In this paper, a distributed resource allocation problem is considered where multiple agents want to allocate network resources among themselves while optimizing certain performance index. The first continuous-time distributed second-order gradient algorithm is proposed for resource allocation over static (non-switching) graphs under synchronous protocol. The algorithm is able to converge to the optimal solution of the problem with exponential convergence rate under suitable assumptions. Finally, a numerical example of a distributed estimation in wireless sensor networks is given by using the algorithm in order to demonstrate the results.

In this paper, distributed consensus-based optimization and network resource allocation problem are considered, where agents make decisions using local information in the presence of random communication topologies. Distributed algorithms are proposed for the two problems such that the algorithms are both asynchronous and totally asynchronous. The algorithms do not require diminishing step sizes and are able to converge almost surely and in mean square without requiring a priori B-connectivity or distribution assumption of switching graphs. The algorithms are able to converge even if weighted matrix of the graph is periodic and irreducible in synchronous protocol. To the best knowledge of the authors, the proposed distributed algorithm for resource allocation is the first algorithm which is both asynchronous and totally asynchronous over random networks. Finally, a numerical example of distributed estimation in wireless sensor networks is provided in order to illustrate the results.

In this paper, collaborative optimization of sum of convex functions is considered where agents make decision using local information over networks subject to globally coupled affine equality and inequality constraints. In this problem, the globally coupled equality and inequality constraints’ information is only partially accessible to each agent. The first discrete-time fully parallel distributed algorithm without diminishing step sizes, (sub)gradient, and/or solving a sub-problem at each time is derived based on monotone operator splitting approach. In the algorithm, the updates of variables happen independent of each other that results in reducing computational time per iteration significantly. The algorithm can converge to an optimal solution for any convex cost functions and any convex constraint sets of agents with arbitrary initialization over any undirected static (non-switching) networks in synchronous protocol. As an application of the problem, solving linear algebraic equations (LAEs) of the form Ax = b among m agents is considered where each agent only knows the partitioned matrix [A i , b i ] such that $A = \left( {\Sigma _{i = 1}^m{A_i}} \right)$ and $b = \left( {\Sigma _{i = 1}^m{b_i}} \right)$. The algorithm for LAEs is able to converge to an optimal solution for any matrices A and b.

In this paper, an unconstrained collaborative optimization of a sum of convex functions is considered where agents make decisions using local information from their neighbors. The communication between nodes are described by a random sequence of possibly state-dependent weighted networks. It is shown that the state-dependent weighted random operator of the graph has quasi-nonexpansivity property, and therefore the operator does not need the distribution of random communication topologies. Hence, it includes random networks with/without asynchronous protocols. As an extension of the problem, a more general mathematical optimization problem than that of the literature is defined, namely minimization of a convex function over the fixed-value point set of a quasi-nonexpansive random operator. A discrete-time algorithm using diminishing step size is given which can converge almost surely to the global solution of the optimization problem under suitable assumptions. Consequently, as a special case, the algorithm reduces to a totally asynchronous algorithm without requiring distribution dependency or B-connectivity assumption for the distributed optimization problem. The algorithm still works in the case where weighted matrix of the graph is periodic and irreducible in a synchronous protocol.

In this paper, relations between distributed consensus-based optimization and a network resource allocation problem are considered. It is shown that first-order gradient algorithm for distributed consensus-based optimization can be used for finding an optimal solution of distributed resource allocation with synchronous protocol under weaker assumptions than those given in the literature. Moreover, second-order gradient algorithm for distributed consensus-based optimization is presented that can be employed for solving distributed resource allocation problems. As a result, several algorithms used for distributed consensus-based optimization can now be applied to derive distributed algorithms for resource allocation. It is shown that first and second order gradient algorithms for distributed resource allocation can be utilized for finding an optimal solution of distributed consensus-based optimization as well. The results presented in this paper can be applied to time-varying or random directed networks with or without synchronous protocols with arbitrary initialization. Finally, a numerical example of a distributed estimation in wireless sensor networks is given to demonstrate the results.

In this paper, semistability and stochastic semistability for switched discrete-time nonlinear systems are considered. The main results of this paper involve sufficient conditions for (stochastic) semistability of discrete-time nonlinear/linear dynamical systems under time-varying or random switching by means of Fixed Point Theory, that has not been considered in the literature. As an application of the results, an iterative algorithm is derived for constrained distributed consensus over random multi-agent networks. The algorithm is a totally asynchronous algorithm without requiring B-connectivity or distribution dependency of switching graphs. The algorithm is able to converge even if the weighted matrix of the graph is periodic and irreducible under synchronous protocol.

