摘要:

Most reported applications of model-predictive control (MFC) have a narrow scope, with 1-5 variables to be regulated and a comparable number of manipulated variables. Several authors have claimed that global-optimization versions of MPC (such as DMC and QDMC) should be more useful for problems in which an entire system can be operated to achieve an economic and/or technical objective. In this paper, we describe the application of MPC to a large-scale, constraint-dominated problem: the minimization of combined-sewer overflows (CSOs) in the Seattle metropolitan area. The key decision variables are flowrates at 23 locations throughout the sewer network. There are approximately 40 output variables that must be kept between lower and upper bounds. The main issues addressed in the application are: (1) definition of an appropriate objective function for on-line optimization; (2) creation and maintenance of complex system models; and (3) use of state estimation to minimize the impact of disturbances and model errors. MPC performance is compared with that of an existing heuristic (rule-based) control strategy for seven design storms, selected from historical records. A realistic, nonlinear simulation of the sewer system acts as the plant. MPC reduces CSOs by 26% (on a yearly basis) relative to the existing control strategy. This was sufficient incentive for the sewer agency to replace their heuristic control strategy with MPC.

ISSN:0020-7179    

DOI:10.1080/00207179408923105

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

摘要:

This paper considers the design of an advanced (relative to conventionally tuned multiloop PI control) control scheme for an industrial C3-splitter column using a range of recently proposed analytic tools. Due to product specifications, this column must be kept under tight control despite the effects of large disturbances and slow system response. Model predictive control was shown to provide a substantial improvement over a conventionally tuned multiloop controller but required a level of computation beyond that possible in a basic distributed control system. Controlled column behaviour comparable with that possible with model predictive control was shown to be achievable using a multiloop controller specifically tuned so as to incorporate disturbance rejection capabilities. The value of such controller design tools and their ready implementation within commercial software (here Matlab) should see their more widespread acceptance and use by the industrial control community.

ISSN:0020-7179    

DOI:10.1080/00207179408923106

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

摘要:

A numerically computed constrained multivariable generalized predictive control algorithm is proposed. The solution of the optimization problem is achieved using an orthogonal matrix decomposition. Apart from improving the numerical integrity of the solution, Singular Value Decomposition also provides additional useful information that can be used to enhance the robustness of the closed loop control system. The proposed algorithm has been applied to two high purity distillation column physiochemical models, and the closed loop control performances studied. These have demonstrated the validity of the algorithm in respect of being able to respond to process operating constraints. In addition, the algorithm has been shown to be capable of stabilizing systems in which the nominal plant dynamic gain matrix was ill-conditioned.

ISSN:0020-7179    

DOI:10.1080/00207179408923107

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor

摘要:

Based on real-time identification and using the concept of NARX (Nonlinear AutoRegressive with exogenous inputs) models, a new adaptive nonlinear predictive controller (ANPC) design is proposed. NARX models represent a natural way to describe the input-output relationship of severely nonlinear systems. From an initial batch of input-output data, a parsimonious NARX model is obtained using the Modified Gram-Schmidt (MGS) orthogonalization algorithm. Following this initial off-line identification and model reduction procedure, the control loop is closed. The ANPC directly uses the obtained structure and initial parameter estimates, which are updated each time step using recursive identification. The controller is designed similar to a typical linear predictive controller based on solving a nonlinear programming (NLP) problem. This paper shows how to solve this NLP problem on-line without the knowledge of the NARX model structure. The design is given for the multi-input multi-output (MIMO) case.

ISSN:0020-7179    

DOI:10.1080/00207179408923108

出版商:Taylor & Francis Group    

年代:1994

数据来源: Taylor