Fuzzy PD Control of Networked Control Systems Based on CMAC Neural Network

The network and plant can be regarded as a controlled time-varying system because of the random induced delay in the networked control systems. The cerebellar model articulation controller (CMAC) neural network and a PD controller are combined to achieve the forward feedback control. The PD controller parameters are adjusted adaptively by fuzzy reasoning mechanism, which can optimize the control effect by reducing the uncertainty caused by the network-induced delay. Finally, the simulations show that the control method proposed can improve the performance effectively

Real time control of nonlinear dynamic systems using neuro-fuzzy controllers

The problem of real time control of a nonlinear dynamic system using intelligent control techniques is considered. The current trend is to incorporate neural networks and fuzzy logic into adaptive control strategies. The focus of this work is to investigate the current neuro-fuzzy approaches from literature and adapt them for a specific application. In order to achieve this objective, an experimental nonlinear dynamic system is considered. The motivation for this comes from the desire to solve practical problems and to create a test-bed which can be used to test various control strategies. The nonlinear dynamic system considered here is an unstable balance beam system that contains two fluid tanks, one at each end, and the balance is achieved by pumping the fluid back and forth from the tanks. A popular approach, called ANFIS (Adaptive Networks-based Fuzzy Inference Systems), which combines the structure of fuzzy logic controllers with the learning aspects from neural networks is considered as a basis for developing novel techniques, because it is considered to be one of the most general framework for developing adaptive controllers. However, in the proposed new method, called Generalized Network-based Fuzzy Inferencing Systems (GeNFIS), more conventional fuzzy schemes for the consequent part are used instead of using what is called the Sugeno type rules. Moreover, in contrast to ANFIS which uses a full set of rules, GeNFIS uses only a limited number of rules based on certain expert knowledge. GeNFIS is tested on the balance beam system, both in a real- time actual experiment and the simulation, and is found to perform better than a comparable ANFIS under supervised learning. Based on these results, several modifications of GeNFIS are considered, for example, synchronous defuzzification through triangular as well as bell shaped membership functions. Another modification involves simultaneous use of Sugeno type as well as conventional fuzzy schemes for the consequent part, in an effort to create a more flexible framework. Results of testing different versions of GeNFIS on the balance beam system are presented

Shifts in the suitable habitat available for brown trout (Salmo trutta L.) under short-term climate change scenarios

The impact of climate change on the habitat suitability for large brown trout (Salmo trutta L.) was studied in a segment of the Cabriel River (Iberian Peninsula). The future flow and water temperature patterns were simulated at a daily time step with M5 models' trees (NSE of 0.78 and 0.97 respectively) for two short-term scenarios (2011 2040) under the representative concentration pathways (RCP 4.5 and 8.5). An ensemble of five strongly regularized machine learning techniques (generalized additive models, multilayer perceptron ensembles, random forests, support vector machines and fuzzy rule base systems) was used to model the microhabitat suitability (depth, velocity and substrate) during summertime and to evaluate several flows simulated with River2D©. The simulated flow rate and water temperature were combined with the microhabitat assessment to infer bivariate habitat duration curves (BHDCs) under historical conditions and climate change scenarios using either the weighted usable area (WUA) or the Boolean-based suitable area (SA). The forecasts for both scenarios jointly predicted a significant reduction in the flow rate and an increase in water temperature (mean rate of change of ca. −25% and +4% respectively). The five techniques converged on the modelled suitability and habitat preferences; large brown trout selected relatively high flow velocity, large depth and coarse substrate. However, the model developed with support vector machines presented a significantly trimmed output range (max.: 0.38), and thus its predictions were banned from the WUA-based analyses. The BHDCs based on the WUA and the SA broadly matched, indicating an increase in the number of days with less suitable habitat available (WUA and SA) and/or with higher water temperature (trout will endure impoverished environmental conditions ca. 82% of the days). Finally, our results suggested the potential extirpation of the species from the study site during short time spans.The study has been partially funded by the IMPADAPT project (CGL2013-48424-C2-1-R) - Spanish MINECO (Ministerio de Economia y Competitividad) - and FEDER funds and by the Confederacion Hidrografica del Jucar (Spanish Ministry of Agriculture, Food and Environment). We are grateful to the colleagues who worked in the field and in the preliminary data analyses, especially Juan Diego Alcaraz-Henandez, David Argibay, Aina Hernandez and Marta Bargay. Thanks to Matthew J. Cashman for the academic review of English. Finally, the authors would also to thank the Direccion General del Agua and INFRAECO for the cession of the trout data. The authors thank AEMET and UC by the data provided for this work (dataset Spain02).Muñoz Mas, R.; López Nicolás, AF.; Martinez-Capel, F.; Pulido-Velazquez, M. (2016). Shifts in the suitable habitat available for brown trout (Salmo trutta L.) under short-term climate change scenarios. Science of the Total Environment. 544:686-700. https://doi.org/10.1016/j.scitotenv.2015.11.14768670054

