Synthesis of gain scheduling controllers for nonlinear batch processes

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

Data-driven two-dimensional integrated control for nonlinear batch processes

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.

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.

This paper summarizes recent progress in the area of estimation and control of batch processes. The task of designing effective strategies for the estimation of unmeasured variables and for the control of the important outputs of the process is linked to our need to optimize the process and its success is depended upon the availability of a process model. For this reason we will provide a substantial focus on the modeling issues that relate to batch processes. In particular we will focus attention on the approach developed in our group and referred to as “tendency modeling” that can be used for the estimation, optimization and control of batch processes. Several batch reactor example processes will be detailed to illustrate the applicability of the general approach. These relate to organic synthesis reactors and bioreactors. The point that distinguishes tendency modeling from other modeling approaches is that the developed Tendency Models are multivariable, nonlinear, and aim to incorporate all the available fundamental information about the process through the use of material and energy balances. These models are not frozen in time as they are allowed to evolve. Because they are not perfectly accurate they are used in the optimization, estimation and control of the process on a tentative basis as they are updated either between batches or more frequently. This iterative or adaptive modeling strategy also influences the controller design. The controller performance requirements and thus the need of a more accurate model increase as successive optimization steps guide the process operation near its constraints.

Model predictive control for nonlinear batch processes with asymptotically perfect tracking

For batch processes with small time delays and actuator partial fault, the existing methods based on iterative learning control still have some limitations, including the conservative and computationally burdensome of stability conditions and the limited fault-tolerant control capabilities. For this background, an iterative learning robust predictive fault-tolerant control method is developed, which integrates the Lyapunov–Razumikhin function method and derives stability conditions based on robust positive definite invariant set and terminal constraint set. With small time delays, the stability conditions of the system deduced using the Lyapunov–Razumikhin function are solved at a lower computational cost, which is due to the fact that the dimensionality of the stabilization condition is directly related to the size of the time delay, and thus the small time delay implies a lower dimensionality. Especially, the computational effort for solving the stability conditions online is reduced, allowing real-time control law gains to be obtained and combined with historical batches of control inputs, reducing the learning cycles of the system, and realizing stable tracking of the setpoints within shorter operating batches. Moreover, the robust positive invariant set and the set of terminal constraints are able to constrain the state of the system within a safe range for all possible uncertainties, bounded disturbances, and faults. This makes the proposed methods based on them more robust and fault tolerant. Finally, a nonlinear batch reactor is used as an example to demonstrate the effectiveness and feasibility of the developed method.

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.

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 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.

Synthesis of Gain-Scheduled Controllers with a Switching Mechanism