A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

Adaptive depth estimation in image based visual servo control of dynamic systems

This paper considers the question of designing a fully image based visual servo control for a dynamic system. The work is motivated by the ongoing development of image based visual servo control of small aerial robotic vehicles. The observed targets considered are coloured blobs on a flat surface to which the normal direction is known. The theoretical framework is directly applicable to the case of markings on a horizontal floor or landing field. The image features used are a first order spherical moment for position and an image flow measurement for velocity. A fully non-linear adaptive control design is provided that ensures global stability of the closed-loop system. © 2005 IEEE.

Monitoring of complex systems of interacting dynamic systems

Increases in functionality, power and intelligence of modern engineered systems led to complex systems with a large number of interconnected dynamic subsystems. In such machines, faults in one subsystem can cascade and affect the behavior of numerous other subsystems. This complicates the traditional fault monitoring procedures because of the need to train models of the faults that the monitoring system needs to detect and recognize. Unavoidable design defects, quality variations and different usage patterns make it infeasible to foresee all possible faults, resulting in limited diagnostic coverage that can only deal with previously anticipated and modeled failures. This leads to missed detections and costly blind swapping of acceptable components because of one’s inability to accurately isolate the source of previously unseen anomalies. To circumvent these difficulties, a new paradigm for diagnostic systems is proposed and discussed in this paper. Its feasibility is demonstrated through application examples in automotive engine diagnostics.

Learning processes in creative service teams : towards a dynamic systems theory

The fastest-growing segment of jobs in the creative sector are in those firms that provide creative services to other sectors (Hearn, Goldsmith, Bridgstock, Rodgers 2014, this volume; Cunningham 2014, this volume). There are also a large number of Creative Services (Architecture and Design, Advertising and Marketing, Software and Digital Content occupations) workers embedded in organizations in other industry sectors (Cunningham and Higgs 2009). Ben Goldsmith (2014, this volume) shows, for example, that the Financial Services sector is the largest employer of digital creative talent in Australia. But why should this be? We argue it is because ‘knowledge-based intangibles are increasingly the source of value creation and hence of sustainable competitive advantage (Mudambi 2008, 186). This value creation occurs primarily at the research and development (R and D) and the marketing ends of the supply chain. Both of these areas require strong creative capabilities in order to design for, and to persuade, consumers. It is no surprise that Jess Rodgers (2014, this volume), in a study of Australia’s Manufacturing sector, found designers and advertising and marketing occupations to be the most numerous creative occupations. Greg Hearn and Ruth Bridgstock (2013, forthcoming) suggest ‘the creative heart of the creative economy […] is the social and organisational routines that manage the generation of cultural novelty, both tacit and codified, internal and external, and [cultural novelty’s] combination with other knowledges […] produce and capture value’. 2 Moreover, the main “social and organisational routine” is usually a team (for example, Grabher 2002; 2004).

Dynamic Image-based visual servo control using centroid and optic flow features

This paper considers the question of designing a fully image-based visual servo control for a class of dynamic systems. The work is motivated by the ongoing development of image-based visual servo control of small aerial robotic vehicles. The kinematics and dynamics of a rigid-body dynamical system (such as a vehicle airframe) maneuvering over a flat target plane with observable features are expressed in terms of an unnormalized spherical centroid and an optic flow measurement. The image-plane dynamics with respect to force input are dependent on the height of the camera above the target plane. This dependence is compensated by introducing virtual height dynamics and adaptive estimation in the proposed control. A fully nonlinear adaptive control design is provided that ensures asymptotic stability of the closed-loop system for all feasible initial conditions. The choice of control gains is based on an analysis of the asymptotic dynamics of the system. Results from a realistic simulation are presented that demonstrate the performance of the closed-loop system. To the author's knowledge, this paper documents the first time that an image-based visual servo control has been proposed for a dynamic system using vision measurement for both position and velocity.

Model-predictive control and closed-loop stability considerations for nonlinear plants described by local ARX-type models

In this paper, a model-predictive control (MPC) method is detailed for the control of nonlinear systems with stability considerations. It will be assumed that the plant is described by a local input/output ARX-type model, with the control potentially included in the premise variables, which enables the control of systems that are nonlinear in both the state and control input. Additionally, for the case of set point regulation, a suboptimal controller is derived which has the dual purpose of ensuring stability and enabling finite-iteration termination of the iterative procedure used to solve the nonlinear optimization problem that is used to determine the control signal.

Dynamic positioning of marine craft using a port-Hamiltonian framework

Dynamic positioning of marine craft refers to the use of the propulsion system to regulate the vessel position and heading. This type of motion control is commonly used in the offshore industry for surface vessels, and it is also used for some underwater vehicles. In this paper, we use a port-Hamiltonian framework to design a novel nonlinear set-point-regulation controller with integral action. The controller handles input saturation and guarantees internal stability, rejection of unknown constant disturbances, and (integral-)input-to-state stability.

On the zeros of the Abelian integrals for a class of Liénard systems

A planar polynomial differential system has a finite number of limit cycles. However, finding the upper bound of the number of limit cycles is an open problem for the general nonlinear dynamical systems. In this paper, we investigated a class of Liénard systems of the form x'=y, y'=f(x)+y g(x) with deg f=5 and deg g=4. We proved that the related elliptic integrals of the Liénard systems have at most three zeros including multiple zeros, which implies that the number of limit cycles bifurcated from the periodic orbits of the unperturbed system is less than or equal to 3.

