Adaptive control of nonlinear systems via approximate linearization

This paper presents an adaptive control scheme for nonlinear systems that violates some of the common regularity and structural conditions of current nonlinear adaptive schemes such as involutivity, existence of a well-defined relative degree, and minimum phase property. While the controller is designed using an approximate model with suitable properties, the parameter update law is derived from an observation error based on the exact model described in suitable coordinates. The authors show that this approach results in a stable, closed-loop system and achieves adaptive tracking with bounds on the tracking error and parameter estimates. The authors also present a constructive procedure for adaptive state regulation which is based on the quadratic linearization technique via dynamic state feedback. This regulation scheme does not impose any restriction on the location of the unknown parameters and is applicable to any linearly controllable nonlinear system     

First-order corrections in optimal feedback control of singularly perturbed nonlinear systems

In this paper first-order correction terms, developed by using the method of matched asymptotic expansions, are incorporated in the feedback solution of a class of singularly perturbed nonlinear optimal control problems frequently encountered in aerospace applications. This improvement is based on an explicit solution of the integrals arising from the first-order matching conditions and leads to correct the initial values of the slow costate variables in the boundary layer. Consequently, a uniformly valid feedback control law, corrected to the first-order, can be synthesized. The new method is applied to an example of a constant speed minimum-time interception problem. Comparison of the zeroth- and first-order feedback control laws to the exact optimal solution demonstrates that first-order corrections greatly extend the domain of validity of the approximation obtained by singular perturbation methods.     

On approximate linearization of nonlinear control systems

A method for the approximate linearization of nonlinear control systems based on the ‘state-space exact linearization’ method is presented. An explicit procedure, both for the single-input and for the multiple-input case, is given, which is straightforward to implement.     

Global external linearization of nonlinear systems via feedback

This note presents necessary conditions and sufficient conditions for an affine nonlinear system to be globally feedback equivalent to a controllable linear system over an open subsetVof Rⁿ. WhenVequals Rⁿ, necessary and sufficient conditions are obtained.     

Economic model predictive control of nonlinear singularly perturbed systems

We focus on the development of a Lyapunov-based economic model predictive control (LEMPC) method for nonlinear singularly perturbed systems in standard form arising naturally in the modeling of two-time-scale chemical processes. A composite control structure is proposed in which, a “fast” Lyapunov-based model predictive controller (LMPC) using a quadratic cost function which penalizes the deviation of the fast states from their equilibrium slow manifold and the corresponding manipulated inputs, is used to stabilize the fast dynamics while a two-mode “slow” LEMPC design is used on the slow subsystem that addresses economic considerations as well as desired closed-loop stability properties by utilizing an economic (typically non-quadratic) cost function in its formulation and possibly dictating a time-varying process operation. Through a multirate measurement sampling scheme, fast sampling of the fast state variables is used in the fast LMPC while slow-sampling of the slow state variables is used in the slow LEMPC. Appropriate stabilizability assumptions are made and suitable constraints are imposed on the proposed control scheme to guarantee the closed-loop stability and singular perturbation theory is used to analyze the closed-loop system. The proposed control method is demonstrated through a nonlinear chemical process example.     

Constrained feedback control of imperfectly known singularly perturbed non-linear systems

Global attractors are investigated for a class of imperfectly known, singularly perturbed, dynamic control systems. The uncertain systems are modelled as non-linear perturbations to a known non-linear idealized system and are represented by two time-scale subsystems. The two subsystems, which depend on a scalar singular perturbation parameter, represent a singularly perturbed system which has the property that the system reduces to one of lower order when the singular perturbation parameter is set to zero. It is assumed that the full-order system is subject to constraints on the control inputs. A class of constrained feedback controllers is developed which assures global uniform attraction of a compact set, containing the state origin, for all values of the singular perturbation parameter less than some threshold value.     

Robust stabilization of singularly perturbed nonlinear systems

Consider a singularly perturbed nonlinear system endowed with a control input and suppose a static nonlinear feedback law is designed. We show that the operations ‘compute the reduced order system’ (i.e., let the singular perturbation parameter μ = 0) and ‘close the feedback loop’ commute, i.e. the closed loop reduced-order system is unambiguously determined. We then show that, if the reduced order system associated with the original system is stabilizable or has uncertainties matched with the input (a condition frequently used in the design of robust controllers), then the closed loop reduced-order system enjoys the same property. As shown by an example, this result can be used to simplify the structure of a composite controller.     

Input linearization of nonlinear systems via pulse-width control

In this note, it is shown that a general nonlinear system can be transformed to an affine nonlinear system by virtue of the use of pulsewidth control. Therefore, the combined system from the pulse width control input to the nonlinear system output behaves as an affine (linear-in-control) system. By this way, a cumbersome nonlinear system model can be transformed to a simpler linear-in-control form without increasing the system dimension; this in turn enables simpler control design. Furthermore, using this methodology, some control design methods developed only for affine systems can be adapted to general nonlinear systems.     

