Global output‐feedback stabilization for nonlinear systems with unknown relative degree

In this paper finite-time stabilization of nonlinear affine systems, is considered. First we provide a formula for a finite-time stabilizing state feedback control law, for the last class of system. Then we extend the result to the case of a norm-bounded uncertain system and a polytopic uncertain system stabilization are given. Some numerical examples are given to illustrate the main result.

In this article, a policy-iteration-based active disturbance rejection control (ADRC) is proposed for uncertain nonlinear systems to achieve real-time output tracking performance, regardless of the specific relative degree of the system. The approach integrates a partial control input generator with a policy-iteration-based reinforcement learning (RL) agent for degree weight adjustment. The partial control input generator includes each ith order partial control input, which is constructed following the ADRC design framework for an ith order system. The RL agent adjusts the degree weights (its actions) to enhance the dominance of the partial control input corresponding to the unknown relative degree through iterative policy refinement. The RL agent is designed to minimize the quadratic reward as the performance index function while enhancing the influence of the partial control input associated with the correct relative degree via the policy iteration procedure. All signals in the closed-loop system (including the time-varying degree weights) ensure semi-global uniformly ultimately boundness using the Lyapunov stability theorem and the affinely quadratically stable property. Consequently, the degree weight adjustments by the RL agent do not affect the closed-loop stability. The proposed method does not require system dynamics, specific relative degree, external disturbances, and other state variable sensing beyond output sensing. The performance of the proposed method was validated via simulations for two different-order uncertain nonlinear systems and experiments using a permanent magnet synchronous motor testbed.

Adaptive regulation for minimum phase systems with unknown relative degree and uncertain exosystems

On observer-based fault detection for nonlinear systems

The problem of local stabilization of nonlinear control systems with linearizations that contain uncontrollable modes on the imaginary axis is considered. A methodology of designing a stabilizing control is investigated. It involves the following steps: (1) reduction of the stability problem to the stability of the center manifold system, (2) simplification of the vector field on the center manifold using the theory of normal forms, and (3) finding conditions under which the simplified vector field is asymptotically stable. Three cases of degeneracies in the linearized system are treated, and sufficient conditions for the existence of stabilizing controls are given in each case. A theorem is presented regarding the robustness of the above control strategies. >

Nonlinear PI control of uncertain systems: an alternative to parameter adaptation

Theproblem ofapproximatedecoupling of nonlinear systemsby outputfeedback is considered. The structure of the controller is high-gain proportional plus integral. Linearization of the nonlinear closed-loop equations results in the standard singular perturbation form. Based on the singular perturbation concept, a control law is found that accomplishes approximate decoupling with stability for nonlinear systems. Application oftheproposed design schemeto thefullsix-degree-of-freedomnonlinearaircraftdynamicsmodelispresented,demonstrating itsvalidity. I. Introduction A SYSTEM is said to be decoupled if each component of the output depends only on the corresponding component of the input. The problem of decoupling a multivariable system has been extensively treated in the literature in the past 30 years. The necessary and sufe cient conditions were developed for linear 1;2 and nonlinear 3i6 systems. However, no systematic procedures for the controller design were considered. Nonlinear system controller design is typically approached by linearizing the system about a nominal operating point and applying linear system design methods. This will provide a linear control law that yields approximate noninteracting control and satisfy design objectives in the neighborhood of the constant operating point. For the purpose of this work, the design objectives for this highly coupled system of e ight dynamics are to closely track individual desired outputs, with zero steady-state response from others. However, the problem of computing the decoupling feedback control laws for a nonlinear system is quite dife cult. Approximate decoupling control for nonlinear systems is based onsingularperturbationconcepts.Thestructureofcontrollerishighgain output feedback with proportional plus integral (PI) control.It will be shown that the linearization of nonlinear closed-loop system about the nominal operating point yields the standard singular perturbation equation form when a scalar gain weighting g ! 1. Then,thesingularperturbationconcept,i.e.,necessaryandsufe cient conditions, is utilized to e nd a decoupling control law. This approach is a new, conceptually simple, but practically useful design method for approximate noninteracting control law design with stability for nonlinear systems. The results developed are easy to follow and are easily applicable to aircraft control decoupling problems. This paper introduces the application of this design method to approximate noninteracting e ight control system design.

