Decentralized optimal control of a group of dynamical objects

The problem of optimal control of a group of coupled dynamical objects is considered. The cases are examined in which the centralized control of a group of objects is impossible. Fast real-time optimal control algorithms of each of the dynamical systems are described that use information exchanged between group members in the course of control. The proposed methods supplement the earlier developed real-time optimal control methods for an individual dynamical system. The results are illustrated using optimal control of two coupled mathematical pendulums as an example.     

Methods for constructing observers for linear dynamical systems under uncertainty

We consider various methods for constructing asymptotic observers for linear stationary MIMO systems under an unknown external perturbation. Square systems (in which the number of measured outputs coincides with that of unknown inputs) are considered separately. For such systems we propose a method for constructing observers that yields either an asymptotic observer (for systems of maximal relative order) or an arbitrarily accurate observer. Hyperoutput systems (in which the number of measured outputs exceeds that of unknown inputs) are considered separately as well. For such systems we propose a method for synthesizing asymptotic observers of the phase vector.     

Discrete mechanics and mixed integer optimal control of dynamical systems

Mixed integer optimal control problems are a generalization of ordinary optimal control problems that include additional integer valued control functions. The integer control functions are used to switch instantaneously from one system to another. We use a time transformation (similar as in [1]) to get rid of the integer valued functions. This allows to apply gradient based optimization methods to approximate the mixed integer optimal control problem. The time transformation from [1] is adapted such that problems with distinct state domains for each system can be solved and it is combined with the direct discretization method DMOC [2,3] (Discrete Mechanics and Optimal Control) to approximate trajectories of the underlying optimal control problems. Our approach is illustrated with the help of a first example, the hybrid mass oscillator. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Decentralized iterative learning control for a class of large scale interconnected dynamical systems

The problem of decentralized iterative learning control for a class of large scale interconnected dynamical systems is considered. In this paper, it is assumed that the considered large scale dynamical systems are linear time-varying, and the interconnections between each subsystem are unknown. For such a class of uncertain large scale interconnected dynamical systems, a method is presented whereby a class of decentralized local iterative learning control schemes is constructed. It is also shown that under some given conditions, the constructed decentralized local iterative learning controllers can guarantee the asymptotic convergence of the local output error between the given desired local output and the actual local output of each subsystem through the iterative learning process. Finally, as a numerical example, the system coupled by two inverted pendulums is given to illustrate the application of the proposed decentralized iterative learning control schemes.     

Decentralized robust control for a class of uncertain large-scale interconnected nonlinear dynamical systems

The problem of the decentralized robust control for a class of large-scale interconnected nonlinear dynamical systems with input interconnection and external interconnection perturbations is considered. Based on the stabilizability of each nominal isolated subsystem (i.e., the isolated subsystem in the absence of interconnection perturbations), a class of decentralized local state feedback controllers is proposed, and some sufficient conditions are derived by making use of the Lyapunov stability criterion such that uncertain large-scale interconnected systems can be stabilized asymptotically by these decentralized state feedback controllers. For large-scale systems with only input interconnection perturbations, such decentralized controllers become a class of decentralized stabilizing state feedback controllers. That is, the decentralized stability of such large-scale systems can be guaranteed always by using the decentralized state feedback controllers proposed in the paper. Finally, a numerical example is given to demonstrate the validity of the results.     

Synthesis of controllers for damping oscillations in dynamical systems under uncertainty

We review the modern approaches to the synthesis of robust H _∞ controllers that ensure optimal damping of oscillations in dynamical systems under uncertainty. In the synthesis method based on Riccati equations, these many-parameter equations can be solved only when the parameters are contained in a bounded parallelepiped with given boundaries. The synthesis of a robust H _∞ output control for systems with unknown bounded parameters is reducible to the solution of an optimization problem constrained by a system of linear matrix inequalities. The proposed controller synthesis algorithms are implemented using standard MATLAB procedures. The efficiency of the proposed methods and algorithms is demonstrated in application to optimal damping of oscillations in a parametrically excited pendulum. __________ Translated from Nelineinaya Dinamika i Upravlenie, No. 4, pp. 87–104, 2004.     

State estimation of linear dynamical systems under bounded control

The problem of estimation of all possible states that a linear system under bounded control may take (namely, the reachable or attainable set) is addressed. A number of previously developed Lyapunov techniques for estimating the reachable set of ann-dimensional linear system are extended and compared. The techniques produce over-estimates in the form ofn-dimensional ellipsoids. Illustrative examples are solved.This work was supported by the Natural Sciences and Engineering Research Council of Canada, Operating Grant Nos. A-0621 and A-4080, and by Research Personnel Support from the Dean of Science, Memorial University of Newfoundland, Canada.     

