Optimal Control Systems Using Evolutionary Algorithm-Control Input Range Estimation

Abstract

The closed-loop optimal control systems using the receding horizon control (RHC) structure make predictions based on a process model (PM) to calculate the current control output. In many applications, the optimal prediction over the current prediction horizon is calculated using a metaheuristic algorithm, such as an evolutionary algorithm (EA). The EAs, as other population-based metaheuristics, have large computational complexity. When integrated into the controller, the EA is carried out at each sampling moment and subjected to a time constraint: the execution time should be smaller than the sampling period. This paper proposes a software module integrated into the controller, called at each sampling moment. The module estimates using the PM integration the future process states, over a short time horizon, for different control input values covering the given technological interval. Only a narrower interval is selected for a ‘good’ evolution of the process, based on the so-called ‘state quality criterion’. The controller will consider only a shrunk control output range for the current sampling period. EA will search for its best prediction inside a smaller domain that does not cause the convergence to be affected. Simulations prove that the computational complexity of the controller will decrease.

Operational Matrix of New Shifted Wavelet Functions for Solving Optimal Control Problem

“…Optimal control in natural methods is seen as a standard optimization problem by searching for the control function u(t) that optimizes the performance index. Different algorithms were used for solving optimal control problems, including the indirect modified pseudospectral method [6], a direct Chebyshev cardinal functions method [7], Cauchy discretization technique [8], the synthesized optimal control technique [9], Legendre functions method [10], Evolutionary Algorithm-Control Input Range Estimation [11], a hybrid of block-pulse function, and orthonormal Taylor polynomials [12]. (See [13][14][15][16][17] for some other articles exploring various optimal control problems.)…”

A Tool for Control Research Using Evolutionary Algorithm That Generates Controllers with a Pre-Specified Morphology

“…Recently, evolutionary algorithms (EA) for automatic controller generation has been proposed [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. These algorithms begin by creating a set of elements, called the initial generation.…”

Bioprocess Intensification of a Continuous-Flow Enzymatic Bioreactor via Productivity Dynamic Optimization under Modeling Uncertainty

“…There are several error cost functions, like integrated time-weighted absolute error (ITAE), integrated squared error (ISE), integrated exponential time squared error (IETSE), integrated squared time cubed error (ISTCE), and integrated time exponential time squared error (ITETSE), that are employed for obtaining optimal controller parameters [19,20]. Also, iterative methods such as numerical optimization, fminsearch subroutine, and soft computing-based optimization methods, which include artificial bee colony (ABC) optimization, particle swarm optimization (PSO), genetic algorithm, cuckoo optimization, and quasi-opposition-based equilibrium optimizer, are used to minimize the cost functions [21][22][23][24][25].…”