Fernando Ornelas-Tellez

Particle Swarm Optimization for Discrete-Time Inverse Optimal Control of a Doubly Fed Induction Generator

In this paper, the authors propose a particle swarm optimization (PSO) for a discrete-time inverse optimal control scheme of a doubly fed induction generator (DFIG). For the inverse optimal scheme, a control Lyapunov function (CLF) is proposed to obtain an inverse optimal control law in order to achieve trajectory tracking. A posteriori, it is established that this control law minimizes a meaningful cost function. The CLFs depend on matrix selection in order to achieve the control objectives; this matrix is determined by two mechanisms: initially, fixed parameters are proposed for this matrix by a trial-and-error method and then by using the PSO algorithm. The inverse optimal control scheme is illustrated via simulations for the DFIG, including the comparison between both mechanisms.

Discrete-Time Neural Inverse Optimal Control for Nonlinear Systems via Passivation

This paper presents a discrete-time inverse optimal neural controller, which is constituted by combination of two techniques: 1) inverse optimal control to avoid solving the Hamilton-Jacobi-Bellman equation associated with nonlinear system optimal control and 2) on-line neural identification, using a recurrent neural network trained with an extended Kalman filter, in order to build a model of the assumed unknown nonlinear system. The inverse optimal controller is based on passivity theory. The applicability of the proposed approach is illustrated via simulations for an unstable nonlinear system and a planar robot.

Discrete-Time Inverse Optimal Control for Nonlinear Systems

Discrete-Time Inverse Optimal Control for Nonlinear Systems proposes a novel inverse optimal control scheme for stabilization and trajectory tracking of discrete-time nonlinear systems. This avoids the need to solve the associated Hamilton-Jacobi-Bellman equation and minimizes a cost functional, resulting in a more efficient controller. Design More Efficient Controllers for Stabilization and Trajectory Tracking of Discrete-Time Nonlinear Systems The book presents two approaches for controller synthesis: the first based on passivity theory and the second on a control Lyapunov function (CLF). The synthesized discrete-time optimal controller can be directly implemented in real-time systems. The book also proposes the use of recurrent neural networks to model discrete-time nonlinear systems. Combined with the inverse optimal control approach, such models constitute a powerful tool to deal with uncertainties such as unmodeled dynamics and disturbances. Learn from Simulations and an In-Depth Case Study The authors include a variety of simulations to illustrate the effectiveness of the synthesized controllers for stabilization and trajectory tracking of discrete-time nonlinear systems. An in-depth case study applies the control schemes to glycemic control in patients with type 1 diabetes mellitus, to calculate the adequate insulin delivery rate required to prevent hyperglycemia and hypoglycemia levels. The discrete-time optimal and robust control techniques proposed can be used in a range of industrial applications, from aerospace and energy to biomedical and electromechanical systems. Highlighting optimal and efficient control algorithms, this is a valuable resource for researchers, engineers, and students working in nonlinear system control.

Inverse optimal neural control of blood glucose level for type 1 diabetes mellitus patients

Abstract In this paper, inverse optimal neural control for trajectory tracking is applied to glycemic control of type 1 diabetes mellitus (T1DM) patients. The proposed control law calculates the adequate insulin delivery rate in order to prevent hyperglycemia and hypoglycemia levels in T1DM patients. Two models are used: (1) a nonlinear compartmental model in order to obtain type 1 diabetes mellitus virtual patient behavior, and (2) a neural model obtained from an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF); the last one allows the applicability of an inverse optimal neural controller. The proposed algorithm is tuned to track a desired trajectory; this trajectory reproduces the glucose absorption of a healthy person. The applicability of the proposed control scheme is illustrated via simulations.

Robust inverse optimal control for discrete-time nonlinear system stabilization

Abstract This paper presents an inverse optimal control approach in order to achieve stabilization of discrete-time nonlinear systems, avoiding the need to solve the associated Hamilton–Jacobi–Bellman equation, and minimizing a cost functional. Then, the proposed approach is extended to discrete-time disturbed nonlinear systems. The synthesized stabilizing optimal controller is based on a discrete-time control Lyapunov function. The applicability of the proposed approach is illustrated via simulations.

Speed-gradient inverse optimal control for discrete-time nonlinear systems

This paper presents a speed-gradient-based inverse optimal control approach for the asymptotic stabilization of discrete-time nonlinear systems. With the solution presented, we avoid to solve the associated Hamilton-Jacobi-Bellman equation, and a meaningful cost function is minimized. The proposed stabilizing optimal controller uses the speed-gradient algorithm and is based on the proposal of what is called a discrete-time control Lyapunov function. This combined approach is referred to as the speed-gradient inverse optimal control. An example is used to illustrate the methodology. Several simulations are provided.

Discrete-time inverse optimal neural control for synchronous generators

This paper presents a robust inverse optimal neural control approach for stabilization of discrete-time uncertain nonlinear systems, which simultaneously minimizes a meaningful cost functional. A neural identifier scheme is used to model the uncertain system, and based on this neural model and an appropriate control Lyapunov function, then the robust inverse optimal neural controller is synthesized. Applicability of the proposed scheme is illustrated via simulation results for a synchronous generator model.

Optimal control for non-polynomial systems

Abstract This paper shows that a large class of nonlinear systems can be recasted into polynomial ones via state variable embedding an idea used by Carleman many decades ago. Then, by representing the original nonlinear system as a polynomial system, one could apply powerful control techniques. In particular, in this work, an infinite-time state-feedback optimal regulator has been synthesized by solving the Hamilton–Jacobi–Bellman equation for the recasted polynomial system, achieving asymptotic stability and minimizing a cost functional. The prowess of the proposal is validated via simulations in the recasting and optimal control of two applications: a pendulum and a synchronous generator connected to an infinite bus.

Neural inverse optimal control for discrete-time uncertain nonlinear systems stabilization

This paper discusses neural inverse optimal control to achieve stabilization for discrete-time uncertain nonlinear systems, and minimizing a meaningful cost functional. A neural identifier scheme is used to model the uncertain nonlinear system, and based on this neural model and the knowledge of a control Lyapunov function, then the inverse optimal controller is synthesized in order to achieve exponential stability. An example illustrates the applicability of the proposed control technique.

Time series forecasting with genetic programming

Genetic programming (GP) is an evolutionary algorithm that has received a lot of attention lately due to its success in solving hard world problems. There has been a lot of interest in using GP to tackle forecasting problems. Unfortunately, it is not clear whether GP can outperform traditional forecasting techniques such as auto-regressive models. In this contribution, we present a comparison between standard GP systems qand auto-regressive integrated moving average model and exponential smoothing. This comparison points out particular configurations of GP that are competitive against these forecasting techniques. In addition to this, we propose a novel technique to select a forecaster from a collection of predictions made by different GP systems. The result shows that this selection scheme is competitive with traditional forecasting techniques, and, in a number of cases it is statistically better.