Data-Driven Model-Free Adaptive Control of Particle Quality in Drug Development Phase of Spray Fluidized-Bed Granulation Process

Abstract

A novel data-driven model-free adaptive control (DDMFAC) approach is first proposed by combining the advantages of modelfree adaptive control (MFAC) and data-driven optimal iterative learning control (DDOILC), and then its stability and convergence analysis is given to prove algorithm stability and asymptotical convergence of tracking error. Besides, the parameters of presented approach are adaptively adjusted with fuzzy logic to determine the occupied proportions of MFAC and DDOILC according to their different control performances in different control stages. Lastly, the proposed fuzzy DDMFAC (FDDMFAC) approach is applied to the control of particle quality in drug development phase of spray fluidized-bed granulation process (SFBGP), and its control effect is compared with MFAC and DDOILC and their fuzzy forms, in which the parameters of MFAC and DDOILC are adaptively adjusted with fuzzy logic. The effectiveness of the presented FDDMFAC approach is verified by a series of simulations.

Data-Driven Adaptive Iterative Learning Method for Active Vibration Control Based on Imprecise Probability

“…where a step size constant ϕ ∈ (0, 1] is added to make Equation (17) general. The parameters of the PPD matrix are estimated as follows [20]:…”

“…A model-free adaptive (MFA) control, a data-driven adaptive control method, can be operated using only input and output (I/O) data from the system [15] and is suitable to deal with uncertainties [16]. Such a method can realize the adaptive adjust in parametric as well as structural manners, and have been successfully incorporated into the IL method for different industrial applications, such as particle quality control for spray fluidized-bed granulation [17], formation control of multi-agent systems [18],freeway traffic iterative learning control [19], and vibration suppression [20]. In this paper, the MFA control was applied to tune the learning gains of the P-type IL method by the system's dynamic behavior.…”

Model-free Adaptive Sliding Mode Control for Continuous Stirred Tank Reactor

“…The aforementioned approaches require the use of the CSTR dynamic model in the controller design which may be difficult to obtain accurately in practice. Model-free adaptive control [13][14][15][16][17] (MFAC) is a method that does not requires any information on the mathematical model. It has been successfully applied to control problems in the fields of oil refining, chemical, electrical, light industry and urban road systems.…”