Modelling of uncertainty in discrete dynamical systems

Dynamic systems that are not Gaussian, stationary and linear are difficult to model by full probabilistic analysis. Sufficient information for practical application can often be obtained by second moment analysis, described in the paper. Alternatively, second moment analysis can be performed using point distributions. Two new methods in this class, one exact for linear systems and one approximate, are described. Examples show the application and illustrate the accuracy.     

Fractional terminal sliding mode control design for a class of dynamical systems with uncertainty

A novel type of control strategy combining the fractional calculus with terminal sliding mode control called fractional terminal sliding mode control is introduced for a class of dynamical systems subject to uncertainties. A fractional-order switching manifold is proposed and the corresponding control law is formulated based on the Lyapunov stability theory to guarantee the sliding condition. The proposed fractional-order terminal sliding mode controller ensures the finite time stability of the closed-loop system. Finally, numerical simulation results are presented and compared to illustrate the effectiveness of the proposed method.     

Sensitivity analysis of linear dynamical systems in uncertainty quantification

We consider linear dynamical systems including random parameters for uncertainty quantification. A sensitivity analysis of the stochastic model is applied to the input-output behaviour of the systems. Thus the parameters that contribute most to the variance are detected. Both intrusive and non-intrusive methods based on the polynomial chaos yield the required sensitivity coefficients. We use this approach to analyse a test example from electrical engineering. (© 2013 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Synthesis of the optimal control of dynamical structures

The problem of synthesizing an optimal control by choosing the structure, in non-linear dynamical systems a with random structure, is formulated. One of the possible approaches to solving this problem is considered: it uses a method from the theory of the optimal control of systems with distributed parameters and enables one to construct the density vector of the distributions of the process under consideration for all states in such a way as to guarantee an optimum of the selected probability functional. An example is given to illustrate the practical possibilities of the approach.     

Computation of alternated reachability tubes for linear control systems under uncertainty

The problem of constructing the reachability domain for linear control system in a presence of geometrically bounded unknown disturbance is considered. A high actuality of the problem for engineering applications requires an efficient calculation technique for the reach sets in a class of closed-loop control. The technique suggested in the article is based on the ellipsoidal approximations developed by A.B. Kurzhansky for the alternated reachability domains. In the article these estimates are complemented with an adaptive regularization to guarantee the continuability of the ellipsoidal estimates. The quadratic structure of the regularization combines well with an ellipsoidal nature of the estimates thus making it possible to adjust the existing ellipsoidal estimation schema in a transparent fashion for achieving the continuable and non-singular estimates via adaptive choice of regularization parameters.     

The optimal harvesting problem under price uncertainty

In this paper we study the exploitation of a one species forest plantation when timber price is governed by a stochastic process. The work focuses on providing closed expressions for the optimal harvesting policy in terms of the parameters of the price process and the discount factor, with finite and infinite time horizon. We assume that harvest is restricted to mature trees older than a certain age and that growth and natural mortality after maturity are neglected. We use stochastic dynamic programming techniques to characterize the optimal policy and we model price using a geometric Brownian motion and an Ornstein–Uhlenbeck process. In the first case we completely characterize the optimal policy for all possible choices of the parameters. In the second case we provide sufficient conditions, based on explicit expressions for reservation prices, assuring that harvesting everything available is optimal. In addition, for the Ornstein–Uhlenbeck case we propose a policy based on a reservation price that performs well in numerical simulations. In both cases we solve the problem for every initial condition and the best policy is obtained endogenously, that is, without imposing any ad hoc restrictions such as maximum sustained yield or convergence to a predefined final state.     

Monotonic dynamical systems under spatial discretization

We estimate the probability of replicating the asymptotic behaviour of a dynamical system generated by a monotonic mapping for randomly centered roundoff lattices.

     

Decentralized robust control design for uncertain delay systems

We consider a class of large-scale uncertain delay systems. The uncertain parameter vector in the system is possibly fast time-varying. It may be nonlinear in the system dynamics. No statistical or fuzzy information of the uncertainty is known. Based on only the possible bound of the uncertain parameter, a decentralized linear robust control is proposed, which renders the system asymptotically stable.