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

A brief review of neural networks based learning and control and their applications for robots

As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation

Deterministic learning enhanced neutral network control of unmanned helicopter

In this article, a neural network-based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid without any concern on either initial conditions or range of state variables. In addition, deterministic learning is applied to the neutral network learning control, such that the adaptive neutral networks are able to store the learned knowledge that could be reused to construct neutral network controller with improved control performance. Simulation studies are carried out on a helicopter model to illustrate the effectiveness of the proposed control design

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

Ph.DDOCTOR OF PHILOSOPH

Nonlinear self-tuning control for power oscillation damping

Power systems exhibit nonlinear behavior especially during disturbances, necessitating the application of appropriate nonlinear control techniques. Lack of availability of accurate and updated models for the whole power system adds to the challenge. Conventional damping control design approaches consider a single operating condition of the system, which are obviously simple but tend to lack performance robustness. Objective of this research work is to design a measurement based self-tuning controller, which does not rely on accurate models and deals with nonlinearities in system response. Designed controller is required to ensure settling of inter-area oscillations within 10−12s, following disturbance such as a line outage. The neural network (NN) model is illustrated for the representation of nonlinear power systems. An optimization based algorithm, Levenberg-Marquardt (LM), for online estimation of power system dynamic behavior is proposed in batch mode to improve the model estimation. Careful study shows that the LM algorithm yields better closed loop performance, compared to conventional recursive least square (RLS) approach with the pole-shifting controller (PSC) in linear framework. Exploiting the capability of LM, a special form of neural network compatible with feedback linearization technique, is applied. Validation of the performance of proposed algorithm is done through the modeling and simulating heavy loading of transmission lines, when the nonlinearities are pronounced. Nonlinear NN model in the Feedback Linearization (FLNN) form gives better estimation than the autoregressive with an external input (ARX) form. The proposed identifier (FLNN with LM algorithm) is then tested on a 4−machine, 2−area power system in conjunction with the feedback linearization controller (FBLC) under varying operating conditions. This case study indicates that the developed closed loop strategy performs better than the linear NN with PSC. Extension of FLNN with FBLC structure in a multi-variable setup is also done. LM algorithm is successfully employed with the multi-input multi-output FLNN structure in a sliding window batch mode, and FBLC controller generates multiple control signals for FACTS. Case studies on a large scale 16−machine, 5−area power system are reported for different power flow scenarios, to prove the superiority of proposed schemes: both MIMO and MISO against a conventional model based controller. A coefficient vector for FBLC is derived, and utilized online at each time instant, to enhance the damping performance of controller, transforming into a time varying controller

Nussbaum gain based iterative learning control for a class of multi-input multi-output nonlinear systems.

YesAn adaptive iterative learning control(ILC) approach is proposed for a class of multi-input multi-output (MIMO) uncertain nonlinear systems without prior knowledge about system control gain matrices. The Nussbaum-type gain and the positive definite discrete matrix kernel are proposed for dealing with selection of the unknown control gain and learning of the repeatable uncertainties, respectively. Asymptotic convergence for a trajectory tracking within a finite time interval is achieved through repetitive tracking. Simulations are carried out to show the validity of the proposed control method

Control optimization, stabilization and computer algorithms for aircraft applications

The analysis and design of complex multivariable reliable control systems are considered. High performance and fault tolerant aircraft systems are the objectives. A preliminary feasibility study of the design of a lateral control system for a VTOL aircraft that is to land on a DD963 class destroyer under high sea state conditions is provided. Progress in the following areas is summarized: (1) VTOL control system design studies; (2) robust multivariable control system synthesis; (3) adaptive control systems; (4) failure detection algorithms; and (5) fault tolerant optimal control theory