Comparison of three optimisation methods for constrained generalised predictive control coupled with an estimation by genetic algorithm

Filali, S.

doi:10.1109/icsmc.2002.1176351

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…The simulation research is implemented based-on the model provided by reference [4] in various constraint conditions:…”

Section: Algorithm Simulationmentioning

confidence: 99%

“…In considering these constraints, the constraint quadratic programming should be solved, also objective function and constraint condition must be differentiable, and commonly only locally optimal solution is obtained. To improve GPC performance and extend its application, the effective method to solve the optimization problem must be researched [4][5] [6]. The paper proposes IGPC based-on PSO hybrid optimization, this modified IGPC can be applied to both of the constraint and non-constraint industry process control.…”

Section: Introductionmentioning

confidence: 99%

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Research of IGPC Control Strategy Based-on Hybrid Optimization for Power Station Boiler Superheated Steam Temperature

Xiao¹,

Chen²,

Xiao³

2014

IJITCS

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Implicit Generalized Predictive Control (IGPC) algorithm can directly identify controller parameters without the need of solving Diophantine equation, thus can reduce the on-line algorithm computation time. In order to improve IGPC performance and extend its application, modified Particle Swarm Optimization (PSO) algorithm is introduced into IGPC rolling horizon optimization, combined with general IGPC gradient optimization method under unconstrained condition, a new hybrid optimization method is obtained, this modified IGPC can be used to both of the non-constraint industry process control and the constraint industry process control. Aiming at the superheated steam temperature control of sub-critical 600MW boiler, a new cascade compound control strategy that combines an outer loop IGPC master adjuster and an inner loop PID auxiliary adjuster is adopted. Finally the simulation results have shown that the proposed method can constrain the control action, prevent dramatic change of the input signal, thus can achieve good static and dynamic performances.

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“…The simulation research is implemented based-on the model provided by reference [4] in various constraint conditions:…”

Section: Algorithm Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research of IGPC Control Strategy Based-on Hybrid Optimization for Power Station Boiler Superheated Steam Temperature

Xiao¹,

Chen²,

Xiao³

2014

IJITCS

View full text Add to dashboard Cite

show abstract

“…In addition, a simple constraint reduction strategy is given to eliminate the superfluous constraints. In [7], a comparison study of Dichotomy and gradient optimization methods for constrained generalized predictive control is given. Generally speaking, a constrained quadratic programming problem has to be solved when constraints are present.…”

Section: Introductionmentioning

confidence: 99%

Constrained multi-variable generalized predictive control using a dual neural network

Cheng

Hou

Tan

2007

Neural Comput & Applic

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Multi-variable generalized predictive control algorithm has obtained great success in process industries. However, it suffers from a high computational cost because the multi-stage optimization approach in the algorithm is time-consuming when constraints of the control system are considered. In this paper, a dual neural network is employed to deal with the multi-stage optimization problem, and bounded constraints on the input and output signals of the control system are taken into account. The dual neural network has many favorable features such as simple structure, rapid execution, and easy implementation. Therefore, the computation efficiency, in comparison with the consecutive executions of numerical algorithms on digital computers, is increased dramatically. In addition, the dual network model can yield the exact optimum values of future control signals while many other neural networks only obtain the approximate optimal solutions. Hence the multi-variable generalized predictive control algorithm based on the dual neural network is suitable for industrial applications with the real-time computation requirement. Simulation examples are given to demonstrate the efficiency of the proposed approach.

show abstract

Comparison of three optimisation methods for constrained generalised predictive control coupled with an estimation by genetic algorithm

Cited by 2 publications

References 5 publications

Research of IGPC Control Strategy Based-on Hybrid Optimization for Power Station Boiler Superheated Steam Temperature

Research of IGPC Control Strategy Based-on Hybrid Optimization for Power Station Boiler Superheated Steam Temperature

Constrained multi-variable generalized predictive control using a dual neural network

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