Discrete-Time Neural Network Output Feedback Control of Nonlinear Systems in Non-Strict Feedback Form

An adaptive neural network (NN)-based output feedback controller is proposed to deliver a desired tracking performance for a class of discrete-time nonlinear systems, which is represented in non-strict feedback form. The NN backstepping approach is utilized to design the adaptive output feedback controller consisting of: 1) a NN observer to estimate the system states with the input-output data, and 2) two NNs to generate the virtual and actual control inputs, respectively. The non-causal problem in the discrete-time backstepping design is avoided by using the universal NN approximator. The persistence excitation (PE) condition is relaxed both in the NN observer and NN controller design. The uniformly ultimate boundedness (UUB) of the closed-loop tracking error, the state estimation errors and the NN weight estimates is shown

Tracking with prescribed transient performance for hysteretic systems

Tracking of reference signals (assumed bounded with essentially bounded derivative) is considered for a class of single-input, single-output, nonlinear systems, described by a functional differential equation with a hysteresis nonlinearity in the input channel. The first control objective is tracking, by the output, with prescribed accuracy: determine a feedback strategy which ensures that, for every reference signal and every system of the underlying class, the tracking error ultimately satisfies the prescribed accuracy requirements. The second objective is guaranteed output transient performance: the graph of the tracking error should be contained in a prescribed set (performance funnel). Under a weak sector boundedness assumption on the hysteresis operator, both objectives are achieved by a memoryless feedback which is universal for the underlying class of systems

Robust bounded control for uncertain nonlinear systems: application to a nonlinear strict feedback wind turbine model with explicit wind speed dynamics.

In this paper, a robust bounded control law for a class of uncertain nonlinear systems is proposed. The proposed bounded controller guarantees asymptotic stability, asymptotic tracking and asymptotic disturbance rejection of systems in strict feedback form with the sum of unmatched uncertainties and the unbounded exogenous disturbance. A feedback law emerged from Artstein's Theorem and Sontag's universal formulas are known to be useful to limit the control signal. However, the formulas are not robust as in many cases, being applied to the systems without uncertainties and disturbances. The controller proposed in this paper takes advantages of a mixed backstepping and Lyapunov redesign, which employed to enrich the Sontag's universal formulas. Therefore, the appealing feature of the proposed controller is that it satisfies small control property in order to preserve performance robustness and stability robustness with less control effort. Another advantage of the proposed controller is the formulas become applicable to higher order systems (i.e. order > 0). This paper also discusses fuzzy logic tuning approach for the controller parameters such that the closed loop system matrix remain Hurtwitz. For practicality, the proposed technique is applied to a variable speed control of a new strict feedback wind turbine system with wind dynamics appeared explicitly in the system model. The proposed controller guarantees the asymptotic tracking of the turbine rotor speed; maintains the optimal tip speed ratio and produces maximum power coefficient. This yields maximum power output from the turbine

Global Practical Tracking by Output Feedback for Nonlinear Systems with Unknown Growth Rate and Time Delay

This paper is the further investigation of work of Yan and Liu, 2011, and considers the global practical tracking problem by output feedback for a class of uncertain nonlinear systems with not only unmeasured states dependent growth but also time-varying time delay. Compared with the closely related works, the remarkableness of the paper is that the time-varying time delay and unmeasurable states are permitted in the system nonlinear growth. Motivated by the related tracking results and flexibly using the ideas and techniques of universal control and dead zone, an adaptive output-feedback tracking controller is explicitly designed with the help of a new Lyapunov-Krasovskii functional, to make the tracking error prescribed arbitrarily small after a finite time while keeping all the closed-loop signals bounded. A numerical example demonstrates the effectiveness of the results

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

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

Putting energy back in control

A control system design technique using the principle of energy balancing was analyzed. Passivity-based control (PBC) techniques were used to analyze complex systems by decomposing them into simpler sub systems, which upon interconnection and total energy addition were helpful in determining the overall system behavior. An attempt to identify physical obstacles that hampered the use of PBC in applications other than mechanical systems was carried out. The technique was applicable to systems which were stabilized with passive controllers

On output feedback nonlinear model predictive control using high gain observers for a class of systems

In recent years, nonlinear model predictive control schemes have been derived that guarantee stability of the closed loop under the assumption of full state information. However, only limited advances have been made with respect to output feedback in connection to nonlinear predictive control. Most of the existing approaches for output feedback nonlinear model predictive control do only guarantee local stability. Here we consider the combination of stabilizing instantaneous NMPC schemes with high gain observers. For a special MIMO system class we show that the closed loop is asymptotically stable, and that the output feedback NMPC scheme recovers the performance of the state feedback in the sense that the region of attraction and the trajectories of the state feedback scheme are recovered for a high gain observer with large enough gain and thus leading to semi-global/non-local results