A two-layer control algorithm is developed for a class of hybrid (discrete-continuous dynamic) systems comprising important applications such as the economically optimal operation of recipe-driven batch or continuous ...
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For model identification of industrial operating systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem, a subspace identification method is proposed in this paper by develop...
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A robust closed-loop iterative learning control (ILC) method is proposed for industrial batch processes with state delay and time-varying uncertainties from cycle to cycle. Based on a two-dimensional (2D) system descr...
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A robust closed-loop iterative learning control (ILC) method is proposed for industrial batch processes with state delay and time-varying uncertainties from cycle to cycle. Based on a two-dimensional (2D) system description of such a batch process, a closed-loop ILC scheme consisting of dynamic output feedback plus feedforward control is established to realize robust tracking of the setpoint trajectory in both the time (during a cycle) and batch (from cycle to cycle) directions. Only measured output errors of current and previous cycles are used for control design to facilitate practical implementation. A flexible 2D difference Lyapunov function that guarantees monotonical state energy decrease in both the time and batch directions is introduced to establish a sufficient condition in terms of the linear matrix inequality (LMI) for holding robust stability of the closed-loop ILC system. Correspondingly, the ILC controller can be explicitly formulated, together with an adjustable robust H infinity performance level. An illustrative example is given to demonstrate effectiveness and merits of the proposed ILC method.
A hierarchical distributed algorithm for nonlinear optimal control is presented. The algorithm enables the change of the operating point for continuous processes in real-time by further reducing computational time esp...
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ISBN:
(纸本)9781457710957
A hierarchical distributed algorithm for nonlinear optimal control is presented. The algorithm enables the change of the operating point for continuous processes in real-time by further reducing computational time especially for large-scale systems with several coupled subsystems. The algorithm is an extension of a two-layer nonlinear control architecture which provides a rigorous solution to the optimal control problem by a slow controller and a sensitivity-based update for the optimal trajectories under disturbances by a fast neighboring-extremal (NE) controller. The direct incorporation of disturbances within the fast NE controller results in a parametric QP which is split up in a set of distributed parametric QP to be solved in parallel.
This work focuses on the control design for feedback linearizable nonlinear systems with unknown time-varying disturbances and uncertainty such that bounds on inputs and state constraints are not violated. Exploiting ...
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This work focuses on the control design for feedback linearizable nonlinear systems with unknown time-varying disturbances and uncertainty such that bounds on inputs and state constraints are not violated. Exploiting the special structure of the systems considered, controllers based on Lyapunov's direct methods are easily synthesized, which enforce asymptotic stability in the presence of bounded inputs. However, these controllers do not account explicitly for state constraints in the presence of disturbances and uncertainty. The solution of this task is addressed in this work by an optimization-based method, the so-called normal vector approach.
A two-layer control algorithm is developed for a class of hybrid (discrete-continuous dynamic) systems comprising important applications such as the economically optimal operation of recipe-driven batch or continuous ...
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A two-layer control algorithm is developed for a class of hybrid (discrete-continuous dynamic) systems comprising important applications such as the economically optimal operation of recipe-driven batch or continuous processes. On the upper layer, the economic optimal control problem is solved rigorously by a slow controller at a low sampling rate, whereas a fast neighboring-extremal controller updates rather than tracks the optimal trajectories to account for disturbances. Consequently, the process is steered to its operational bounds, while the optimal switching times under disturbances can be determined such that the economic potential of the process is fully exploited anytime.
For model identification of industrial operating systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem, a subspace identification method is proposed in this paper by develop...
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For model identification of industrial operating systems subject to noisy input-output observations, known as the error-in-variables (EIV) problem, a subspace identification method is proposed in this paper by developing an orthogonal projection approach to guarantee consistent estimation of the deterministic part of such a system. The rank condition for such orthogonal projection is analyzed in terms of the nominal state-space model structure. Using the principal component analysis (PCA), the extended observability matrix and low triangular block-Toeliptz matrix of the state-space model are analytically derived. Accordingly, the system state-space matrices can be retrieved in a transparent manner from the above matrices through linear algebra or an ordinary least-squares (LS) algorithm. A benchmark example used in the existing references is adopted to demonstrate the effectiveness and merit of the proposed subspace identification method.
A novel distributed model predictive control method for linear discrete-time systems is considered. The method can handle coupled constraints as well as coupled objective functions. For the decomposed optimal control ...
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Based on a closed-loop step response test, a low-order model identification method is proposed to facilitate on-line controller tuning for load disturbance rejection in industrial engineering practices. By introducing...
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This paper presents a generalization of the model reduction method proper orthogonal decomposition to systems of differential-algebraic equations of arbitrary index. It is known that proper orthogonal decomposition ge...
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