Prathyush P. Menon

Clearance of Nonlinear Flight Control Laws Using Hybrid Evolutionary Optimization

The application of two evolutionary optimization methods, namely, differential evolution and genetic algorithms, to the clearance of nonlinear flight control laws for highly augmented aircraft is described. The algorithms are applied to the problem of evaluating a nonlinear handling quality clearance criterion for a simulation model of a high-performance aircraft with a delta canard configuration and a full-authority flight control law. Hybrid versions of both algorithms, incorporating local gradient-based optimization, are also developed and evaluated. Statistical comparisons of computational cost and global convergence properties reveal the benefits of hybridization for both algorithms. The differential evolution approach in particular, when appropriately augmented with local optimization methods, is shown to have significant potential for improving both the reliability and efficiency of the current industrial flight clearance process

Brief paper: Decentralised static output feedback stabilisation and synchronisation of networks

In this paper global stabilisation of a complex network is attained by applying local decentralised output feedback control to a minimum number of nodes of the network. The stabilisation of the network is treated as a rank constrained problem. Strict positive realness conditions on the node level dynamics allow nonlinearities/uncertainties which satisfy the sector conditions to be considered. A network of Chua oscillators with 75 nodes is considered to demonstrate the efficacy of the approach.

A distributed control law with guaranteed LQR cost for identical dynamically coupled linear systems

This paper considers a collection of agents per forming a shared task making use of relative information communicated over an information network. A two step design procedure for distributed state feedback control of such systems is proposed. The control law is guaranteed to provide a certain level of performance in terms of an LQR cost at a network level. An analysis of the proposed control law in the presence of delays in the relative information is carried out to obtain a bound on the maximum delay that can be accommodated.

Anti-windup synthesis for nonlinear dynamic inversion controllers

Abstract An anti-windup compensation method is proposed for a class of constrained nonlinear systems which, in the absence of saturation, are controlled by certain types of nonlinear dynamic inversion controllers. The anti-windup compensation scheme shares a similar architecture to that proposed by the authors in prior work for linear systems subject to saturation constraints. An encouraging aspect of the proposed scheme is that for globally exponentially stable systems, a particularly simple choice of anti-windup compensator exists and, moreover, this could be regarded as a “nonlinear„ internal model control based anti-windup compensator. More generally, a framework for synthesising optimal anti-windup compensators is suggested, based on nonlinear partial differential matrix inequalities. Finally, a simple example illustrates the effectiveness of the scheme.

Distributed fuzzy tracking for multi-agent systems

This paper proposes a systematic analysis for a stabilisation/tracking problem in a network of nonlinear systems. A reasonably general class of nonlinear systems is considered in their equivalent Takagi-Sugeno (TS) representation. Due to the structure, properties and reduced mathematical complexity of the TS approach, a decoupling of the network into node level dynamics is achieved which simplifies the stability analysis and the design of a control law. A distributed control law is introduced which is composed of both node and network level information. The design involves two stages. Firstly feedback gains are synthesized for the isolated agents ignoring interconnections. Then utilising a common Lyapunov matrix, from the first step, using the relative differences in the states of the agents at network level, co-operative behaviour among the agents is introduced. This is all performed subject to design criteria, posed as Linear Matrix Inequalities (LMIs). The benefits of this analysis is that the design of the controller is decoupled from the size and topology of the network, and it allows a convenient choice of feedback gains for the term that is based on the relative state information. An illustrative example is included to outline the potential of the analysis.

Cooperative tracking for a swarm of Unmanned Aerial Vehicles: A distributed Tagaki-Sugeno fuzzy framework design

This paper focuses on a systematic analysis for the tracking problem in swarm-based missions for Unmanned Aerial Vehicles (UAVs) with linear and angular velocity constraints. In this paper the nonlinear model of the dynamics are represented by Takagi-Sugeno (TS) fuzzy models. A distributed control law is introduced which is composed of both node and network level information. Firstly feedback gains are synthesised for the isolated UAVs ignoring interconnections. The resulting common Lyapunov matrix is utilised at network level, to incorporate into the control law the relative differences in the states of the agents, to induce cooperative behaviour. Eventually stability is guaranteed for the entire swarm. The control synthesis is all performed subject to design criteria, posed as Linear Matrix Inequalities (LMIs). An illustrative example based on a UAV tracking scenario is included to outline the potential of the analysis.