Deconstructing neurobiological coordination

Inherent indeterminacy of neurobiological systems has been revealed by research on coordination of multiarticular actions. We consider three important issues that these investigations raise for biomechanical measurement and performance modeling. These issues highlight the role of dynamic systems theory as a platform for integration of motor control and biomechanics in exercise and sports science.

Design of fixed order robust power oscillation damper for TCSC

This paper illustrates robust fixed order power oscillation damper design for mitigating power systems oscillations. From implementation and tuning point of view, such low and fixed structure is common practice for most practical applications, including power systems. However, conventional techniques of optimal and robust control theory cannot handle the constraint of fixed-order as it is, in general, impossible to ensure a target closed-loop transfer function by a controller of any given order. This paper deals with the problem of synthesizing or designing a feedback controller of dynamic order for a linear time-invariant plant for a fixed plant, as well as for an uncertain family of plants containing parameter uncertainty, so that stability, robust stability and robust performance are attained. The desired closed-loop specifications considered here are given in terms of a target performance vector representing a desired closed-loop design. The performance of the designed controller is validated through non-linear simulations for a range of contingencies.

Context-resonant systems perspectives in career theory

"The focus of this chapter is on context-resonant systems perspectives in career theory and their implications for practice in diverse cultural and contextual settings. For over two decades, the potential of systems theory to offer a context-resonant approach to career development has been acknowledged. Career development theory and practice, however, have been dominated for most of their history by more narrowly defined theories informed by a trait-and-factor tradition of matching the characteristics of individuals to occupations. In contrast, systems theory challenges this parts-in-isolation approach and offers a response that can accommodate the complexity of both the lives of individuals and the world of the 21st century by taking a more holistic approach that considers individuals in context. These differences in theory and practice may be attributed to the underlying philosophies that inform them. For example, the philosophy informing the trait-and-factor theoretical position, logical positivism, places value on: studying individuals in isolation from their environments; content over process; facts over feelings; objectivity over subjectivity; and views individual behavior as observable, measurable, and linear. In practice, this theory base manifests in expert-driven practices founded on the assessment of personal traits such as interests, personality, values, or beliefs which may be matched to particular occupations. The philosophy informing more recent theoretical positions, constructivism, places value on: studying individuals in their contexts; making meaning of experience through the use of subjective narrative accounts; and a belief in the capacity of individuals known as agency. In practice, this theory base manifests in practices founded on collaborative relationships with clients, narrative approaches, and a reduced emphasis on expert-driven linear processes. Thus, the tenets of constructivism which inform the systems perspectives in career theory are context-resonant. Systems theory stresses holism where the interconnectedness of all elements of a system is considered. Systems may be open or closed. Closed systems have no relationship with their external environment whereas open systems interact with their external environment and are open to external influence which is necessary for regeneration. Congruent with general systems theory, the systems perspectives emerging within career theory are based on open systems. Such systems are complex and dynamic and comprise many elements and subsystems which recursively interact with each other as well as with influences from the surrounding environment. As elements of a system should not be considered in isolation, a systems approach is holistic. Patterns of behavior are found in the relationships between the elements of dynamic systems. Because of the multiplicity of relationships within and between elements of subsystems, the possibility of linear causal explanations is reduced. Story is the mechanism through which the relationships and patterns within systems are recounted by individuals. Thus the career guidance practices emanating from theories informed by systems perspectives are inherently narrative in orientation. Narrative career counseling encourages career development to be understood from the subjective perspective of clients. The application of systemic thinking in practice takes greater account of context. In so doing, practices informed by systems theory may facilitate relevance to a diverse client group in diverse settings. In a world that has become increasingly global and diverse it seems that context-resonant systems perspectives in career theory are essential to ensure the future of career development. Translating context-resonant systems perspectives into practice offers important possibilities for methods and approaches that are respectful of diversity."--publisher website

Information for regulating action in sport : metastability and emergence of tactical solutions under ecological constraints

The aims of this chapter are twofold. First, we show how experiments related to nonlinear dynamical systems theory can bring about insights on the interconnectedness of different information sources for action. These include the amount of information as emphasised in conventional models of cognition and action in sport and the nature of perceptual information typically emphasised in the ecological approach. The second aim was to show how, through examining the interconnectedness of these information sources, one can study the emergence of novel tactical solutions in sport; and design experiments where tactical/decisional creativity can be observed. Within this approach it is proposed that perceptual and affective information can be manipulated during practice so that the athlete's cognitive and action systems can be transposed to a meta-stable dynamical performance region where the creation of novel action information may reside.

UAS mission path planning system (MPPS) using hybrid-game coupled to multi-objective optimiser

This paper presents the application of advanced optimization techniques to unmanned aerial system mission path planning system (MPPS) using multi-objective evolutionary algorithms (MOEAs). Two types of multi-objective optimizers are compared; the MOEA nondominated sorting genetic algorithm II and a hybrid-game strategy are implemented to produce a set of optimal collision-free trajectories in a three-dimensional environment. The resulting trajectories on a three-dimensional terrain are collision-free and are represented by using Bézier spline curves from start position to target and then target to start position or different positions with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of adding a hybrid-game strategy to a MOEA and for a MPPS.