A study of gain scheduling control applied to an exothermic CSTR

Gain-scheduled Composite Non linear Feedback (CNF) control has been implemented to Continuous Stirred Tank Reactor (CSTR) in this work. Chemical reaction takes place inside the CSTR, which is adjured from high residual concentration to low residual concentration. Control of CSTR is a bit complex, as it requires complete dynamic study and its implementation. During the entire process, the reaction kinetics changes from stable-unstable-stable range with the decrease in residual concentration. To stabilize the reaction, appropriate cooling is necessary, to ensure that overheating of the CSTR doesn't occur. Gain-scheduled composite non linear feedback controller has been implemented successfully for better tracking performance and error has been computed in terms of Integral of Absolute Error (IAE). The novelty of the work lies in lesser error obtained for this particular model of CSTR. Comparison of the IAE has been done with Model Reference Adaptive Control (MRAC) MIT control in CSTR.

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

This paper presents a concept of the continuous-time gain scheduled 2DOF control for the nonlinear system of two funnel liquid tanks in series, based on linearization of nonlinear equations of the system about selected operating points. Discussed technique extends the idea of design via linearization approach. In order to achieve desired stability and performance requirements, a linear feedback controller is designed at each point. Based on this strategy, polynomial method is taken into account. Specifically, the exact pole placement method as well as weighting matrix, dividing weights among numerators of transfer function matrices of subcontrollers guarantee control quality. The parameters of the resulting family of linear controllers are scheduled as functions of reference variables, resulting in a single feedback matrix controller. The derived gain scheduled controller is implemented and applied on the illustrative nonlinear model of two funnel liquid tanks in series.

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.

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

Dynamic gain scheduled process control

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.

Response-spectrum-based design method for gain-scheduling control of active base-isolated structures with nonlinear viscous dampers

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.

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 & Sons, Ltd.

Gain scheduled controllers for dynamic systems using sector nonlinearities

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.

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.

Neural Network SVS Gain Scheduling for Improving Power Systems Stability

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.