Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach

10.1109/TNN.2008.2003290IEEE Transactions on Neural Networks19111873-1886ITNN

Adaptive Predictive Control Using Neural Network for a Class of Pure-feedback Systems in Discrete-time

10.1109/TNN.2008.2000446IEEE Transactions on Neural Networks1991599-1614ITNN

Lyapunov based optimal control of a class of nonlinear systems

Optimal control of nonlinear systems is in fact difficult since it requires the solution to the Hamilton-Jacobi-Bellman (HJB) equation which has no closed-form solution. In contrast to offline and/or online iterative schemes for optimal control, this dissertation in the form of five papers focuses on the design of iteration free, online optimal adaptive controllers for nonlinear discrete and continuous-time systems whose dynamics are completely or partially unknown even when the states not measurable. Thus, in Paper I, motivated by homogeneous charge compression ignition (HCCI) engine dynamics, a neural network-based infinite horizon robust optimal controller is introduced for uncertain nonaffine nonlinear discrete-time systems. First, the nonaffine system is transformed into an affine-like representation while the resulting higher order terms are mitigated by using a robust term. The optimal adaptive controller for the affinelike system solves HJB equation and identifies the system dynamics provided a target set point is given. Since it is difficult to define the set point a priori in Paper II, an extremum seeking control loop is designed while maximizing an uncertain output function. On the other hand, Paper III focuses on the infinite horizon online optimal tracking control of known nonlinear continuous-time systems in strict feedback form by using state and output feedback by relaxing the initial admissible controller requirement. Paper IV applies the optimal controller from Paper III to an underactuated helicopter attitude and position tracking problem. In Paper V, the optimal control of nonlinear continuous-time systems in strict feedback form from Paper III is revisited by using state and output feedback when the internal dynamics are unknown. Closed-loop stability is demonstrated for all the controller designs developed in this dissertation by using Lyapunov analysis --Abstract, page iv

Adaptive neural control of MIMO nonlinear systems with a block-triangular pure-feedback control structure

This paper presents adaptive neural tracking control for a class of uncertain multi-input-multi-output (MIMO) nonlinear systems in block-triangular form. All subsystems within these MIMO nonlinear systems are of completely nonaffine purefeedback form and allowed to have different orders. To deal with the nonaffine appearance of the control variables, the mean value theorem (MVT) is employed to transform the systems into a block-triangular strict-feedback form with control coefficients being couplings among various inputs and outputs. A systematic procedure is proposed for the design of a new singularityfree adaptive neural tracking control strategy. Such a design procedure can remove the couplings among subsystems and hence avoids the possible circular control construction problem. As a consequence, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded (SGUUB). Moreover, the outputs of the systems are ensured to converge to a small neighborhood of the desired trajectories. Simulation studies verify the theoretical findings revealed in this work

Adaptive control and neural network control of nonlinear discrete-time systems

Ph.DDOCTOR OF PHILOSOPH

Advanced Control of Small-Scale Power Systems with Penetration of Renewable Energy Sources

Stability, protection, and operational restrictions are important factors to be taken into account in a proper integration of distributed energy. The objective of this research is presenting advanced controllers for small-scale power systems with penetration of renewable energy sources resources to ensure stable operation after the network disturbances. Power systems with distributed energy resources are modeled and controlled through applying nonlinear control methods to their power electronic interfaces in this research. The stability and control of both ac and dc systems have been studied in a multi-source framework. The dc distribution system is represented as a class of interconnected, nonlinear discrete-time systems with unknown dynamics. It comprises several dc sources, here called subsystems, along with resistive and constant-power loads (which exhibit negative resistance characteristics and reduce the system stability margins.) Each subsystem includes a dc-dc converter (DDC) and exploits distributed energy resources (DERs) such as photovoltaic, wind, etc. Due to the power system frequent disturbances this system is prone to instability in the presence of the DDC dynamical components and constant-power loads. On the other hand, designing a centralized controller may not be viable due to the distance between the subsystems (dc sources.) In this research it is shown that the stability of an interconnected dc distribution system is enhanced through decentralized discrete-time adaptive nonlinear controller design that employs neural networks (NNs) to mitigate voltage and power oscillations after disturbances have occurred. The ac power system model is comprised of conventional synchronous generators (SGs) and renewable energy sources, here, called renewable generators (RGs,) via grid-tie inverters (GTI.) A novel decentralized adaptive neural network (NN) controller is proposed for the GTI that makes the device behave as a conventional synchronous generator. The advantage of this modeling is that all available damping controllers for synchronous generator, such as AVR (Automatic Voltage Regulator) + PSS (Power System Stabilizer), can be applied to the renewable generator. Simulation results on both types of grids show that the proposed nonlinear controllers are able to mitigate the oscillations in the presence of disturbances and adjust the renewable source power to maintain the grid voltage close to its reference value. The stability of the interconnected grids has been enhanced in comparison to the conventional methods