TS fuzzy approach for modeling, analysis and design of non-smooth dynamical systems

There has been growing interest in the past two decades in studying the physical model of dynamical systems that can be described by nonlinear, non-smooth differential equations, i.e. non-smooth dynamical systems. These systems exhibit more colourful and complex dynamics compared to their smooth counterparts; however, their qualitative analysis and design are not yet fully developed and still open to exploration. At the same time, Takagi-Sugeno (TS) fuzzy systems have been shown to have a great ability to represent a large class of nonlinear systems and approximate their inherent uncertainties. This thesis explores an area of TS fuzzy systems that have not been considered before; that is, modelling, stability analysis and design for non-smooth dynamical systems. TS fuzzy model structures capable of representing or approximating the essential dis- continuous dynamics of non-smooth systems are proposed in this thesis. It is shown that by incorporating discrete event systems, the proposed structure for TS fuzzy models, which we will call non-smooth TS fuzzy models, can accurately represent the smooth (or contin- uous) as well as non-smooth (or discontinuous) dynamics of different classes of electrical and mechanical non-smooth systems including (sliding and non-sliding) Filippov's systems and impacting systems. The different properties of the TS fuzzy modelling (or formalism) are discussed. It is highlighted that the TS fuzzy formalism, taking advantage of its simple structure, does not need a special platform for its implementation. Stability in its new notion of structural stability (stability of a periodic solution) is one of the most important issues in the qualitative analysis of non-smooth systems. An important part of this thesis is focused on addressing stability issues by extending non- smooth Lyapunov theory for verifying the stability of local orbits, which the non-smooth TS fuzzy models can contain. Stability conditions are proposed for Filippov-type and impacting systems and it is shown that by formulating the conditions as Linear Matrix inequalities (LMIs), the onset of non-smooth bifurcations or chaotic phenomena can be detected by solving a feasibility problem. A number of examples are given to validate the proposed approach. Stability robustness of non-smooth TS fuzzy systems in the presence of model uncertainties is discussed in terms of non-smoothness rather than traditional observer design. The LMI stabilization problem is employed as a building block for devising design strategies to suppress the unwanted chaotic behaviour in non-smooth TS fuzzy models. There have been a large number of control applications in which the overall closed-loop sys tem can be stabilized by switching between pre-designed sub-controllers. Inspired by this idea, the design part of this thesis concentrates on fuzzy-chaos control strategies for Filippov-type systems. These strategies approach the design problem by switching be- tween local state-feedback controllers such that the closed-loop TS fuzzy system of interest rapidly converges to the stable periodic solution of the system. All control strategies are also automated as a design problem recast on linear matrix inequality conditions to be solved by modern optimization techniques. Keywords: Takagi-Sugeno fuzzy systems, non-smooth Lyapunov theory, non-smooth dy- namical systems, piecewise-smooth dynamical systems, structural stability, discontinuity- induced bifurcation, chaos controllers, dc-dc converters, Filippov's system, impacting system, linear matrix inequalities.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Adaptive nonlinear control using fuzzy logic and neural networks

