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This paper presents a study of simultaneous input and state estimation for nonlinear dynamic systems. The problem considers both unknown input and state variables, where the inputs represent unknown signals driving or existing in a system, e.g., disturbances, system uncertainties or unmodeled dynamics. To deal with the problem, we will develop a set of ensemble-based filtering approaches in a Bayesian...
This paper investigates the behavior of causal Jacobi differentiator in online robot identification applications. The identification process is carried out online using robot dynamic model. Causal Jacobi differentiator is applied to compute the acceleration from the noisy position measurements, with an exact time delay. Moreover its frequency domain property is analyzed via a finite impulse response...
Interface control is an important area in applications of Domain Decomposition (DD) for linear advection-diffusion equations, since it attempts to minimize the errors committed by DD methods. In this work a localised control and estimation strategy, confined to selected sub-domains, that combines DD and filtering is proposed for linear non-stationary advection-diffusion equations. This approach mitigates...
An estimation approach is presented for an autonomous tethered kite system for the purpose of airborne wind energy generation. Accurate estimation of the kite state is critical to the performance of automatic flight controllers. We propose an estimation scheme which fuses measurements from range sensing, based on ultra-wideband radios, and inertial readings from an inertial measurement unit. Ranges...
In this paper, we present filtering algorithms for simultaneous input and state estimation of linear discrete-time stochastic systems when the unknown inputs are partially known, i.e., when some aggregate information of the unknown inputs is available as linear equality or inequality constraints. The stability and optimality properties of the filters are presented and proven using two complementary...
In this paper, we present recursive algorithms for linear discrete-time stochastic systems that simultaneously estimate the states and unknown inputs in an unbiased minimum-variance sense with a delay. By allowing potential delays in state estimation, the stricter assumptions in a previous work [1] can be relaxed. Moreover, we show that a system property known as strong detectability plays a key role...
Implementing a control law on an Unmanned Aerial Vehicle can be problematic when the existing autopilot system is incompatible with the controller structure. This paper extends the control strategy that incorporates a pre-existing autopilot system in a closed-loop control law design [1]. In [1], the control scheme explicitly involves the autopilot in the controller derivation, based on an assumption...
Self-propelled particle models have been used to study the collective behavior of animal groups such as fish schools and bird flocks, and these models have also been useful in designing control strategies for fleets of autonomous vehicles. In this paper, we construct a dynamical particle model inspired by mating swarms of wild mosquitoes. The model generates three different behaviors (swarming, velocity...
This paper presents a tracking error convergence proof for the multi-input multi-output direct model reference adaptive control problem. The proof is valid for square plants that are potentially nonminimum phase. This work is an extension of the surrogate tracking error adaptive control techniques previously developed, though the assumed plant structure is altered to accommodate a wider range of dynamics...
We propose a construction of Lyapunov functions for linear and bilinear infinite-dimensional integral input-to-state stable systems. In contrast to other constructions available in the literature, we do not impose strong restrictions on the type of the bilinear systems. The coercivity of such Lyapunov functions as well as a possible extension to systems with unbounded input operators are discussed.
This paper presents novel theoretical results on the exponential state estimation issue for Markovian jumping neural networks (MJNNs) with mixed time-varying delays and discontinuous activations. The jumping parameters are modeled as a continuous-time finite-state Markov chain. The nonlinear perturbation of the measurement equation are assumed to be locally Lipschitzian. By introducing triple-integral...
In this paper we present a new result on rejection of unmatched external disturbances on port-Hamiltonian systems using Control by Interconnection (CbI). The PHS structure is used to design a controller that rejects unmatched constant disturbances from non-passive outputs. In the PHS framework, the disturbance rejection problem has been addressed adding integral action and using a change of coordinates...
This paper investigates the finite horizon H∞ control problem for a class of nonlinear stochastic systems with multiple decision makers. First, it is shown that the H∞ controllers for the decision makers can be obtained by solving a dynamic Nash game problem. In order to find the H∞ controllers, necessary conditions for the existence of Nash equilibrium in the worst case disturbance, which consist...
This paper investigates finite-time horizon dynamic games for a class of nonlinear stochastic systems with multiple players. First, the necessary conditions for the existence of an open-loop Nash equilibrium are established using the stochastic maximum principle. Such conditions can be represented as the solvability conditions of cross-coupled forward-backward stochastic differential equations (CFBSDEs)...
In this paper, we provide a compositional framework for the construction of infinite approximations of interconnected stochastic control systems. Our approach is based on a notion of so-called stochastic simulation functions that are associated with interfaces. The stochastic simulation functions are used to quantify the approximation error while the interfaces are used to lift the controllers synthesized...
Investment diversification is believed to have potential adverse effects on systemic risk. This paper examines the performance of systemic risk with respect to two factors: the available diversification choices and the investor's ability to utilize them. Building on an existing stylized financial system model, we enrich it by partitioning the assets and the investors according to their accessibility...
Complete and rigorous foundations for basic thermodynamic laws from the statistical description of microscopic systems has been a long-standing goal for mathematicians and physicists alike since Boltzmann. In this paper, we show how Willems's dissipativity theory provides a convenient framework to study a physical system at both microscopic and macroscopic level, and suggests a natural storage function...
This work is concerned with stochastic consensus of multi-agent systems with additive measurement noises, where the information exchange among agents is described by a directed graph. By combining the algebraic graph theory, matrix theory and stochastic analysis, the stochastic weak and strong consensus are examined. For the case with general digraphs, we give the necessary and sufficient conditions...
In this paper, the optimal H2 control problem for spatially invariant systems is considered. The framework adopted is borrowed from the work pioneered by Voulgaris et al. In particular, the H2 optimal control problem is solved via the computation of an orthogonal projection of a tensor Hilbert space onto a particular subspace. Next, the optimal H2 decentralized control problem is solved by computing...
This work is concerned with the optimization aspects of networked distributed parameter systems. It is assumed that the information exchange between the networked systems, each of which is governed by an evolution equation in an abstract space, is a priori given and the design objective is to choose the leader and the synchronization controllers so that all the followers track the leader in an appropriate...
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