Resonance frequency tracking method for multi-degree-of-freedom resonant systems based on adaptive extremum seeking control

Adaptive extremum-seeking control of a continuous stirred tank bioreactor with Haldane's Kinetics

On-line optimization of fedbatch bioreactors by adaptive extremum seeking control

On-line optimization of fed-batch bioreactors by adaptive extremum seeking control

To maintain the maximum achievable efficiency for the photovoltaic (PV) systems, it is crucial to achieve the maximum power point tracking (MPPT) operation for realistic illumination conditions. This paper presents the application of the adaptive extremum seeking control (AESC) scheme to the PV MPPT problem. A state-space model is derived for the PV system with buck converter. The AESC is used to maximize the power output by tuning the duty ratio of the pulse-width modulator (PWM) of the DC-DC buck converter. To address the nonlinear PV characteristics, the radial basis function (RBF) neural network is used to approximate the unknown nonlinear I-V curve. The convergence of the system to an adjustable neighborhood of the optimum is guaranteed by utilizing a Lyapunov-based adaptive control method. The performance of the controller is verified through simulations.

Due to the relatively higher cost of energy (COE) for the photovoltaic (PV) systems, it is crucial to locate the maximum power point (MPP) so as to increase the system efficiency. The nonlinear PV characteristic curve and the MPP depend on PV’s intrinsic characteristics and environment conditions such as solar irradiation intensity and temperature. Maximum power point tracking (MPPT) control serves to seek the MPP of the PV system with the unpredicted environment uncertainties. In this paper, the adaptive extremum seeking control (AESC) scheme is investigated for the PV MPPT, which optimizes the duty ratio for the pulse-width modulator (PWM) of the DC-DC converter. The adopted AESC scheme utilizes an explicit structure information of the PV-buck system based on the system states and unknown PV characteristics. The radial basis function (RBF) neural network has been used to approximate the unknown nonlinear I-V curve. A Lyapunov-based adaptive learning control technique is used to ensure the convergence of the system to a neighborhood of the optimum which depends on the approximation error. The performance of the controller is verified through simulation.

A time-varying extremum-seeking control approach

This paper considers the solution of a real-time optimization problem using adaptive extremum seeking control. It is assumed that the equations describing the dynamics of the nonlinear system and the cost function to be minimized are unknown and that the objective function is measured. The main contribution of the paper is to formulate the extremum-seeking problem as a time-varying estimation problem. The proposed approach is shown to avoid the need for averaging results which minimizes the impact of the choice of dither signal on the performance of the extremum seeking control system. A simulation is used to illustrate the effectiveness of the proposed technique.

Dynamic response of a system varies widely depending on environmental condition or operating condition. A performance function respect to efficiency of system, gain or cost, will be kept extremum value by extremum seeking control even if environmental condition varies. Adaptive extremum seeking control that the system includes unknown parameters, have ever been investigated, however, it is difficult to apply to the index with time delay and disturbance because of its stability. In this paper, by introducing a method of sliding mode control, we will present a method of nonlinear adaptive extremum control with time delay and disturbance. The result checking the availability of our method based on computer simulation.

This paper considers the solution of a minmax optimization problem using adaptive extremum seeking control. It is assumed that the equations describing the dynamics of the nonlinear system and the cost function to be minimized are unknown and that the objective function is measured. The appropriate selection of the minimizing and the maximizing inputs is assumed to be known a priori. The proposed extremum-seeking control technique uses a time-varying estimation of the unknown gradients that minimizes the impact of the choice of dither signal on the performance of the extremum seeking control system. A simulation example is used to illustrate the effectiveness of the proposed technique.

Adaptive Extremum Seeking Control of Continuous Stirred Tank Bioreactors 1

Extremum seeking control for a mass structured cell population balance model in a bioreactor

In this paper, we present an adaptive extremum seeking control scheme for nonisothermal continuous stirred tank reactors subject to reactor temperature constraints. Only limited knowledge of the reaction kinetics is assumed with no direct measurement of the reaction mixture composition. An adaptive learning technique is introduced to construct an optimum seeking algorithm that drives the system states to optimal equilibrium concentrations of the reaction mixture taking into account reactor temperature constraints. Lyapunov's stability theorem is used in the design of the extremum seeking controller structure and the development of the parameter learning laws. Under mild assumptions, the resulting controller is an output-feedback controller. The performance of the technique is demonstrated with the van de Vusse reaction.

Adaptive extremum seeking control of continuous stirred tank bioreactors with unknown growth kinetics

Adaptive Extremum Seeking Control of Nonisothermal Continuous Stirred Tank Reactors 1

This paper considers the solution of a minmax optimization problem using adaptive extremum seeking control. It is assumed that the equations describing the dynamics of the nonlinear system and the cost function to be minimized are unknown and that the objective function is measured. The appropriate selection of the minimizing and the maximizing inputs is also assumed to be known a priori. The proposed extremum-seeking control technique uses a time-varying estimation of the unknown gradients that minimizes the impact of the choice of dither signal on the performance of the extremum seeking control system. Two examples are used to illustrate the effectiveness of the proposed technique.