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Offset-free multistep nonlinear model predictive control under plant-model mismatch

Offset-free multistep nonlinear model predictive control under plant-model mismatch
Offset-free multistep nonlinear model predictive control under plant-model mismatch
A multistep nonlinear model predictive control (MPC) framework is developed to achieve steady-state offsetfree control in the presence of plant–model mismatch. Our formulation explicitly accounts for the effect of plant–model mismatch by involving the output feedback error, which is expressed as the difference between the measured process output and the predicted model output at the previous sampling instance, in the multistep model recursive prediction. The proposed scheme is capable of improving the performance of nonlinear MPC, because the plant–model mismatch is effectively compensated through the recursive prediction propagation. We prove that this formulation is able to remove the steady-state error to achieve offset-free control. The proposed nonlinear MPC framework is applied to a highly nonlinear two-input two-output continuous stirred tank reactor, in comparison with other MPC implementations. The results obtained demonstrate that the proposed technique outperforms some existing popular MPC schemes and can realise offset-free control even under significant plant–model mismatch and unmeasured disturbances
0890-6327
444-463
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Wang, Ping
5f7a5780-5969-4486-ab0c-c527e48b3c34
Huang, Dexian
91cd0de2-794c-4b33-ade7-010127652926
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Wang, Ping
5f7a5780-5969-4486-ab0c-c527e48b3c34
Huang, Dexian
91cd0de2-794c-4b33-ade7-010127652926
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Tian, Xuemin, Wang, Ping, Huang, Dexian and Chen, Sheng (2014) Offset-free multistep nonlinear model predictive control under plant-model mismatch. International Journal of Adaptive Control and Signal Processing, 28 (3-5), 444-463. (doi:10.1002/acs.2367).

Record type: Article

Abstract

A multistep nonlinear model predictive control (MPC) framework is developed to achieve steady-state offsetfree control in the presence of plant–model mismatch. Our formulation explicitly accounts for the effect of plant–model mismatch by involving the output feedback error, which is expressed as the difference between the measured process output and the predicted model output at the previous sampling instance, in the multistep model recursive prediction. The proposed scheme is capable of improving the performance of nonlinear MPC, because the plant–model mismatch is effectively compensated through the recursive prediction propagation. We prove that this formulation is able to remove the steady-state error to achieve offset-free control. The proposed nonlinear MPC framework is applied to a highly nonlinear two-input two-output continuous stirred tank reactor, in comparison with other MPC implementations. The results obtained demonstrate that the proposed technique outperforms some existing popular MPC schemes and can realise offset-free control even under significant plant–model mismatch and unmeasured disturbances

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Published date: March 2014
Organisations: Southampton Wireless Group

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Local EPrints ID: 363293
URI: http://eprints.soton.ac.uk/id/eprint/363293
ISSN: 0890-6327
PURE UUID: 3727cd8f-5cb1-49b2-8b97-aa320b013b0f

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Date deposited: 24 Mar 2014 11:36
Last modified: 16 Dec 2019 20:26

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