Predictive functional control for the temperature control of a chemical batch reactor

A predictive functional control (PFC) technique is applied to the temperature control of a pilot-plant batch reactor equipped with a mono-fluid heating/cooling system. A cascade control structure has been implemented according to the process sub-units reactor and heating/cooling system. Hereby differences in the sub-units dynamics are taken into consideration. PFC technique is described and its main differences with a standard model predictive control (MPC) technique are discussed. To evaluate its robustness, PFC has been applied to the temperature control of an exothermic chemical reaction. Experimental results show that PFC enables a precise tracking of the set-point temperature and that the PFC performances are mainly determined by its internal dynamic process model. Finally, results show the performance of the cascade control structure to handle different dynamics of the heating/cooling system

Nonlinear predictive restricted structure control

This thesis defines new developments in predictive restricted structure control for industrial applications. It begins by describing the augmented system for both state-space and polynomial model descriptions. These descriptions can contain the plant model, the disturbance model, and any additional essential model subsystems. It then describes the predictive restricted structure control solution for both linear and nonlinear systems in state-space form. The solution utilizes the recent development in nonlinear predictive generalized minimum variance by adding a general operator subsystem that defines nonlinear system along with the linear or the linear parameter varying output subsystem. The next contribution is the polynomial predictive restricted structure control algorithm for polynomial linear parameter varying model that may result from nonlinear equations or experimental data-driven model identification. This algorithm utilizes the generalised predictive control method to approximate and control nonlinear systems in the linear parameter varying system inputoutput transfer operator matrices. The solution is simple in unconstrained and constrained optimization solutions and required a small computing capacity. Four examples have been chosen to test the algorithms for different nonlinear characteristics. In the first three examples, state-space versions of the algorithm for the linear, the quasi-linear parameter varying and the state-dependent were employed to control the quadruple tank process, electronic throttle body, and the continuous stirred tank reactors. In the last example, the polynomial linear parameter varying restricted structure controller is used to control automotive variable camshaft timing system.This thesis defines new developments in predictive restricted structure control for industrial applications. It begins by describing the augmented system for both state-space and polynomial model descriptions. These descriptions can contain the plant model, the disturbance model, and any additional essential model subsystems. It then describes the predictive restricted structure control solution for both linear and nonlinear systems in state-space form. The solution utilizes the recent development in nonlinear predictive generalized minimum variance by adding a general operator subsystem that defines nonlinear system along with the linear or the linear parameter varying output subsystem. The next contribution is the polynomial predictive restricted structure control algorithm for polynomial linear parameter varying model that may result from nonlinear equations or experimental data-driven model identification. This algorithm utilizes the generalised predictive control method to approximate and control nonlinear systems in the linear parameter varying system inputoutput transfer operator matrices. The solution is simple in unconstrained and constrained optimization solutions and required a small computing capacity. Four examples have been chosen to test the algorithms for different nonlinear characteristics. In the first three examples, state-space versions of the algorithm for the linear, the quasi-linear parameter varying and the state-dependent were employed to control the quadruple tank process, electronic throttle body, and the continuous stirred tank reactors. In the last example, the polynomial linear parameter varying restricted structure controller is used to control automotive variable camshaft timing system

Circle grid fractal plate as a turbulent generator for premixed flame: an overview

This review paper focuses to ascertain a new approach in turbulence generation on the structure of premixed flames and external combustion using a fractal grid pattern. This review paper discusses the relationship between fractal pattern and turbulence flow. Many researchers have explored the fractal pattern as a new concept of turbulence generators, but researchers rarely study fractal turbulence generators on the structure premixed flame. The turbulent flow field characteristics have been studied tand investigated in a premixed combustion application. In terms of turbulence intensity, most researchers used fractal grid that can be tailored so that they can design the characteristic needed in premixed flame. This approach makes it extremely difficult to determine the exact turbulent burning velocity on the velocity fluctuation of the flow. The decision to carry out additional research on the effect circle grid fractal plate as a turbulent generator for premixed flame should depends on the blockage ratio and fractal pattern of the grid. 1

Robust nonlinear generalised predictive control for a class of uncertain nonlinear systems via an integral sliding mode approach

In this paper, a robust nonlinear generalised predictive control (GPC) method is proposed by combining an integral sliding mode approach. The composite controller can guarantee zero steady-state error for a class of uncertain nonlinear systems in the presence of both matched and unmatched disturbances. Indeed, it is well known that the traditional GPC based on Taylor series expansion cannot completely reject unknown disturbance and achieve offset-free tracking performance. To deal with this problem, the existing approaches are enhanced by avoiding the use of the disturbance observer and modifying the gain function of the nonlinear integral sliding surface. This modified strategy appears to be more capable of achieving both the disturbance rejection and the nominal prescribed specifications for matched disturbance. Simulation results demonstrate the effectiveness of the proposed approach