The problem of adaptive nonlinear control, i.e. the control of nonlinear dynamic systems with unknown parameters, is considered. Current techniques usually assume that either the control system is linearizable or the type of nonlinearity is known. This results in poor control quality for many practical problems. Moreover, the control system design becomes too complex for a practicing engineer. The objective of this thesis is to provide a practical, systematic approach for solving the problem of identification and control of nonlinear systems with unknown parameters, when the explicit linear parametrization is either unknown or impossible. Fuzzy logic (FL) and neural networks (NNs) have proven to be the tools for universal approximation, and hence are considered. However, FL requires expert knowledge and there is a lack of systematic procedures to design NNs for control. A hybrid technique, called fuzzy logic adaptive network (FLAN), which combines the structure of an FL controller with the learning aspects of the NNs is developed. FLAN is designed such that it is capable of both structure learning and parameter learning. Gradient descent based technique is utilized for the parameter learning in FLAN, and it is tested through a variety of simulated experiments in identification and control of nonlinear systems. The results indicate the success of FLAN in terms of accuracy of estimation, speed of convergence, insensitivity against a range of initial learning rates, robustness against sudden changes in the input as well as noise in the training data. The performance of FLAN is also compared with the techniques based on FL and NNs, as well as several hybrid techniques

Contribution à la commande des systèmes non linéaires : application à la machine synchrone à réluctance variable

N ombreux sont les problèmes en ingénierie nécessite l’estimation de l’état d’un système via un observateur. Cependant, la modélisation et la synthèse de l’observateur deviennent des taches difficiles pour des systèmes non linéaires. Face à ces difficultés, l’approche multimodèle peut être mise à profit. Les travaux de recherche présentés dans cette thèse portent sur l’estimation d’état des systèmes non linéaires représentés par des multimodèles flous de type Takagi-Sugeno couplé. Cette représentation est obtenue grâce à l’utilisation de la décomposition en secteurs non linéaire qui nous permettant de réécrire le nouveau système sous forme de polytopes sans perte d’information. Cette forme est ensuite utile pour la synthèse d’un observateur robuste vis-à-vis des entrées inconnues afin de reconstruire les états du système et les entrées inconnues. Après une brève introduction à l’approche multimodèle, le problème de l’estimation d’état des systèmes non linéaires décrits par les multimodèles flous couplés est abordé. Ensuite, nous présentons des algorithmes pour synthétiser des observateurs d’état robustes face à des entrées inconnues. Nous avons utilisé deux types d’observateurs à gains proportionnel-intégral et à gains multi-intégral. Finalement, nous appliquons ces approches au modèle d’une machine synchrone à réluctance variable

Recursive neuro fuzzy techniques for online identification and control

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de ComputadoresThe main goal of this thesis will be focused on developing an adaptative closed loop control solution, using fuzzy methodologies. A positive theoretical and experimental contribution, regarding modelling and control of fuzzy and neuro fuzzy systems, is expected to be achieved. Proposed non-linear identification solution will use for modelling and control, a recurrent neuro fuzzy architecture. Regarding model solution, a state space approach will be considered during fuzzy consequent local models design. Developed controller will be based on model parameters, being expected not only a stable closed loop solution, but also a static error with convergence towards zero. Model and controller fuzzy subspaces, will be partitioned throughout process dynamical universe, allowing fuzzy local models and controllers commutation and aggregation. With the aim of capturing process under control dynamics using a real time approach, the use of recursive optimization techniques are to be adopted. Such methods will be applied during parameter and state estimation, using a dual decoupled Kalman filter extended with unscented transformation. Two distinct processes one single-input (SISO) other multi-input (MIMO), will be used during experimentation. It is expected from experiments, a practical validation of proposed solution capabilities for control and identification. Presented work will not be completed, without first presenting a global analysis of adopted concepts and methods, describing new perspectives for future investigations

Soft-Boosted Self-Constructing Neural Fuzzy Inference Network

© 2013 IEEE. This correspondence paper proposes an improved version of the self-constructing neural fuzzy inference network (SONFIN), called soft-boosted SONFIN (SB-SONFIN). The design softly boosts the learning process of the SONFIN in order to decrease the error rate and enhance the learning speed. The SB-SONFIN boosts the learning power of the SONFIN by taking into account the numbers of fuzzy rules and initial weights which are two important parameters of the SONFIN, SB-SONFIN advances the learning process by: 1) initializing the weights with the width of the fuzzy sets rather than just with random values and 2) improving the parameter learning rates with the number of learned fuzzy rules. The effectiveness of the proposed soft boosting scheme is validated on several real world and benchmark datasets. The experimental results show that the SB-SONFIN possesses the capability to outperform other known methods on various datasets