ANNSA: A hybrid artificial neural network/simulated annealing algorithm for optimal control problems

Department of Chemical Engineering, Indian Institute of Science, Bangalore 560012, India
Chemical Engineering Science (Impact Factor: 2.34). 07/2003; 58(14):3131-3142. DOI: 10.1016/S0009-2509(03)00168-4


This paper introduces a numerical technique for solving nonlinear optimal control problems. The universal function approximation capability of a three-layer feedforward neural network has been combined with a simulated annealing algorithm to develop a simple yet efficient hybrid optimisation algorithm to determine optimal control profiles. The applicability of the technique is illustrated by solving various optimal control problems including multivariable nonlinear problems and free final time problems. Results obtained for the different case studies considered agree well with those reported in the literature.

Download full-text


Available from: Jayant M Modak,
  • [Show abstract] [Hide abstract]
    ABSTRACT: In the present work, it was developed an adaptive model predictive control algorithm to control a semi batch pyrolysis reactor. An 8L reactor and a separation system were assembled for this purpose. The reactor temperature control was carried out through a digital control system implemented for this process. The model used to infer about the process was a multilayered neural network completely recursive. To avoid offset problems an adaptive algorithm was applied, performing on-line weights actualization. The neural network was used to explicitly predict the process output (reactor temperature) through a pre-defined prediction horizon. Through optimization, this output vector was used to estimate the process input (heat power supply). A qualitative analysis of the products and the total time of operation for a fast pyrolysis, sustained for ten minutes in the set point temperature, had pointed out, in this case, a superior performance to the proposed controller when compared with the classical feedback controller. Besides, the temperature stabilizes without overshoots and offsets. The developed control algorithm was able to compensate the strong disturbances that occur during the partial discharge of pyrolysis products, due to reactor pressure relief. A performance index based on ISA criteria was used and again, exhibits consistent improvement of the adaptive control over a classical feedback algorithm.
  • [Show abstract] [Hide abstract]
    ABSTRACT: The determination of optimal feed rate profiles for fed-batch bioreactors with more than one feed rates is a numerically difficult problem involving multiple singular control variables. A solution strategy based on genetic algorithm approach for the determination of optimal substrate feeding policies for fed-batch bioreactors with multi-control variables is proposed. The multiplier updating method is introduced in the proposed method to handle inequality constraints on state variables. The efficiency of the algorithm is demonstrated for two case studies on fed-batch bioreactors with two control variables taken from literature. The control policies obtained retain the characteristics of the profiles generated through rigorous application of control theory.
    Computers & Chemical Engineering 05/2004; 28(5-28):789-798. DOI:10.1016/j.compchemeng.2004.02.018 · 2.78 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: In many industrial processes, especially chemistry and metallurgy industry, the plant is slow for feedback and data test because of complex and varying factors. Considering the multi-objective feature and the complex problem of production stability in optimal control, this paper proposed an optimal control strategy based on genetic programming (GP), used as a multi-step state transferring procedure. The fitness function is computed by multi-step comprehensive evaluation algorithm, which provides a synthetic evaluation of multi-objective in process state based on single objective models. The punishment to process state variance is also introduced for the balance between optimal performance and stability of production. The individuals in GP are constructed as a chain linked by a few relation operators of time sequence for a facilitated evolution in GP with compact individuals. The optimal solution gained by evolution is a multi-step command program of process control, which not only ensures the optimization tendency but also avoids violent process variation by adjusting control parameters step by step. An optimal control system for operation direction is developed based on this strategy for imperial smelting process in Shaoguan. The simulation and application results showed its effectiveness for production objects optimization in complex process control.
    Engineering Applications of Artificial Intelligence 08/2004; 17(5):491-500. DOI:10.1016/j.engappai.2004.04.018 · 2.21 Impact Factor
Show more