In this paper, a centralized two-block separable optimization is considered for which a fully parallel primal-dual discrete-time algorithm with fixed step size is derived based on monotone operator splitting method. In this algorithm, the primal variables are updated in an alternating fashion like Alternating Direction Method of Multipliers (ADMM). However, unlike existing discrete-time algorithms such as Method of Multipliers (MM), ADMM, Bi-Alternating Direction Method of Multipliers (BiADMM), and Primal-Dual Fixed Point (PDFP) algorithms, that all suffer from sequential updates, all primal and dual variables are updated in parallel in the sense that to update a variable at each time, updated version of other variable(s) is not required. One of advantages of the proposed algorithm is that its direct extension to multi-block optimization is still convergent. Then the method is applied to distributed optimization for which a fully parallel primal-dual distributed algorithm is obtained. Finally, since direct extension of ADMM may diverge for multi-block optimization, a numerical example of a three-block optimization is given for which the direct extension of the proposed algorithm is shown to converge to a solution.

Modern Flight Control Systems consist of aerodynamic control surfaces and/or the engines' nozzles, actuators, sensors, a sampler and ZOH device, and compensators. The control effectors' actuator dynamics play an important role in flight control. Tracking control in particular in the presence of actuator saturation requires a time-domain approach. Therefore, the objective is the derivation of a receding horizon/model predictive tracking control law, in the case where both fast and slow state variables feature in the plant model. The optimal control solution of the tracking control problem requires advance knowledge of the exogenous reference signal. To apply model predictive tracking control to dynamical systems with time-scale separation, the classical nested feedback control loop structure of the frequency domain is transcribed into the time domain. Using feedback of the gyro signals, motors apply moments to the gyros to make the angular velocity of the platform zero.

This paper presents an linear quadratic Gaussian (LQG)-based robust control strategy for active noise reduction in a 3D enclosure wherein acoustic-structure interaction dynamics is present. The acoustic disturbance is created by the piezo-actuated vibrating boundary surface of the enclosure. The control signal is generated by the speaker which is noncollocated with the sensing microphone mounted inside the enclosure. The dynamic model of the system is obtained using frequency-domain system identification techniques. The state weighting matrix in the LQG cost function is determined analytically in the closed-form which allows the control designer to directly penalize the total acoustic energy of the system. The robustness of the controller is also ensured to guarantee the closed-loop stability against the unmodeled dynamics and parametric uncertainties. Simulation and experiment results are given which demonstrate the effectiveness of the proposed control methodology.

Dynamics of flutter is an important consideration in the design of aircraft structures. Flutter is an unstable self-excitation of the structure due to an undesirable coupling of structural elasticity and aerodynamics. Flutter is very difficult to predict and its occurrence can lead to catastrophic structural failure. The dynamics of flutter are affected by several factors including nonlinearities in structural stiffness, damping, and free-play in control surfaces. The free-play nonlinearity in control surfaces mechanisms is similar to the backlash in gears. Such nonlinearity introduces persistent limit cycle oscillations (LCO's) and significantly affects the onset of flutter. The impact of free-play on the flutter speed and frequency is not fully understood and is an active area of research. Historically, very conservative estimates have been used for the allowable free-play. The current military specification limit for free-play is based on the wind tunnel tests performed in 1950's at the Wright Air Development Center (WADC). The key contribution of this paper lies in gaining deeper understanding of free-play dynamics to enable more accurate modeling of free-play and predict its impact on flutter speed and frequency. The proposed modeling methodology is validated via close agreement of the simulation with WADC test data. Energy-based novel approach is presented for life cycle assessment and to predict flutter instability.

Scramjet powered hypersonic vehicles represent the next critical step toward achieving NASA's vision for Highly Reliable Reusable Launch Systems (HRRLS), affordable space access, planetary re-entry systems, and global reach vehicles. The design of such vehicles is a very interdisciplinary and highly complex problem. As such, the development of varying fidelity mathematical models for assessing overall stability and performance during the design process is very important. In particular, developing "low-order" models with "sufficient fidelity" to capture control-centric phenomena becomes vital in early stages of vehicle design. Historically, the early stage vehicle design process never incorporated control related considerations. The design obtained by such practice is not optimal and can often lead to poor design from a stability and performance view point. This paper presents control-relevant modeling efforts which will facilitate quick iterative control analysis and design during early stages of vehicle design. The paper is intended to be of an introductory nature and presents the high level modeling framework and associated challenges. An example linear 6 DOF model with some representative analysis is also given to demonstrate the applicability of the tool suite.

This paper presents a novel concept in active pneumatic vibration isolation. The novelty in the concept is in utilizing an air-spring-orifice-accumulator combination to vary the natural frequency as well as inject damping into the system per requirement, thereby eliminating the need for a hydraulic cylinder or a magnetorheological damper This continuously variable natural frequency and damping (CVNFD) technology is aimed at achieving active vibration isolation. For analysis purposes, a particular application in the form of pneumatic seat suspension for off-road vehicles is chosen. A mathematical model representing the system is derived rigorously from inertial dynamics and first principles in thermodynamics. Empirical corelations are also used to include nonlinearities such as friction that cannot be accounted for in the thermodynamic equations. An exhaustive computational study is undertaken to help understand the physics of the System. The computational study clearly depicts the CVNFD capability of the vibration isolation system. An experimental test rig is built to experimentally validate analytical and simulation modeling of the system. Experimental verification corroborated the variable natural frequency and damping characteristic of the system observed through computational simulations.

