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Noisy-output-based direct learning tracking control with Markov nonuniform trial lengths using adaptive gains
In this article, a noisy-output-based direct learning tracking control is proposed for stochastic
linear systems with nonuniform trial lengths. The iteration-varying trial length is modeled …
linear systems with nonuniform trial lengths. The iteration-varying trial length is modeled …
Nonlinear monotonically convergent iterative learning control for batch processes
Iterative learning control (ILC) has been successfully applied to numerous batch processes
over the past decades. Monotonic convergence of tracking error is a desired characteristic …
over the past decades. Monotonic convergence of tracking error is a desired characteristic …
Multi-scale data-driven engineering for biosynthetic titer improvement
Industrial biosynthesis is a very complex process which depends on a range of different
factors, from intracellular genes and metabolites, to extracellular culturing conditions and …
factors, from intracellular genes and metabolites, to extracellular culturing conditions and …
Data-driven soft sensing for batch processes using neural network-based deep quality-relevant representation learning
Soft sensors provide a means to reliably estimate unmeasurable variables, thereby playing
a prevalent role in formulating closed-loop control in batch processes. In soft sensor …
a prevalent role in formulating closed-loop control in batch processes. In soft sensor …
Real-time model-free coordination of active and reactive powers of distributed energy resources to improve voltage regulation in distribution systems
This paper proposes and implements a model-free optimal strategy to regulate the voltage in
distribution systems by effective control of Inverter-Interfaced Distributed Energy Resources …
distribution systems by effective control of Inverter-Interfaced Distributed Energy Resources …
Realtime brain-inspired adaptive learning control for nonlinear systems with configuration uncertainties
This paper investigates the problem of adaptive tracking control for quadcopter in the
presence of nonlinear configuration uncertainties. It utilizes a real-time brain-inspired …
presence of nonlinear configuration uncertainties. It utilizes a real-time brain-inspired …
Extremum-seeking control for optimization of time-varying steady-state responses of nonlinear systems
Extremum-seeking control (ESC) is a useful tool for the steady-state performance
optimization of plants for which limited knowledge about its dynamical behavior and …
optimization of plants for which limited knowledge about its dynamical behavior and …
Extremum seeking control for personalized zone adaptation in model predictive control for type 1 diabetes
Zone model predictive control has proven to be an effective closed-loop method to regulate
blood glucose for people with type 1 diabetes (T1D). In this paper, we present a universal …
blood glucose for people with type 1 diabetes (T1D). In this paper, we present a universal …
Averaging techniques for balancing learning and tracking abilities over fading channels
With the wide use of networks in repetitive systems, channels between a plant and a
controller may experience random fading, which is a common problem in long-distance …
controller may experience random fading, which is a common problem in long-distance …
An intelligent non-optimality self-recovery method based on reinforcement learning with small data in big data era
Batch processes have attracted extensive attention as a crucial manufacturing way in
modern industries. Although they are well equipped with control devices, batch processes …
modern industries. Although they are well equipped with control devices, batch processes …