MICROWAVE CONTROLLER

Microwave Controller

Microwave Controller

Introduction

PIC Microcontrollers are quickly replacing computers when it comes to programming robotic devices. These microcontrollers are small and can be programmed to carry out a number of tasks and are ideal for school and industrial projects. A simple program is written using a computer, it is then downloaded to a microcontroller which in turn can control a robotic device.

In microwave combination ovens, heating is induced by the simultaneous action of microwaves and convection. Microwaves penetrate the product and promote internal heating, while convection takes care of heating near the surface. This represents an obvious advantage over traditional convection heating, where the required energy flux needs an “outside-to-inside” driving force which usually leads to extremely high surface temperatures that can induce product quality deterioration. In this way, microwave combination ovens offer more flexibility to satisfy safety and quality trade-offs.

Different objectives such as final temperature or quality uniformity can be considered in addition to the required satisfactory microbiological levels (Ryckaert, Claes & Impe, 1999). Once they are defined, optimal policies can be developed, in a systematic way, through the use of efficient modelling and optimization tools. This approach has been successfully employed to develop optimal thermal sterilization policies for the canning industry ( Banga et al., 1991; Silva et al., 1993 and Durance, 1997). However, in order to implement the operation, additional conditions must be considered. These include uncertainties and unexpected disturbances, always present in any real life situation, which demand efficient on-line controllers to enforce optimal operation ( Alonso, Banga & Perez-Martin, 1998).

As we can see therobustness are concepts linked to an acceptable description of the physical principles governing the process. Detailed modelling, based on first principles is available for convection ovens (see Sato, Matsumura & Shibukawa, 1987) but unsuitable for standard control applications due to its mathematical complexity. Simplified dynamic descriptions were incorporated to design temperature and quality controllers for ovens ( McFarlane, 1994 and Ryckaert et al., 1999). However, as pointed out by Fahloul, Trystram, Duquenoy and Barbotteao (1994), it is difficult to set up reliable models to correlate operating variables with physical conditions in the product.

In order to overcome the inherent complexity, we adopt an approach that relies on simple dynamic models, continuously updated with input-output information to capture the effect of the intrinsic nonlinearity. Locally, the internal model control framework (Morari & Zafiriou, 1989) is employed to design optimal tracking controllers from causal inversion of linear plants. Variability in plant parameters, due to the inherent nonlinearity of the plant, is captured by recursive parameter identification schemes. This approach was successfully applied in batch thermal processing to enforce accurate tracking of constant and variable sterilization temperature profiles ( Alonso et al., 1998).

First, the concept of optimum quality, in the context of microwave thermal processes will be discussed. A simplified dynamic description of the process will be justified and shown to be suitable as part of the controller. A linear version of this model will be inverted to achieve optimal ...