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Smart batch process: The evolution from 1D and 2D to new 3D perspectives in the era of Big Data
Big Data will revolutionize modern industry by improving process optimization, facilitating
insight discovery, and improving decision-making. This big data revolution presents a …
insight discovery, and improving decision-making. This big data revolution presents a …
Combined iterative learning and model predictive control scheme for nonlinear systems
Batch processes are typically nonlinear systems with constraints. Model predictive control
(MPC) and iterative learning control (ILC) are effective methods for controlling batch …
(MPC) and iterative learning control (ILC) are effective methods for controlling batch …
Data-Driven Robust Iterative Learning Predictive Control for MIMO Nonaffine Nonlinear Systems with Actuator Constraints
The coupling of multivariate repeated systems and the nonlinearity that is difficult to
characterize through mechanisms, along with actuator constraints and data noise pollution …
characterize through mechanisms, along with actuator constraints and data noise pollution …
A two-dimensional model predictive iterative learning control based on the set point learning strategy for batch processes
Although conventional two-dimensional model predictive iterative learning control (2D-
MPILC) based on an extended non-minimum state space (ENMSS) model can avoid …
MPILC) based on an extended non-minimum state space (ENMSS) model can avoid …
Self-tuning nonlinear iterative learning for a precision testing stage: A set-membership approach
L Li, H Zhao, Y Liu - IEEE Transactions on Industrial Informatics, 2022 - ieeexplore.ieee.org
Iterative learning control (ILC) is an appealing method in motion control applications that can
achieve the performance limit of feedforward compensation in repeating tasks …
achieve the performance limit of feedforward compensation in repeating tasks …
Data-driven high-order point-to-point ILC with higher computational efficiency
For a class of unknown MIMO non-affine nonlinear repetitive discrete-time systems, a novel
data-driven high-order point-to-point iterative learning control scheme is proposed. The …
data-driven high-order point-to-point iterative learning control scheme is proposed. The …
Data-efficient constrained learning for optimal tracking of batch processes
Repeatability provides an opportunity to learn from historical process data, thus enabling
batch processes to produce high-value and batch-improved products. However, industrial …
batch processes to produce high-value and batch-improved products. However, industrial …
A novel two‐dimensional PID controller design using two‐dimensional model predictive iterative learning control optimization for batch processes
It is known that the key indicators of batch processes are controlled by conventional
proportional–integral–derivative (PID) strategies from the view of one‐dimensional (1D) …
proportional–integral–derivative (PID) strategies from the view of one‐dimensional (1D) …
Data-Driven Iterative Learning Temperature Control for Rubber Mixing Processes
R Chi, Z Zhou, H Zhang, N Lin… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Considering the four challenges of non-identical initial states, non-repetitive uncertainties,
different batch lengths, and unavailable mathematical model of a rubber mixing process …
different batch lengths, and unavailable mathematical model of a rubber mixing process …
Offline-to-Online Learning Enabled Robust Control for Uncertain Robotic Systems Pursuing Constraint-Following
A major challenge in robust control design of robotic systems is finding a comprehensive
uncertainty bound (CUB) with low conservativeness for uncertainty compensation. This …
uncertainty bound (CUB) with low conservativeness for uncertainty compensation. This …