Fuzzy neural networks for control of dynamic systems

This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.

CXCR4 Antagonism attenuates the development of diabetic cardiac fibrosis

Heart failure (HF) is an increasingly recognized complication of diabetes. Cardiac fibrosis is an important causative mechanism of HF associated with diabetes. Recent data indicate that inflammation may be particularly important in the pathogenesis of cardiovascular fibrosis. We sought to determine the mechanism by which cardiac fibrosis develops and to specifically investigate the role of the CXCR4 axis in this process. Animals with type I diabetes (streptozotocin treated mice) or type II diabetes (Israeli Sand-rats) and controls were randomized to treatment with a CXCR4 antagonist, candesartan or vehicle control. Additional groups of mice also underwent bone marrow transplantation (GFP+ donor marrow) to investigate the potential role of bone marrow derived cell mobilization in the pathogenesis of cardiac fibrosis. Both type I and II models of diabetes were accompanied by the development of significant cardiac fibrosis. CXCR4 antagonism markedly reduced cardiac fibrosis in both models of diabetes, similar in magnitude to that seen with candesartan. In contrast to candesartan, the anti-fibrotic actions of CXCR4 antagonism occurred in a blood pressure independent manner. Whilst the induction of diabetes did not increase the overall myocardial burden of GFP+ cells, it was accompanied by an increase in GFP+ cells expressing the fibroblast marker alpha-smooth muscle actin and this was attenuated by CXCR4 antagonism. CXCR4 antagonism was also accompanied by increased levels of circulating regulatory T cells. Taken together the current data indicate that pharmacological inhibition of CXCR4 significantly reduces diabetes induced cardiac fibrosis, providing a potentially important therapeutic approach.

Statin-induced increases in atrophy gene expression occur independently of changes in PGC1α protein and mitochondrial content

One serious side effect of statin drugs is skeletal muscle myopathy. Although the mechanism(s) responsible for statin myopathy remains to be fully determined, an increase in muscle atrophy gene expression and changes in mitochondrial content and/or function have been proposed to play a role. In this study, we examined the relationship between statin-induced expression of muscle atrophy genes, regulators of mitochondrial biogenesis, and markers of mitochondrial content in slow- (ST) and fast-twitch (FT) rat skeletal muscles. Male Sprague Dawley rats were treated with simvastatin (60 or 80 mg·kg(-1)·day(-1)) or vehicle control via oral gavage for 14 days. In the absence of overt muscle damage, simvastatin treatment induced an increase in atrogin-1, MuRF1 and myostatin mRNA expression; however, these were not associated with changes in peroxisome proliferator gamma co-activator 1 alpha (PGC-1α) protein or markers of mitochondrial content. Simvastatin did, however, increase neuronal nitric oxide synthase (nNOS), endothelial NOS (eNOS) and AMPK α-subunit protein expression, and tended to increase total NOS activity, in FT but not ST muscles. Furthermore, simvastatin induced a decrease in β-hydroxyacyl CoA dehydrogenase (β-HAD) activity only in FT muscles. These findings suggest that the statin-induced activation of muscle atrophy genes occurs independent of changes in PGC-1α protein and mitochondrial content. Moreover, muscle-specific increases in NOS expression and possibly NO production, and decreases in fatty acid oxidation, could contribute to the previously reported development of overt statin-induced muscle damage in FT muscles.

Theranostic multimodular potential of zinc-doped ferrite-saturated metal-binding protein-loaded novel nanocapsules in cancers

The present study successfully developed orally deliverable multimodular zinc (Zn) iron oxide (Fe3O4)-saturated bovine lactoferrin (bLf)-loaded polymeric nanocapsules (NCs), and evaluated their theranostic potential (antitumor efficacy, magnetophotothermal efficacy and imaging capability) in an in vivo human xenograft CpG-island methylator phenotype (CIMP)-1(+)/CIMP2(-)/chromosome instability-positive colonic adenocarcinoma (Caco2) and claudin-low, triple-negative (ER(-)/PR(-)/HER2(-); MDA-MB-231) breast cancer model. Mice fed orally on the Zn-Fe-bLf NC diet showed downregulation in tumor volume and complete regression in tumor volume after 45 days of feeding. In human xenograft colon cancer, vehicle-control NC diet-group (n=5) mice showed a tumor volume of 52.28±11.55 mm(3), and Zn-Fe-bLf NC diet (n=5)-treated mice had a tumor-volume of 0.10±0.073 mm(3). In the human xenograft breast cancer model, Zn-Fe-bLf NC diet (n=5)-treated mice showed a tumor volume of 0.051±0.062 mm(3) within 40 days of feeding. Live mouse imaging conducted by near-infrared fluorescence imaging of Zn-Fe-bLf NCs showed tumor site-specific localization and regression of colon and breast tumor volume. Ex vivo fluorescence-imaging analysis of the vital organs of mice exhibited sparse localization patterns of Zn-Fe-bLf NCs and also confirmed tumor-specific selective localization patterns of Zn-Fe-bLf NCs. Dual imaging using magnetic resonance imaging and computerized tomography scans revealed an unprecedented theranostic ability of the Zn-Fe-bLf NCs. These observations warrant consideration of multimodular Zn-Fe-bLf NCs for real-time cancer imaging and simultaneous cancer-targeted therapy.

Multi-surface sliding control of MIMO autonomous flight systems

In this paper, we present a hardware in the loop simulation of our proposed multi-surface sliding control (MSSC) for trajectory tracking of 6 degrees of freedom (6-DOF) inertia coupled aerial vehicles with multiple inputs and multiple outputs (MIMO). Using MSSC on MIMO autonomous flight systems creates confluent control that can account for both matched and mismatched uncertainties, system disturbances and excitation in internal dynamics. The control law is implemented on an onboard computer and is validated though Hardware-In-the-Loop (HIL) simulations, between the hardware and the flight simulator X-Plane, which simulates the unmanned aircraft dynamics, sensors, and actuators. Simulation results are presented to validate the analysis.