Advanced Generalized Predictive Control and Its Application to Tiltrotor Aircraft for Stability Augmentation and Vibration Reduction

The goals of this research were to restore generalized predictive control (GPC) capability at NASA and within the community, to better understand GPC and its performance relative to other options, and to improve upon the capability of GPC. Unique to this research is the comparison of GPC with other control options including PID controllers, optimal control theory, and other versions of the similar AutoRegressive moving average model with eXogenous inputs (ARX) models. Similar to GPC, ARX models use an experimentally acquired system identification to characterize the input/output relationship between controls and response measurements. Because this relationship is determined from acquired data, minimal knowledge of the system behavior is required to employ ARX or GPC controllers. As a result of these comparisons, it was observed that GPC is typically the best performing control option and typically has better gain and phase margins when properly employed. Also unique to this dissertation is the use of orthogonal multisine excitation as the command inputs for GPC application rather than the typical distinguishable random noise. Finally, the concept of Advanced GPC (AGPC) is introduced as a part of this dissertation work. AGPC is a self-adapting algorithm that improves traditional GPC when conditions change from those used to derive the system identification. AGPC is also better performing than traditional GPC in some cases even when the conditions do not change from those used to acquire the system identification. Application of AGPC requires the monitoring of performance figures of merit, and the application of control dither when the metrics indicate that the controls are not distinguishable enough or the response of the system is inadequate to properly characterize the input/output relationship. Finally, for experimental application of GPC and AGPC, techniques were introduced to increase model safety and include features such as a magnitude ramp rate when closing the control loop, master gain values to reduce control or dither authority, continual computation of figures of merit, the ability to gradually change from one control algorithm to another, and visualization of control commands prior to closing the control loop and/or switching from one control algorithm to another

A Case Study of Fractional-Order Control

This thesis concerns fractional-order (non-integer) methods for control system design. Although fractional-order calculus has a long history in mathematics and engineering, the uptake of relevant fractional-order concepts in control systems research has been relatively slow, and interest in the topic remains comparatively low—albeit with some important exceptions, as highlighted by the literature review of this thesis. The first part of the thesis considers fractional-order methods for modelling and control in quite broad terms, before later focusing on one particular approach from the control systems literature, namely Fractional-order Generalised Predictive Control (FGPC). The FGPC approach is of particular interest here because of its relationship with the well-known, conventional control algorithm, namely Generalised Predictive Control (GPC). Both algorithms have a relatively straightforward implementation form, making them attractive to practitioners. Hence, one contribution of the thesis is to use worked examples in MATLAB as an introduction to GPC and FGPC design methods, in part for tutorial reasons. More significantly, the thesis demonstrates how fractional-order methods are utilised to increase control design flexibility. In this regard, the thesis investigates both conventional GPC and FGPC methods using various simulation examples. The robustness of control systems is investigated via Monte Carlo simulation, with consideration of model mismatch and unmeasured disturbances. These results Abstract II are utilised to develop recommendations for how to optimise the extra design coefficients introduced in the fractional-order case. The comparative study is extended to a laboratory example, namely the control of airflow in a 1 m by 2 m by 2 m forced ventilation environmental test chamber. To facilitate further uptake of FGPC methods in the future, the algorithms developed are prepared as a MATLAB toolbox, i.e. a collection of functions that calculate and implement the FGPC approach and subsequently measure the performance of the controller

Restricted structure predictive control for linear and nonlinear systems

An optimal predictive control algorithm is introduced for the control of linear and nonlinear discrete-time multivariable systems. The controller is specified in a 'restricted structure' form involving a set of given linear transfer-functions and a set of gains that minimise a Generalised Predictive Control (GPC) cost-index. The set of functions can be chosen as proportional, integral and derivative terms, however, a wide range of controller structures is possible. This is referred to as Restricted-Structure GPC control. The multi-step predictive control cost-function is novel, since it includes weightings on the ‘low-order’ controller gains and the rate of change of gains. This considerably improves the numerical computations ensuring critical inverse computations cannot lead to a singular matrix. It also provides the option of adding soft or hard constraints on the controller gains which provides additional flexibility for control design. The ability to include a plant model that can include a general nonlinear operator is also new for restricted structure control solutions. The low-order controller provides a potential improvement in robustness, since it is often less sensitive to plant uncertainties. The simple controller structure also enables relatively unskilled staff to retune the system using familiar tuning terms, and provides a potentially simpler QP problem for the constrained case