This paper presents a novel concept of active pneumatic suspension which uses an accumulator, airspring, and smart control system to actuate a variable orifice mechanism which connects the airspring to the accumulator. The main objective is to obtain a linear model for this pneumatic suspension so as to use existing advanced linear control methodologies to ascertain the closed loop performance and robustness. The two control designs investigated are a robustified LQG and mixed sensitivity H/spl infin/ control. The controllers designed (H/spl infin/ in particular) are shown to provide good performance enhancement over open loop with regard to vibration isolation while guaranteeing a desired degree of robustness to modelling uncertainties.

This paper presents multi-input multi-output (MIMO) generalized predictive control (GPC) law and its application to reconfigurable control. A method to compute the desired end-point state from the desired output and end-point state weighting matrix is given. In particular, an application to flight control with actuator saturation failure is presented. A numerical simulation using a short-period approximation model of a civil transport aircraft is presented to demonstrate the reconfigurable control architecture.

This paper presents several robust control designs for a MIMO morphing airfoil concept. A linear aeroelastic model of the morphing wing is utilized to design an H∞ controller and a two degree of freedom H∞ loop shaping controller that track commanded lift and roll moments. These methodologies are developed, along with the robustness conditions for input multiplicative, and real parametric uncertainty in ωn and ςn. The robustness of each of these controllers is then evaluated using these conditions and the structured singular value (SSV). Comments and discussion on these control methodologies are then drawn presented.

This paper addresses modelling, system identification, and control of acoustic-structure interaction dynamics for 3D enclosures. The specific configuration of interest is the one in which an acoustic disturbance is created by one of the vibrating boundaries of the enclosure. The mathematical modelling approach presented in this paper can be extended to model acoustic-structure interaction dynamics in many important real-life systems such as aircraft cabins. The transfer function and state-space models are also obtained in analytical form. The analytical model is compared with the experimental data. An alternate modelling approach using system identification techniques is also used. Differences in analytical and experimentally identified models are discussed and selected control design strategies are outlined for broadband reduction of noise.

This paper addresses passivity-based control of elastic systems. For inherently passive nonlinear and linear elastic systems such as flexible structures with collocated and compatible actuators and sensors, robust linear and nonlinear control laws are presented. For linear elastic systems that are inherently nonpassive, a method is presented to render them passive by compensation. The resulting system can then be controlled by a class of passive linear controllers. The method is applied to two elastic systems and is found to give robust stability and performance.

This paper presents modelling, system identification, simulation, and experimental results for passivity-based robust control of piezo-actuated flexible beam. The flexible beam configuration considered is a cantilever aluminum beam with a piezoelectric transducer used as the actuator and tip-accelerometer as the sensor. The actuator and sensor are non-collocated. The Lagrangian formulation is used to obtain mathematical model of the flexible link dynamics with piezo actuator. For control design purposes, a finite dimensional approximate model is derived using assumed modes approach. It is shown that the approximate model compares very well with the experimentally identified model. Since the system is inherently not passive, passification techniques are used to render the system robustly passive which enables the use of passivity-based feedback control design. The controller design is validated both in simulation as well as in experiments. The simulation and experimental results demonstrate the effectiveness of controller in suppressing the tip vibrations of the link. The controller design is shown to be robust to both parametric uncertainties and unmodeled dynamics.

This paper presents an LQG-based robust control strategy for a noise reduction in 3 -D enclosure wherein acoustic-structure interaction dynamics is present. The dynamic model of the system is obtained using frequency-domain system identification techniques. The state weighting matrix in the LQG cost function is determined analytically in the closed-form which allows the control designer to penalize the total acoustic energy of the system. The robustness of controller is ensured via an iterative LQG design that satisfies frequency-domain robustness condition. Simulation and experiment results are given which demonstrate the effectiveness of the controller.

An active feedback controller design methodology based on passivity-based robust control techniques is presented for broad-band noise control in an acoustic duct. The controller design methodology is demonstrated on an experimental one-dimensional (1-D) acoustic duct facility. The experimental results exhibit the effectiveness of the controller in suppressing acoustic noise levels over a broad frequency range without destabilizing high frequency dynamics of the system. The controller design is shown to be robust to unmodelled dynamics and parametric uncertainties. A finite dimensional mathematical model is derived for a 1-D acoustic duct using analytical as well as system identification techniques. It is shown that the theoretically determined model agrees very well with the experimentally identified system model. The control design methodology exploits inherent robustness of passivity-based controllers and selective mode attenuation capability of resonant mode controllers. The controller is easy to implement as it uses only output feedback. Moreover, the controller is also low-order, robust, broadband, and has guaranteed stability. (C) 2003 Institute of Noise Control Engineering.