In this chapter, the basic theory of feedback linearization is presented and issues of particular relevance to process control applications are discussed. Two fundamental nonlinear controller design techniques — input-output linearization and state-space linearization — are discussed in detail. The theory also is presented for linear systems to facilitate understanding of the nonlinear results....

An input/output feedback linearization control strategy is developed for control of three-level three-phase neutral-point-clamped rectifier using its dual Lagrangian modeling. The load current of the rectifier can be given in two forms: (i) the load current involving the current of capacitor C1 (ii) the load current involving the current of capacitor C2. Applying the obtained load current to th...

We address the problem of adaptive output-feedback stabilization of general first-order hyperbolic partial integro-differential equations (PIDE). Such systems are also referred to as PDEs with non-local (in space) terms. We apply control at one boundary, take measurements on the other boundary, and allow the system’s functional coefficients to be unknown. To deal with the absence of both full-s...

This paper investigates adaptive output feedback control problem for switched uncertain nonlinear systems with input quantization, unmeasured system states and state constraints. Firstly, fuzzy logic are introduced to identify uncertainties, then the based observer is constructed estimate unavailable states. Secondly, combing backstepping technique barrier Lyapunov function method, an law desig...

The nonlinearity and coupling of the missile dynamics can be linearized and decoupled by well known feedback linearization techniques. The fundamental problem of feedback linearization technique is not able to cater the model uncertainty. In accordance to this, a new robust nonlinear controller structure using feedback linearization technique is presented here. It linearizes the nonlinear dynam...

in this paper, an adaptive attitude control algorithm is developed based on neural network for a satellite using four reaction wheels in a tetrahedron configuration. then, an attitude control based on feedback linearization control has been designed and uncertainties in the moment of inertia matrix and disturbances torque have been considered. in order to eliminate the effect of these uncertain...

Adaptive robust control (ARC) of MIMO nonlinear systems transformable to two semi-strict feedback forms are considered. The forms can have both parametric uncertainties and uncertain nonlinearities such as external disturbances. In addition, the forms allow coupling and appearance of parametric uncertainties in the input matrix of each layer. Furthermore, the usual assumption on the linear para...

This paper proposes a trajectory tracking control for non-holonomic systems using dynamic feedback linearization based on piecewise multi-linear (PML) models. The approximated model is fully parametric. Input-output (I/O) dynamic feedback linearization is applied to stabilize PML control system. Although the controller is simpler than the conventional I/O feedback linearization controller, the ...

In this paper, an Adaptive Backstepping Neural Network control approach is used for a class of affine nonlinear systems which describe the pump model in the strict feedback form. The close loop signals are semi globally uniformly ultimately bounded and the output of the system is proven to follow a desired trajectory. Simulation results are presented to show the effectiveness of the approach pr...

The objective of this study is to design a robust direct model reference adaptive controller (DMRAC) for a nonlinear cardiovascular model over a range of plant parameters representing a variety of physical conditions. The direct adaptive controllers used in thisd study require the plant to be almost strictly positive real (ASPR) that is, for a plant to be controlled there must exist a feedback ...