Haptic control of multi-axis robotic systems

Control of tele-operated remote robot’s is nothing new; the public was introduced to this 'new' field in 1986 when the Chernobyl cleanup began. Pictures of weird and wonderful robotic workers pouring concrete or moving rubble flooded the world. Integration of force feedback or 'haptics' to remote robot's is a new development and one that is likely to make a big difference in man-machine interaction. Development of haptic capable tele-operation schema is a challenge. Often platform specific software is developed for one off tasks. This research focussed on the development of an open software platform for haptic control of multiple remote robotic platforms. The software utilises efficient server/client architecture for low data latency, while efficiently performing required kinematic transforms and data manipulation in real time. A description of the algorithm, software interface and hardware is presented in this paper. Preliminary results are encouraging as haptic control has been shown to greatly enhances remote positioning tasks.

Observer-based control of multi-agent systems under decentralized information structure

This paper addresses the problem of decentralized implementation of a global state feedback controller for multi-agent systems. The system is assumed to be under the constraint of a complete decentralized information structure. The decentralization of the control task is achieved through the construction of low-order decentralized functional observers with the purpose of generating the required corresponding control signal for each local control station. A design procedure is developed for obtaining an approximate solution to the design of the observers. Stability analysis is provided for the global system using the proposed observer-based approach. A numerical example is given to illustrate the design procedure and cases when the observers' order increases from the lowest value.

A flow control methodology for the rapid generation of vehicle traffic models

This paper presents a conveyor-based methodology to model complex vehicle flows common to factory and distribution warehouse facilities. The AGV and human path modelling techniques available in many commercial discrete event simulation packages require extensive knowledge and time to implement even the simplest flow control rules for multiple vehicle interaction. Although discrete event simulation is accepted as an effective tool to model vehicle delivery movements, human paths and delivery schedules for modern assembly lines, the time to generate accurate models is a significant limitation of existing simulation-based optimisation methodologies. The flow control method has been successfully implemented using two commercial simulation packages. It provides a realistic visual representation, as well as accurate statistical results, and reduces the model development process cost.

Smart cluster bombs - control of multi-agent systems for military applications

This paper introduces an aggregation algorithm for airborne swarming guided weapon systems, which can aggregate munitions into a given shape while reaching the surface. The algorithm uses an artificial force based controller to navigate the members of the swarm into the desired geographical position and evenly distribute them inside the shape. Inter-member repulsion forces are used to avoid collisions among members, which is crucial for a weapon deployment system. Moreover, a lower bound for the release height was obtained which guarantee convergence of the complete weapon system into the target area. The proposed swarming guided weapon system was tested using computer simulations.

A distributed approach to global feedback control of multi-agent systems

This paper presents a distributed approach to global state feedback control of for multi-agent systems. The proposed solution is obtained from the construction of decentralised functional observers. The required local control signal is generated asymptotically using only corresponding local output information. It is shown that a complete distribution of the control of a decentralised system having N agents can be achieved upon the satisfaction of some necessary conditions, and that the resulting controller can emulate the performance of a global state feedback controller. A step-by-step design algorithm is given. Its simplicity and correctness are illustrated through a numerical example.

3D virtual haptic cone for intuitive vehicle motion control

Haptic human-machine interfaces and interaction techniques have been shown to offer advantages over conventional approaches. This work introduces the 3D virtual haptic cone with the aim of improving human remote control of a vehicle's motion. The 3D cone introduces a third dimension to the haptic control surface over existing approaches. This approach improves upon existing methods by providing the human operator with an intuitive method for issuing vehicle motion commands whilst simultaneously receiving real-time haptic information from the remote system. The presented approach offers potential across many applications, and as a case study, this work considers the approach in the context of mobile robot motion control. The performance of the approach in providing the operator with improved motion controllability is evaluated and the performance improvement determined.

3D virtual haptic cone for intuitive vehicle motion control

Haptic technology provides the ability for a system to recreate the sense of touch to a human operator, and as such offers wide reaching advantages. The ability to interact with the human's tactual modality introduces haptic human-machine interaction to replace or augment existing mediums such as visual and audible information. A distinct advantage of haptic human-machine interaction is the intrinsic bilateral nature, where information can be communicated in both directions simultaneously. This paper investigates the bilateral nature of the haptic interface in controlling the motion of a remote (or virtual) vehicle and presents the ability to provide an additional dimension of haptic information to the user over existing approaches [1-4]. The 3D virtual haptic cone offers the ability to not only provide the user with relevant haptic augmentation pertaining to the task at hand, as do existing approaches, however, to also simultaneously provide an intuitive indication of the current velocities being commanded.

Observer-based robust finite-time cooperative consensus control for multi-agent networks

This paper studies the finite-time consensus tracking control for multi-agent networks. The time-varying control input and the velocity of the leader is unknown to any follower. Only the position of the leader is known to its neighbors. We first propose a new finite-time multiple-surface sliding mode observer to estimate the leader's velocity. It is seen that the estimation error of the observer can converge to zero in a finite time. Then, we prove that finite-time consensus tracking of multi-agent networks can be achieved on a new terminal sliding mode surface. Simulation results are presented to validate the analysis.

Integral terminal sliding mode cooperative control of multi-robot networks

This paper studies the integral terminal sliding mode cooperative control of multi-robot networks. Here, we first propose an integral terminal sliding mode surface for a class of first order systems. Then, we prove that finite time consensus tracking of multi-robot networks can be achieved on this integral terminal sliding mode surface. Simulation results are presented to validate the analysis.