In this paper an algorithmic framework is presented for the derivation of the explicit optimal control policy for continuous linear dynamic systems that involve constraints on process inputs and outputs. The control a...
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ISBN:
(纸本)0780372980
In this paper an algorithmic framework is presented for the derivation of the explicit optimal control policy for continuous linear dynamic systems that involve constraints on process inputs and outputs. The control actions are usually computed by solving regularly an on-line optimization problem in the discrete space based on a set of measurements that specify the current process state. The novel approach presented in this paper derives the optimal control law off-line as a function of the state of the process in the continuous time-domain, thus eliminating the repetitive solution of on-line optimization problems. Hence, the on-line implementation is reduced to a sequence of simple function evaluations. The key advantageous features of the algorithm are demonstrated via an illustrative example.
In this paper an algorithm is presented for the derivation of the explicit optimal control policy for linear dynamical systems that also involve (i) logical. decisions and (ii) constraints on process inputs and output...
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ISBN:
(纸本)0780372980
In this paper an algorithm is presented for the derivation of the explicit optimal control policy for linear dynamical systems that also involve (i) logical. decisions and (ii) constraints on process inputs and outputs. The control actions axe usually computed by solving at regular time intervals an on-line optimization problem based on a set of measurements that specify the current process state. The approach presented in this paper derives the optimal control law off-line as a function of the state of the process, thus eliminating the repetitive solution of on-line optimization problems. Hence, the on-line implementation is reduced. to a sequence of simple function evaluations. The key advantageous features of the algorithm axe demonstrated via an illustrative example.
This paper describes the development of a simulation method to evaluate the accuracy of rough cutting in WEDM. The simulation program is a repetition of pulse discharge cycle based on the rule that the point where the...
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This paper describes the development of a simulation method to evaluate the accuracy of rough cutting in WEDM. The simulation program is a repetition of pulse discharge cycle based on the rule that the point where the working gap is shortest is selected as the discharge location. As some of the parameters used in the simulation are difficult to measure from experiments, they are obtained by solving the reverse problem by parametric programming. Investigations were carried out to evaluate simulation accuracy by executing simulation under various machining conditions after substituting identified parameters into the simulator. The simulation results agreed with the experimental ones qualitatively in terms of cutting groove shape, machining speed, and discharge frequency.
In this paper a method is presented for deriving the explicit robust model-based optimal control law for constrained linear dynamic systems. The controller underlying structure is derived off-line via parametric progr...
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ISBN:
(纸本)078037388X
In this paper a method is presented for deriving the explicit robust model-based optimal control law for constrained linear dynamic systems. The controller underlying structure is derived off-line via parametric programming before any actual process implementation takes place. The proposed control scheme guarantees feasibility under the presence of uncertainties and disturbances, and steady state offset elimination by (i) augmenting the model dynamics with a set of integral states and (ii) explicitly incorporating in the design stage a set of feasibility constraints.
This paper presents an algorithm for the design of robust model-based predictive controller for hybrid system under uncertainty via parametric programming. A min-max approach is adopted to design robust hybrid paramet...
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This paper presents an algorithm for the design of robust model-based predictive controller for hybrid system under uncertainty via parametric programming. A min-max approach is adopted to design robust hybrid parametric predictive controller (RHPPC) where a cost function is minimized for the maximum violation of the uncertainty involved. The proposed hybrid control scheme guarantees stability and feasible operation in the presence of bounded input uncertainty. The governing piecewise affine optimal control policy as a function of states can then be administered on-line as a sequence of simple function evaluations. An example is presented to illustrate the details of the proposed RHPPC design.
Closed-form Model Predictive Control (MPC) results in a polytopic subdivision of the set of feasible states, where each region is associated with an affine control law. Solving the MPC problem on-line then requires de...
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Closed-form Model Predictive Control (MPC) results in a polytopic subdivision of the set of feasible states, where each region is associated with an affine control law. Solving the MPC problem on-line then requires determining which region contains the current state measurement. This is the so-called point location problem. For MPC based on linear control objectives (e.g., 1- or ∞-norm), we show that this problem can be written as an additively weighted nearest neighbour search that can be solved on-line in time linear in the dimension of the state space and logarithmic in the number of regions. We demonstrate several orders of magnitude sampling speed improvement over traditional MPC and closed-form MPC schemes.
Enterprise-wide supply chain planning problems naturally exhibit a multi-level decision network structure, where for example, one level may correspond to a local plant control/scheduling/planning problem and another l...
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Enterprise-wide supply chain planning problems naturally exhibit a multi-level decision network structure, where for example, one level may correspond to a local plant control/scheduling/planning problem and another level to a corresponding plant-wide planning/network problem. Such a multi-level decision network structure can be mathematically represented by using multi-level programming principles. In this paper, we specifically address bilevel decision-making problems under uncertainty in the context of enterprise-wide supply chain optimization with one level corresponding to a plant planning problem, while the other to a distribution network problem. We first describe how such problems can be modelled as bilevel programming problems and then we present an effective solution strategy based on parametric programming techniques. An attractive feature of the proposed strategy is the fact that it transforms the bilevel problem into a family of single parametric optimization problems, which can be solved to global optimality. A numerical example is presented to illustrate the proposed framework. (C) 2003 Elsevier Ltd. All rights reserved.
Enterprise-wide supply chain planning problems naturally exhibit a multi-level decision network structure, where for example, one level may correspond to a local plant control/scheduling/planning problem and another l...
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Enterprise-wide supply chain planning problems naturally exhibit a multi-level decision network structure, where for example, one level may correspond to a local plant control/scheduling/planning problem and another level to a corresponding plant-wide planning/network problem. Such a multi-level decision network structure can be mathematically represented by using multi-level programming principles. In this paper, we specifically address bilevel decision-making problems under uncertainty in the context of enterprise-wide supply chain optimization with one level corresponding to a plant planning problem, while the other to a distribution network problem. We first describe how such problems can be modelled as bilevel programming problems and then we present an effective solution strategy based on parametric programming techniques. An attractive feature of the proposed strategy is the fact that it transforms the bilevel problem into a family of single parametric optimization problems, which can be solved to global optimality. A numerical example is presented to illustrate the proposed framework. (C) 2003 Elsevier Ltd. All rights reserved.
An algorithm for enumerating all non-dominated vectors of multiple objective integer linear programs is presented. Using a straightforward theoretical approach, the problem is solved using a sequence of progressively ...
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An algorithm for enumerating all non-dominated vectors of multiple objective integer linear programs is presented. Using a straightforward theoretical approach, the problem is solved using a sequence of progressively more constrained integer linear programs generating a new solution at each step. The algorithm can also give subsets of efficient solutions that can be useful for designing interactive procedures for large, real-life problems. (C) 2003 Elsevier B.V. All rights reserved.
In this paper we introduce a new stage cost and show that the use of this cost allows one to formulate a robustly stable feedback min-max model predictive control problem that can be solved using a single linear progr...
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In this paper we introduce a new stage cost and show that the use of this cost allows one to formulate a robustly stable feedback min-max model predictive control problem that can be solved using a single linear program. Furthermore, this is a multi-parametric linear program, which implies that the optimal control law is piecewise affine and can be explicitly pre-computed so that the linear program does not have to be solved on-line. We assume that the plant model is known, is discrete-time and linear time-invariant, is subject to unknown but bounded state disturbances and that the states of the system are measured. Two numerical examples are presented;one of these is taken from the literature, so that a direct comparison of solutions and computational complexity with earlier proposals is possible. Copyright (C) 2004 John Wiley Sons, Ltd.
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