On linearization of nonlinear singularity perturbed systems

This note considers external (feedback) linearization of nonlinear singularly perturbed systems. When more accurate representation of some dynamic systems is taken into account. The necessary condition for linearization may be violated. In these cases the slow manifold theory is shown to provide a practical tool for solving this problem. Furthermore, for a class of systems with fast actuators, direct application of present methodology in the literature is shown to be valid provided the linear equivalent system contains fast dynamics as well.     

Composite control of non-linear singularly perturbed systems: a geometric approach

Using a geometric approach, a composite control—the sum of a slow control and a fast control—is derived for a general class of non-linear singularly perturbed systems. A new and simpler method of composite control design is proposed whereby the fast control is completely designed at the outset. The slow control is then free to be chosen such that the slow integral manifold of the original system approximates a desired design manifold to within any specified order of ε accuracy.     

Gain scheduling control of nonlinear systems based on approximate input-output linearization

The gain scheduling control mostly has been developed based on Jacobian linearization around the operating points related with scheduling variables. In this paper, We introduce a gain scheduling control method based on approximate input-output linearization. First, the nonlinear system is approximately input-output linearized via a diffeomorphism. Then, a gain scheduling controller with derivative information is developed. The proposed controller consists of two parts. The outer loop controller is like a feedback linearizing controller and the internal controller is a gain scheduling controller. It is shown that the overall resulting controller has a simple structure and at the same time achieves better tracking performance over the existing Jacobian-based gain scheduling controller.     

Multivariable singularly perturbed feedback systems with time delay

Multivariable singularly perturbed systems with both small and large time delays in the feedback loop are considered. Conditions for stability of the full-order closed-loop system are given in terms of properties of the slow and fast subsystems.     

Disturbance-observer-based LQR control of singularly perturbed systems via recursive decoupling methods

This paper addresses the disturbance-observer-based linear quadratic regulation (LQR) problem of singularly perturbed systems subject to harmonic disturbances. To reduce the computational burden and facilitate the on-board implementation, a recursive matrix decoupling method is proposed to decompose the stiff singularly perturbed model into the block-diagonal form. Then, a robust LQR controller is designed based on the equivalent decoupled system, which consists of a state feedback controller for performance optimisation and a robust controller for disturbance compensation. Moreover, the designed controller is integrated with a finite frequency disturbance observer to attenuate the effect of harmonic disturbances. Finally, a simulation example on a DC motor system is provided to verify the effectiveness of the proposed design method.     

Non-regular feedback linearization of nonlinear systems via a normal form algorithm

In this paper, the problem of non-regular static state feedback linearization of affine nonlinear systems is considered. First of all, a new canonical form for non-regular feedback linear systems is proposed. Using this form, a recursive algorithm is presented, which yields a condition for single input linearization. Then the left semi-tensor product of matrices is introduced and several new properties are developed. Using the recursive framework and new matrix product, a formula is presented for normal form algorithm. Based on it, a set of conditions for single-input (approximate) linearizability is presented.     

Exact design manifold control of a class of nonlinear singularly perturbed systems

The integral manifold approach captures from a geometric point of view the intrinsic two-time-scale behavior of singularly perturbed systems. An important class of nonlinear singularly perturbed systems considered in this note are fast actuator-type systems. For a class of fast actuator-type systems, which includes many physical systems, an explicit corrected composite control, the sum of a slow control and a fast control, is derived. This corrected control will steer the system exactly to a required design manifold.     

Stability analysis of nonlinear multiparameter singularly perturbed systems

Asymptotic stability of nonlinear multiparameter singularly perturbed systems is analyzed. Sufficient conditions for existence of a Lyapunov function and uniform asymptotic stability are derived. The new feature of these conditions over earlier results is that there is no restriction on the relative magnitudes of the small singular perturbation parameters. Moreover, the class of systems under consideration can be nonlinear in both the slow and fast variables, while earlier results were limited to systems linear in the fast variables.     

Asymptotic stability of nonlinear multiparameter singularly perturbed systems

Sufficient conditions are derived to guarantee the asymptotic stability of a class of nonlinear singularly perturbed systems with several perturbation parameters of the same order. Estimates of the region of attraction and bounds on the small parameters are obtained.     

Gain scheduling control of nonlinear singularly perturbed time-varying systems with derivative information

In this paper, we analyse the ultimate boundedness of nonlinear singularly perturbed time-varying systems and propose a control law using gain scheduling where the slow state and the exogenous signals are used as scheduling variables. In our control scheme, we have some flexibility in selecting the slow manifold of the system. Moreover, the derivative information can be properly engaged to manipulate the size of ultimate bound in tracking error of the controlled system.     

On the model-based networked control for singularly perturbed systems with nonlinear uncertainties

In this paper, the model-based networked control is addressed for a class of singularly perturbed control systems with nonlinear uncertainties. An approximate linear slow and fast control system of the plant, which can be obtained by omitting the nonlinear uncertainties, are used as a model to estimate the state behavior of the plant between transmission times. The stability of model-based networked control systems is investigated under the assumption that the controller/actuator is updated with the sensor information at constant time intervals. It is shown that there exists the allowable upper bound of the singular perturbation parameter such that the model-based networked control system is globally exponentially stable.