Nolinear model predictive control using Hammerstein models

Nonlinear model predictive control of the Tennessee Eastman challenge process

A fuzzy model predictive control (FMPC) approach is introduced to design a control system for a highly nonlinear process. In this approach, a process system is described by a fuzzy convolution model that consists of a number of quasi-linear fuzzy implications. In controller design, prediction errors and control energy are minimized through a two-layered iterative optimization process. At the lower layer, optimal local control policies are identified to minimize prediction errors in each subsystem. A near optimum is then identified through coordinating the subsystems to reach an overall minimum prediction error at the upper layer. The two-layered computing scheme avoids extensive online nonlinear optimization and permits the design of a controller based on linear control theory. The efficacy of the FMPC approach is demonstrated through three examples.

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Fuzzy model predictive control

This paper investigates in detail the possible application of neural networks to the modeling and adaptive control of nonlinear systems. Nonlinear neural-network-based plant modeling is first discussed, based on the approximation capabilities of the multilayer perceptron. A structure is then proposed to utilize feedforward networks within a direct model reference adaptive control strategy. The difficulties involved in training this network, embedded within the closed-loop are discussed and a novel neural-network-based sensitivity modeling approach proposed to allow for the backpropagation of errors through the plant to the neural controller. Finally, a novel nonlinear internal model control (IMC) strategy is suggested, that utilizes a nonlinear neural model of the plant to generate parameter estimates over the nonlinear operating region for an adaptive linear internal model, without the problems associated with recursive parameter identification algorithms. Unlike other neural IMC approaches the linear control law can then be readily designed. A continuous stirred tank reactor was chosen as a realistic nonlinear case study for the techniques discussed in the paper.

Nonlinear control structures based on embedded neural system models

A nonlinear model predictive control system based on Wiener piecewise linear models

An adaptive nonlinear predictive controller

The design and implementation of a new adaptive nonlinear predictive controller is presented using a general nonlinear model and variable transformations. The resulting controller is similar in form to standard linear model predictive controllers and can be tuned analogously. This design allows the controller to be updated on-line without recalculating the controller gain matrix, which involves a matrix inversion. The new controller is compared to a PI controller and to an adaptive linear predictive controller through simulations.

An Adaptive Nonlinear Predictive Controller

A new multivariable adaptive nonlinear predictive controller is designed using a general nonlinear input-output model and variable tansformations. The controller is similar in form to typical linear predictive controllers and can be tuned analogously or by specifying a single parameter for each controlled variable. In addition, the design procedure is computationally efficient The new controller is compared to a multi-loop PI control system with one-way static decoupling, and to a nonadaptive, nonlinear predictive controller through tests on a simulated nonlinear distillaton column.

A Multivariable Adaptive Nonlinear Predictive Controller

Experimental evaluation of a multivariable adaptive nonlinear predictive controller

Although predictive control techniques such as Dynamic Matrix Control and Model Algorithmic Control have received much attention in recent years, systematic methods for on-line updating of the process models and predictive control laws rarely have been reported. Such adaptation is desirable if the process conditions change significantly over a period of time. The same approach also could be used to generate a discrete convolution model for the initil controller design. In this paper, several alternative strategies for on-line adaptation during closed-loop operation are evaluated and compared via simulations.

J. Duane Morningred

Papers

An adaptive nonlinear predictive controller

An Adaptive Nonlinear Predictive Controller

A Multivariable Adaptive Nonlinear Predictive Controller

Experimental evaluation of a multivariable adaptive nonlinear predictive controller

On-Line Adaptation of Predictive Controllers