We establish necessary and sufficient conditions for the solvability of a Lyapunov-type system of PDEs in the class of homogeneous functions. Using these, we propose an approach to studying the stability of an equilibrium of an essentially nonlinear system of ODEs in the critical case of n zero roots and n pure imaginary roots. The approach bases on decomposition of the system in question into two separate subsystems of half dimension.

Due to the existence of dynamical uncertainties, it is important to pay attention to the robustness of nonlinear control systems, especially when designing adaptive critic control strategies. In this paper, based on the neural network learning component, the robust stabilization scheme of nonlinear systems with general uncertainties is developed. Through system transformation and employing adaptive critic technique, the approximate optimal controller of the nominal plant can be applied to accomplish robust stabilization for the original uncertain dynamics. The neural network weight vector is very convenient to initialize by virtue of the improved critic learning formulation. Under the action of the approximate optimal control law, the stability issues for the closed-loop form of nominal and uncertain plants are analyzed, respectively. Simulation illustrations via a typical nonlinear system and a practical power system are included to verify the control performance.

This paper addresses the global stabilization via adaptive output‐feedback for a class of uncertain nonlinear systems. Remarkably, the systems under investigation are with multiple uncertainties: unknown control directions, unknown growth rates and unknown input bias, and can be used to describe more physical plants. Multiple uncertainties, which usually cannot be compensated by a sole compensation technique, may give rise to big technical difficulty for controller design. To overcome such difficulty and to achieve the global stabilization, a new adaptive output‐feedback scheme is proposed in this paper, by flexibly combining Nussbaum‐type function, tuning function technique and extended state observer. It is shown that, under the designed controller, the system states globally converge to zero. A simulation example on non‐zero set‐point regulation is given to demonstrate the effectiveness of the theoretical results.

The stabilization of nonlinear stochastic systems with input and output delays is investigated in this article. First, an observer that includes input delay information is designed to estimate the state information after eliminating the effect of input delay. Second, a predictive scheme with delay information is utilized to predict the system state affected by output delay, and an event‐triggered predictive controller is established to eliminate the effects of input and output delays simultaneously. Subsequently, the convergence of the prediction error is analyzed by utilizing a recursive method, and a sufficient condition to ensure the global exponential stability is given by constructing a new closed‐loop system. Finally, numerical simulations of two nonlinear stochastic systems with different delay sizes are utilized to verify the impact of the event‐triggered predictive control in addressing the stabilization of nonlinear stochastic systems with time delays.

Nonlinearities and system uncertainties are the most important difficulties in designing controllers that ensure stability and acceptable closed-loop performance. Many significant results on the stability and robust control of uncertain nonlinear systems using the TS fuzzy model have been reported over the past decades, and considerable advances have been made (Connor et al. 1992; Chen et al. 1996; Rocha et al. 2005; Boukhezzar et al. 2006; Kamal et al. 2011, 2012a, 2013; Khanl et al. 2011). However, as stated in Khan and Hossain (2011), many approaches for stability and robust control of uncertain systems are often characterized by conservatism when dealing with uncertainties. It has been well known that the TS fuzzy model is very effective representation of complex nonlinear systems. In the TS fuzzy model, the state space of a nonlinear system is divided into different fuzzy regions with a local linear model being used in each region. The overall model output is obtained by defuzzification using the center of gravity (COG) method. Once the fuzzy model is obtained, control design can be carried out via the so-called parallel and distributed computing (PDC) approach, which employs multiple linear controllers corresponding to the locally linear plant models (Boukhezzar et al. 2006). This class of systems is described as a weighted sum of some simple linear subsystems, and thus is easily analyzable.

The design of an adaptive output feedback compensator is addressed to reject biased multi-sinusoidal disturbances of unknown amplitudes, phases and frequencies, acting on unknown stable single-input single-output linear systems of unknown order and relative degree. A single biased sinusoidal disturbance is first considered. It is shown that the knowledge of the sign of the real part (or the imaginary part) of its transfer function at zero and at the disturbance frequency is sufficient for the explicit design of an adaptive compensator which guarantees local exponential convergence to zero of the system output and of the frequency estimation error. This result is generalized to the case of biased multi-sinusoidal disturbances. As a special case, an adaptive notch filter which provides the unknown frequencies contained in the disturbance is obtained.

Global finite-time adaptive stabilization for nonlinear systems with multiple unknown control directions

Learning-based robust output tracking control for unknown discrete-time nonlinear systems with dynamic uncertainty