stabilisation strategy of SVD. Global learning with explorative search achieves faster convergence without getting trapped at local optima to find the optimal set of trainable parameters of the proposed deep recurrent learning architecture. BA-3+ has been tested on the sentiment classification task to classify symmetric and asymmetric distribution of the datasets from different domains, including Twitter, product reviews, and movie reviews. Comparative results have been obtained for advanced deep language models and Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. BA-3+ converged to the global minimum faster than the DE and PSO algorithms, and it outperformed the SGD, DE, and PSO algorithms for the Turkish and English datasets. The accuracy value and F1 measure have improved at least with a 30–40% improvement than the standard SGD algorithm for all classification datasets. Accuracy rates in the RNN model trained with BA-3+ ranged from 80% to 90%, while the RNN trained with SGD was able to achieve between 50% and 60% for most datasets. The performance of the RNN model with BA-3+ has as good as for Tree-LSTMs and Recursive Neural Tensor Networks (RNTNs) language models, which achieved accuracy results of up to 90% for some datasets. The improved accuracy and convergence results show that BA-3+ is an efficient, stable algorithm for the complex classification task, and it can handle the vanishing and exploding gradients problem of deep RNNs

Load frequency controllers considering renewable energy integration in power system

Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency

Time-delayed impulsive control for discrete-time nonlinear systems with actuator saturation

This paper focuses on the problem of time-delayed impulsive control with actuator saturation for discrete-time dynamical systems. By establishing a delayed impulsive difference inequality, combining with convex analysis and inequality techniques, some sufficient conditions are obtained to ensure exponential stability for discrete-time dynamical systems via time-delayed impulsive controller with actuator saturation. The designed controller admits the existence of some transmission delays in impulsive feedback law, and the control input variables are required to stay within an availability zone. Several numerical simulations are also given to demonstrate the effectiveness of the proposed results.&nbsp

Evolutionary robotics and neuroscience

No description supplie

Recent tendencies in the use of optimization techniques in geotechnics:a review

The use of optimization methods in geotechnics dates back to the 1950s. They were used in slope stability analysis (Bishop) and evolved to a wide range of applications in ground engineering. We present here a non-exhaustive review of recent publications that relate to the use of different optimization techniques in geotechnical engineering. Metaheuristic methods are present in almost all the problems in geotechnics that deal with optimization. In a number of cases, they are used as single techniques, in others in combination with other approaches, and in a number of situations as hybrids. Different results are discussed showing the advantages and issues of the techniques used. Computational time is one of the issues, as well as the assumptions those methods are based on. The article can be read as an update regarding the recent tendencies in the use of optimization techniques in geotechnics