Xiong et al., 2005 - Google Patents
Neural network model-based on-line re-optimisation control of fed-batch processes using a modified iterative dynamic programming algorithmXiong et al., 2005
- Document ID
- 7241066898764834027
- Author
- Xiong Z
- Zhang J
- Publication year
- Publication venue
- Chemical Engineering and Processing: Process Intensification
External Links
Snippet
A strategy to overcome the problems of unknown disturbances and model-plant mismatches in fed-batch process optimal control through on-line re-optimisation is presented in this paper. To address the difficulty in developing detailed first-principle models and the time …
- 238000000034 method 0 title abstract description 64
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/048—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators using a predictor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B17/00—Systems involving the use of models or simulators of said systems
- G05B17/02—Systems involving the use of models or simulators of said systems electric
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0205—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system
- G05B13/024—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/12—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for modelling or simulation in systems biology, e.g. probabilistic or dynamic models, gene-regulatory networks, protein interaction networks or metabolic networks
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Xiong et al. | Neural network model-based on-line re-optimisation control of fed-batch processes using a modified iterative dynamic programming algorithm | |
Wu et al. | Machine‐learning‐based predictive control of nonlinear processes. Part II: Computational implementation | |
Nagy et al. | Evaluation study of an efficient output feedback nonlinear model predictive control for temperature tracking in an industrial batch reactor | |
Hosen et al. | Control of polystyrene batch reactors using neural network based model predictive control (NNMPC): An experimental investigation | |
Pistikopoulos et al. | Towards the integration of process design, control and scheduling: Are we getting closer? | |
Alhajeri et al. | Tuning guidelines for model-predictive control | |
Al Seyab et al. | Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation | |
Hussain | Review of the applications of neural networks in chemical process control—simulation and online implementation | |
Xiong et al. | A batch-to-batch iterative optimal control strategy based on recurrent neural network models | |
Özkan et al. | Control of a solution copolymerization reactor using multi-model predictive control | |
Lee et al. | Approximate dynamic programming based approach to process control and scheduling | |
Jia et al. | Integrated neuro-fuzzy model and dynamic R-parameter based quadratic criterion-iterative learning control for batch process | |
Sun et al. | Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions | |
Zavala | Computational strategies for the optimal operation of large-scale chemical processes | |
Ławryńczuk | Explicit nonlinear predictive control algorithms with neural approximation | |
Kansha et al. | New results on VRFT design of PID controller | |
Schwedersky et al. | Nonlinear model predictive control algorithm with iterative nonlinear prediction and linearization for long short-term memory network models | |
Eftekhari et al. | Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering, GA and unscented filter | |
Teixeira et al. | Bioprocess iterative batch‐to‐batch optimization based on hybrid parametric/nonparametric models | |
Marusak | A numerically efficient fuzzy MPC algorithm with fast generation of the control signal | |
Doncevic et al. | Deterministic global nonlinear model predictive control with neural networks embedded | |
Palma-Flores et al. | Simultaneous design and nonlinear model predictive control under uncertainty: A back-off approach | |
Xiong et al. | Modelling and optimal control of fed-batch processes using a novel control affine feedforward neural network | |
Ye et al. | Generalized global self-optimizing control for chemical processes part I. The existence of perfect controlled variables and numerical design methods | |
Silva et al. | Optimization and nonlinear model predictive control of batch polymerization systems |