Optimal control strategy based on neural model of nonlinear systems and evolutionary algorithms for renewable energy production as applied to biofuel generation

Abstract

In the renewable energy generation, several processes require the integration of a set of advanced techniques in order to find optimal solutions. Dynamic estimation, stabilizing control for disturbance rejection, optimization for control effort, and parameter tuning are techniques used to address the whole process requirements and obtain optimal results. In this paper, an optimal control strategy for a maximum biofuel production in the presence of disturbances is proposed. First, an integrated optimal control strategy to maximize biofuel production in the presence of disturbances is proposed. Second, due to its high nonlinearity, complex nature, and multiplicity of equilibrium points, a biological process for biofuel generation is described in order to demonstrate the efficiency of the optimal control strategy. A nonlinear discrete-time neural observer for unknown nonlinear systems in the presence of external disturbances and parameter uncertainties is used to estimate unmeasurable variables. An inverse optimal control law for trajectory tracking based on the neural observer is designed such that asymptotic convergence reference trajectory is guaranteed. Differential Evolution and Clonal Selection Algorithms are used to calculate the optimal parameters for neural network training, neural network gains, and feedback control gains. Additionally, a supervisory fuzzy control is proposed in order to select the adequate control action between the closed loop and the open loop and to determine optimal reference trajectories. Simulation results comparison and statistical validation are presented, where it is demonstrated that the optimal control strategy integrated with the Differential Evolution algorithm gives better results to maximize the biofuel production in the presence of disturbances.

Inverse Optimal Control Using Metaheuristics of Hydropower Plant Model via Forecasting Based on the Feature Engineering

“…then, the system (10) with input ( 12) is globally asymptotically stable at the equilibrium point x(k) = 0. Moreover, is inverse optimal in the sense that the input control minimizes the meaningful functional given by [16] J…”

“…The Particle Swarm Optimization (PSO) algorithm [33] is used in the IOC procedure to find the matrix that guarantees asymptotic stability in passivity CL-system [15][16][17]21].…”

“…Another trait as important as the management of the hydraulic head is trajectory tracking, in which a compromise between ensuring the desired output and the discharge evacuation through landfills are sought after. Several works exist within the literature that are aimed to tackle this problem, especially those that describe the IOC technique as an alternative for referenced tracking, in which a passive system is constructed through the feedback of the output, ensuring its stability, and optimizing its response [14][15][16]. However, despite its good performance, this technique is not suitable in the presence of parametric variations within the system [17].…”

Modelado neuronal de un proceso de digestión aeróbica de aguas residuales

“…Esta red es entrenada con un algoritmo basado en un Extendend Kalman Filter (EKF). [13][14][15][16], el objetivo filtro de Kalman es diseñar un observador óptimo que estime los estados de un sistema con ruido blanco en la salida y en los estados. Considerando el sistema discreto siguiente:…”

A TITO Control Strategy to Increase Productivity in Uncertain Exothermic Continuous Chemical Reactors

“…Heuristic optimization is another control approach, which does not usually involve assumptions about the problem to be optimized. The heuristic approach can search large spaces of candidate solutions to identify optimal or near-optimal solutions at a reasonable computational cost, but it is unable to guarantee either feasibility or optimization, and, in many cases, it does not indicate how close a certain feasible solution is to the optimum [33][34][35]. A wide range of direct search methods have been developed from heuristic optimization, such as genetic algorithms, evolutionary programming, differential evolution, genetic programming, evolutionary strategy, particle swarm optimization, and artificial bee colonies [36,37]; however, a theoretical analysis of convergence is not available, which is a major shortcoming [16,38].…”