A systems engineering approach to validation of a pulmonary physiology simulator for clinical applications

Physiological simulators which are intended for use in clinical environments face harsh expectations from medical practitioners; they must cope with significant levels of uncertainty arising from non-measurable parameters, population heterogeneity and disease heterogeneity, and their validation must provide watertight proof of their applicability and reliability in the clinical arena. This paper describes a systems engineering framework for the validation of an in silico simulation model of pulmonary physiology. We combine explicit modelling of uncertainty/variability with advanced global optimization methods to demonstrate that the model predictions never deviate from physiologically plausible values for realistic levels of parametric uncertainty. The simulation model considered here has been designed to represent a dynamic in vivo cardiopulmonary state iterating through a mass-conserving set of equations based on established physiological principles and has been developed for a direct clinical application in an intensive-care environment. The approach to uncertainty modelling is adapted from the current best practice in the field of systems and control engineering, and a range of advanced optimization methods are employed to check the robustness of the model, including sequential quadratic programming, mesh-adaptive direct search and genetic algorithms. An overview of these methods and a comparison of their reliability and computational efficiency in comparison to statistical approaches such as Monte Carlo simulation are provided. The results of our study indicate that the simulator provides robust predictions of arterial gas pressures for all realistic ranges of model parameters, and also demonstrate the general applicability of the proposed approach to model validation for physiological simulation.

General Anti-windup synthesis for input constrained nonlinear systems controlled using nonlinear dynamic inversion

A general anti-windup compensation scheme is provided for a class of input constrained nonlinear systems. The controller considered is an inner-loop nonlinear dynamic inversion controller, augmented with an outer-loop linear controller, of arbitrary structure. It is shown that for globally exponentially stable plants, there exists a simple choice of anti-windup compensator ensuring closed-loop stability and guaranteed L2 performance. A framework for synthesising an optimal anti-windup compensator is proposed, based on nonlinear partial differential inequalities. The application of the theory to a dual tank control system simulation exemplifies the performance of the proposed scheme

Simultaneous source localization and boundary mapping for contaminants

This paper addresses the problem of localizing the sources of contaminants spread in the environment, and mapping the boundary of the affected region using an innovative swarm intelligence based technique. Unlike most work in this area the algorithm is capable of localizing multiple sources simultaneously while also mapping the boundary of the contaminant spread. At the same time the algorithm is suitable for implementation using a mobile robotic sensor network. Two types of agents, called the source localization agents (or S-agents) and boundary mapping agents (or B-agents) are used for this purpose. The paper uses the basic glowworm swarm optimization (GSO) algorithm, which has been used only for multiple signal source localization, and modifies it considerably to make it suitable for both these tasks. This requires the definition of new behaviour patterns for the agents based on their terminal performance as well as interactions between them that helps the swarm to split into subgroups easily and identify contaminant sources as well as spread along the boundary to map its full length. Simulations results are given to demonstrate the efficacy of the algorithm.

Optimization of Mechanical Ventilator Settings for Pulmonary Disease States

The selection of mechanical ventilator settings that ensure adequate oxygenation and carbon dioxide clearance while minimizing the risk of ventilator-associated lung injury (VALI) is a significant challenge for intensive-care clinicians. Current guidelines are largely based on previous experience combined with recommendations from a limited number of in vivo studies whose data are typically more applicable to populations than to individuals suffering from particular diseases of the lung. By combining validated computational models of pulmonary pathophysiology with global optimization algorithms, we generate in silico experiments to examine current practice and uncover optimal combinations of ventilator settings for individual patient and disease states. Formulating the problem as a multiobjective, multivariable constrained optimization problem, we compute settings of tidal volume, ventilation rate, inspiratory/expiratory ratio, positive end-expiratory pressure and inspired fraction of oxygen that optimally manage the tradeoffs between ensuring adequate oxygenation and carbon dioxide clearance and minimizing the risk of VALI for different pulmonary disease scenarios.