Fuzzy predictive control of highly nonlinear pH process
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Cited by (70)
A predictive control based on decentralized fuzzy inference for a pH neutralization process
2022, Journal of Process ControlCitation Excerpt :Among them, the T–S fuzzy model has been proved to be very suitable for nonlinear predictive control due to its more accurate approximation ability for complex nonlinear systems [32,33]. By calculating the unit step response of the T–S model, Kavsek-Biasizzo et al. [34] constructed a DMC algorithm to control a strongly nonlinear pH process. The results showed that the algorithm had better inhibition effect on model mismatching and unmeasurable disturbance than the conventional DMC algorithm.
Stochastic data-driven model predictive control using gaussian processes
2020, Computers and Chemical EngineeringCitation Excerpt :Many mechanistic and empirical models are however often too complex to be used online and in addition have often high development costs. Alternatively, black-box identification models can be exploited instead, such as support vector machines (Xi et al., 2007), fuzzy models (Kavsek-Biasizzo et al., 1997), neural networks (NNs) (Piche et al., 2000), or Gaussian processes (GPs) (Kocijan et al., 2004). For example, recently in Wu et al. (2019c,b) recurrent NNs are utilised for an extensive NMPC approach with proofs on closed-loop state boundedness and convergence applied to a chemical reactor.
Stable fuzzy control and observer via LMIs in a fermentation process
2018, Journal of Computational ScienceCitation Excerpt :Thus, a gain-scheduling fuzzy control can be used to develop a fuzzy control in different operating points [19], each control can be obtained from a heuristic design and then copied in each submodel; as these controllers are heuristic in nature because there is not a design procedure, it cannot be guaranteed stability for all applications [20]. One way to obtain a design procedure to have a stable control for a bioprocess is to use the local linear models as consequents of fuzzy rules [11] where the approximate linear submodels are obtained for different operating points; in this way, if it is possible to obtain a common matrix P for all submodels using the Lyapunov theory, it is possible to obtain a stable fuzzy controller [21]. The development of an output-feedback controller using this idea was presented in [22] where they were added degrees of freedom to achieve a proper solution of the LMIs.
Shape-independent model predictive control for Takagi–Sugeno fuzzy systems
2017, Engineering Applications of Artificial IntelligenceCitation Excerpt :For instance, the widely-cited work (Sousa et al., 1997) resorts to clustering but, actually, it carries out nonlinear MPC on the resulting identified fuzzy model, via nonconvex branch-and-bound optimisation. The work Kavsek-Biasizzo et al. (1997) computes a linear MPC by “freezing” the memberships at a particular instant and assuming they will be constant in the future; this might work in practice, but it lacks justification in fast transients. The work Lu and Arkun (2000) presents an interesting approach in which a sequence of quadratic cost bounds and state-feedback gains solves (suboptimally) the MPC problem.
Model predictive control of pH neutralization processes: A review
2015, Control Engineering PracticeCitation Excerpt :As only one model was identified the result are very similar to the linear model case described in Section 5.1.1. The first application of the Takagi–Sugeno approach was done by Kavsek-Biasizzo, Skrjanc, and Matko (1997). They obtained a nonlinear model from the fuzzy modeling, while applying triangular weights, hence just linearly combining two models at the time, to obtain the linear model applied at each time step.
Grey prediction fuzzy control for pH processes in the food industry
2010, Journal of Food Engineering