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Offshore wind energy capitalizes on the higher and less turbulent wind speeds at sea. The use of floating structures for deeper waters is being explored. The control objective is a tradeoff between power capture and fatigue, especially that produced by the oscillations caused by the reduced structural stiffness of a floating installation in combination with a coupling between the fore-aft motion of...
This paper deals with the stabilization and the H2 control synthesis of Linear Parameter Varying (LPV) descriptor systems. The compensator design problem is examined through a parameter-dependent observer-based control structure. The proposed approach uses parameter-dependent Lyapunov functions and involves slack variables in order to achieve less restrictive synthesis conditions. These synthesis...
We study the adaptive controller design for a special class of MIMO system, which is composed of arbitrary but a finite number of SISO linear subsystems under noisy output measurements and partly measured disturbances sequentially interconnected with additional feedbacks. We first formulate the robust adaptive control problem as a nonlinear H∞ control problem under imperfect state measurement, derive...
This paper deals with the connectivity preservation of multi-agent systems with state-dependent error in distance measurement. It is assumed that upper bounds on the measurement error and also its rate of change as a function of distance are available. A general class of distributed control strategies is then proposed for the distance-dependent connectivity preservation of the agents in the network...
A framework and stability conditions are presented for the analysis of stability of three different classes of dynamic artificial neural networks: (1) neural state space models, (2) global input-output models, and (3) dynamic recurrent neural networks. The models are transformed into a standard nonlinear operator form for which linear matrix inequality-based stability analysis is applied. Theory and...
The three main classes of dynamic artificial neural network models for identification of nonlinear dynamical systems are reviewed: (1) neural state-space models, (2) global input-output models, (3) dynamic recurrent neural network models. The presentation of the mathematical models and architectures are followed by their representations in terms of a consistent block diagram convenient for stability...
This paper presents a new robust stabilization method for a class of uncertain nonlinear systems[UNS] via piecewise linear differential inclusions[PWLDI]. We propose several effective numerical methods for computing the PWLDI to cover the UNS, then present controller design method of obtained PWLDI via piecewise quadratic Lyapunov function to realize robust stability. Because of the inclusion relationship...
This paper presents a novel approach to robust model predictive control (MPC) for LTI discrete time systems subject to model uncertainty and additive disturbances. By exploiting recent results in random convex programming (RCP), a randomization approach is used and it is shown that the resulting state-feedback control law achieves asymptotic closed loop stability and constraint satisfaction, up to...
μ-analysis has for many years been the de facto standard robustness analysis tool in a wide variety of control applications. To use μ-analysis, the uncertain system must be cast in the form of a linear fractional transformation (LFT). Once it is appropriately transformed, a variety of algorithms are available to calculate the upper and the lower bounds on μ. Several difficulties arise during this...
We summarize some recent results on the dynamic stability of wholesale electricity markets. We discuss how to construct control-theoretic market analysis frameworks using concepts of market efficiency, Lyapunov stability, and predictive control. Such frameworks can be used to design, analyze, and monitor the stability and robustness properties of different market designs. In particular, we discuss...
Uncertain systems is a fundamental area of automatic control. This paper addresses robust stability of uncertain linear systems with rational dependence on unknown time-varying parameters constrained in a polytope. For this problem, a new sufficient condition based on the search for a common homogeneous polynomial Lyapunov function is proposed through a particular representation of parameter-dependent...
We propose a “smart parking” system for an urban environment based on a dynamic resource allocation approach. The system assigns and reserves an optimal resource (parking space) for a user (driver) based on the user's objective function that combines proximity to destination with parking cost, while also ensuring that the overall parking capacity is efficiently utilized. Our approach solves a Mixed...
The numerical solution of optimal control problems is important in a variety of industries. One of the popular methods for complex problems is direct transcription. Direct transcription does not rely on the necessary conditions and it is easy to incorporate a wide variety of constraints on both controls and states. Optimal control problems can often involve delays in the state or the control or both...
This paper addresses the design problem of Gain-Scheduled Output-Feedback (GSOF) controllers for discrete-time Linear Parameter-Varying (LPV) systems. In our problem setting, the scheduling parameters are supposed to be provided with some uncertainties. The LPV systems as well as the GSOF controllers to be designed are supposed to be parametrically affine. Two design problems, i.e. H∞-type and H2-type...
The development of amine scrubbing for coal and natural gas-fired power plants represents a key technology to reduce CO2 emissions. Among the strategies required to maximize CO2 capture during plant operations is the design of tailor-made dynamic models for optimal control. This paper presents a novel application of subspace system identification to a CO2 recovery plant, where major decision variables...
We consider an optimal control problem for networked, linear systems, where the loop is closed via a lossy network with a lossy acknowledgment mechanism. So, the data send from the controller to the actuators might be lost. In these cases often specific strategies called input scheme are used to choose backup inputs. In this work we employ an extended class of such strategies. We derive the necessary...
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