Gain scheduled controllers for dynamic systems using sector nonlinearities

This paper describes how gain scheduling control is applied to a continuous stirred tank reactor model. The CSTR process, equipped with a cooling system, is modelled. Based on the resulting nonlinear model, a gain scheduling controller is designed. The gain scheduling follows a scheme denoted bias compensation. Compared to earlier reported gain scheduling schemes, the proposed scheme results in a controller that is less complex, which is advantageous in the controller implementation stage. Numerous simulations are performed, using the gain scheduling controller and two choices of control input to the reactor model. The simulation results indicate that a gain scheduling controller performs better than a linear controller. Simulations using different choices of control input to the reactor model indicate that proper process design is crucial for the controlled process performance.

LPV VS MULTI-MODEL PI(D) GAIN-SCHEDULING APPLIED TO CANAL CONTROL

Gain Scheduling (GS) controller is one of the most popular approaches to nonlinear control design. Commonly, Gain Scheduling (GS) controllers have better performance than robust ones. In this paper, the structure of a proposed gain scheduled PID fuzzy controller (GS-PIDF) designed and analyzed. The Fuzzy PID controller gains weighted through gain scheduling designed system to grantee both the stability and the best performance of a multi links robot manipulator system. The overall designed controller system is applied to two links robot manipulator system, which is subject to modelling using Lagrangian dynamics method and simulation vie MatLab and Simulink package to minimize the vibration and acquire best performance.

This paper discusses control of seeded batch cooling crystallizers by supersaturation tracking control. Batch crystallization is characterized by nonlinear behavior throughout the complete batch period. In this paper, state feedback linearization is used to create a linear input-output mapping for the absolute supersaturation. It is shown that by implementation of a nominal state feedback law, the nonlinear model can be represented by an uncertain linear model with an input disturbance. This method can be used to account for parametric model uncertainty in the design of a robust ℋ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -controller. Simulation results of control with an ℋ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -controller and a Model Predictive Controller (MPC) in combination with state feedback linearization reported.

Gain-scheduling robust control with guaranteed stability for ball screw drives with uncertain load mass and varying resonant modes

This paper shows the theoretical development and application of dynamic gain scheduled control to an aircraft model. The idea behind this method is to schedule the control law gains with a fast varying state variable rather than with a slow varying state or an input parameter. This type of gain scheduling is shown to improve the transient response of the aircraft model when stepping between trim conditions and to overcome some of the problems associated with conventional gain scheduled controllers. ‘Hidden coupling terms’ that introduce unwanted dynamics when scheduling gains with a fast state are, under specific conditions, eliminated directly by applying a transformation to the classical parameterscheduled gain distributions which are calculated using eigenstructure assignment. Whilst dynamic gain scheduling has previously been implemented on aircraft models, the study discussed in this paper seeks to address practical application issues on more complex wideenvelope nonlinear aircraft models; a mathematical model of an F16 aircraft has been selected for this purpose.

In this paper, LPV identification and gain-scheduling (GS) control of a sewer system composed by a detention tank and a single reach sewer is proposed. First, a linear parameter-varying (LPV) control-oriented model for the single reach canal is presented and identified using LPV identification methods. Then, LPV GS theory is used to designing a PI GS controller guaranteeing performance and stability. Finally, results using the designed LPV GS controller is compared with the ones obtained using classical GS.

Recent theoretical developments have improved the understanding of gain scheduled control and suggested new methods for design, analysis and implementation of such nonlinear control systems. An integrated software environment for gain scheduled local controller network design and analysis, including computer-aided modelling and system identification, is described. Some background theory is included, and a speed control design problem for an experimental vehicle illustrates the application of the approach.

In this paper we propose a nonlinear H 2 control to improve transient response in the position control and to reduce energy consumption for the position tracking of Sawyer motors. The proposed method consists of a nonlinear torque modulation with a commutation scheme and gain scheduling LPV controller. The position control problem for the Sawyer motor is reformulated into the LPV system optimal problem. The nonlinear models of the Sawyer motors are analyzed as a LPV system. However, in LPV formulation, each vertex to guarantee the controllability should be chosen because of relationship between tracking motion and yaw motion. Hence, stabilizing the inner-loop system with state feedback controller is designed to solve above problem. The control gain scheduling is determined by using H 2 control based on linear matrix inequality (LMI) approach. Since the proposed method is designed based on optimal control with gain scheduling based on LPV synthesis, the proposed method obtains both improved transient response and reduced energy consumption in the position control.

The problem of robust global stabilization of linear systems subject to input saturation and input‐additive uncertainties is revisited in this paper. By taking advantages of the recently developed parametric Lyapunov equation‐based low gain feedback design method and an existing dynamic gain scheduling technique, a new gain scheduling controller is proposed to solve the problem. In comparison with the existing ℋ2‐type gain scheduling controller, which requires the online solution of a state‐dependent nonlinear optimization problem and a state‐dependent ℋ2 algebraic Riccati equation (ARE), all the parameters in the proposed controller are determined a priori. In the absence of the input‐additive uncertainties, the proposed controller also partially recovers Teel's ℋ∞‐type scheduling approach by solving the problem of global stabilization of linear systems with actuator saturation. The ℋ∞‐type scheduling approach achieves robustness not only with non‐input‐additive uncertainties but also requires the closed‐form solution to an ℋ∞ ARE. Thus, the proposed scheduling method also addresses the implementation issues of the ℋ∞‐type scheduling approach in the absence of non‐input‐additive uncertainties. Copyright © 2009 John Wiley &amp; Sons, Ltd.

In general, designing Gain-Scheduled (GS) controllers for Linear Parameter-Varying (LPV) systems using Parameter-Dependent Lyapunov Functions (PDLFs) yields controllers that depend on the derivatives of parameters. This makes their implementation impractical because ideal parameter derivatives are not available in the real world. In contrast to this, for open-loop system design, such as filter design and inverse system design, we propose a GS controller design method using PDLFs that gives controllers which do not depend on parameter derivatives. Although the proposed method uses structured PDLFs, we show that it is as conservative as existing methods. Numerical examples of designing GS filters and GS right inverse systems for LPV systems are included to demonstrate our conclusion.

Gain scheduling (GS) is one of the most popular approaches to nonlinear control design and it is known that GS controllers have better performance than robust controllers. Following the terminology in control engineering linear parameter-varying (LPV) systems are time-varying plants whose state space matrices are fixed functions of some vector of varying parameters. Our approach is based on considering the LPV system and scheduling parameter and their derivatives with respect to time are supposed to lie in a priori given hyper rectangles. For guarantee the performance we use the notion of guaranteed cost and optimised to given a cost function. The class of control structure includes decentralised fixed order output feedback like PID controller. Numerical examples illustrate the effectiveness of the proposed approach.

In this paper, a design methodology has been developed for the control of nonautonomous batch processes which typically require good tracking performance. The design, based on the framework of differential geometric methods and linear gain scheduling theory, involves a multiloop structure. The inner loop is an input-output linearizing state feedback law based on a nominal, time-invariant plant model. The outer loop includes a gain scheduling controller which provides the desired stability and tracking performance in the face of time varying parameters.

Dynamic gain scheduled process control

Neural Network SVS Gain Scheduling for Improving Power Systems Stability