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Closed-loop Performance (closed-loop + performance)

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Engineering60%

Selected Abstracts

Adaptive model predictive control for co-ordination of compression and friction brakes in heavy duty vehicles

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 10 2006
Ardalan Vahidi
Abstract In this paper, an adaptive model predictive control scheme is designed for speed control of heavy vehicles. The controller co-ordinates use of compression brakes and friction brakes on downhill slopes. Moreover, the model predictive controller takes the actuator constraints into account. A recursive least square scheme with forgetting is used in parallel with the controller to update the estimates of vehicle mass and road grade. The adaptation improved the model predictive controller. Also online estimation of the road grade enhanced the closed-loop performance further by contributing through feedforward control. Simulations of realistic driving scenarios with a validated longitudinal vehicle model are used throughout this paper to illustrate the benefits of co-ordinating the two braking mechanisms and influence of unknown vehicle mass and road grade. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Robust trajectory tracking for a scale model autonomous helicopter

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 12 2004
Robert Mahony
Abstract This paper considers the question of obtaining an a priori bound on the tracking performance, for an arbitrary trajectory, of closed-loop control of an idealized model of a scale model autonomous helicopter. The problem is difficult due to the presence of small body forces that cannot be directly incorporated into the control design. A control Lyapunov function is derived for an approximate model (in which the small body forces are neglected) using backstepping techniques. The Lyapunov function derived is used to analyse the closed-loop performance of the full system. A theorem is proved that provides a priori bounds on initial error and the trajectory parameters (linear acceleration and its derivatives) that guarantees acceptable tracking performance of the system. The analysis is expected to be of use in verification of trajectory planning procedures. Copyright © 2004 John Wiley & Sons, Ltd. [source]

Sequential and iterative architectures for distributed model predictive control of nonlinear process systems

AICHE JOURNAL, Issue 8 2010
Jinfeng Liu
Abstract In this work, we focus on distributed model predictive control of large scale nonlinear process systems in which several distinct sets of manipulated inputs are used to regulate the process. For each set of manipulated inputs, a different model predictive controller is used to compute the control actions, which is able to communicate with the rest of the controllers in making its decisions. Under the assumption that feedback of the state of the process is available to all the distributed controllers at each sampling time and a model of the plant is available, we propose two different distributed model predictive control architectures. In the first architecture, the distributed controllers use a one-directional communication strategy, are evaluated in sequence and each controller is evaluated only once at each sampling time; in the second architecture, the distributed controllers utilize a bi-directional communication strategy, are evaluated in parallel and iterate to improve closed-loop performance. In the design of the distributed model predictive controllers, Lyapunov-based model predictive control techniques are used. To ensure the stability of the closed-loop system, each model predictive controller in both architectures incorporates a stability constraint which is based on a suitable Lyapunov-based controller. We prove that the proposed distributed model predictive control architectures enforce practical stability in the closed-loop system and optimal performance. The theoretical results are illustrated through a catalytic alkylation of benzene process example. © 2010 American Institute of Chemical Engineers AIChE J, 2010 [source]

Distributed model predictive control of nonlinear process systems

AICHE JOURNAL, Issue 5 2009
Jinfeng Liu
Abstract This work focuses on a class of nonlinear control problems that arise when new control systems which may use networked sensors and/or actuators are added to already operating control loops to improve closed-loop performance. In this case, it is desirable to design the pre-existing control system and the new control system in a way such that they coordinate their actions. To address this control problem, a distributed model predictive control method is introduced where both the pre-existing control system and the new control system are designed via Lyapunov-based model predictive control. Working with general nonlinear models of chemical processes and assuming that there exists a Lyapunov-based controller that stabilizes the nominal closed-loop system using only the pre-existing control loops, two separate Lyapunov-based model predictive controllers are designed that coordinate their actions in an efficient fashion. Specifically, the proposed distributed model predictive control design preserves the stability properties of the Lyapunov-based controller, improves the closed-loop performance, and allows handling input constraints. In addition, the proposed distributed control design requires reduced communication between the two distributed controllers since it requires that these controllers communicate only once at each sampling time and is computationally more efficient compared to the corresponding centralized model predictive control design. The theoretical results are illustrated using a chemical process example. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source]

Robust control from data via uncertainty model sets identification

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 11 2004
S. Malan
Abstract In this paper an integrated robust identification and control design procedure is proposed. The plant to be controlled is supposed to be linear, time invariant, stable, possibly infinite dimensional and a set of noise-corrupted input,output measurements is supposed to be available. The emphasis is placed on the design of controllers guaranteeing robust stability and robust performances, and on the trade-off between controller complexity and achievable robust performances. First, uncertainty models are identified, consisting of parametric models of different order and tight frequency bounds on the magnitude of the unmodelled dynamics. Second, Internal Model Controllers, guaranteeing robust closed-loop stability and best approximating the ,perfect control' ideal target, are designed using H,/,-synthesis techniques. Then, the robust performances of the designed controllers are computed, allowing one to determine the level of model/controller complexity needed to guarantee desired closed-loop performances. Copyright © 2004 John Wiley & Sons, Ltd. [source]