Design Considerations
2.5 Applications of Opto-Mechatronic Systems
2.5.3 Optical Implementation of a SISO Rule-Based Controller
Fuzzy logic techniques are used to control electro-mechanical components in a variety of consumer products and industrial plants. However, these controllers are often slow because of the extensive signal-processing and algorithmic computations required to arrive at a satisfactory conclusion from numerous rules. To increase the processing speed of rule-based controllers, several researchers have proposed an optical solution [Gur et al., 1998; Itoh et al., 1997; Zalevsky et al., 2000; Zhang and Karim, 1999]. Itoh et al. [1997] describe an opto-electronic controller that performs fuzzy logic operations in real time by FIGURE 2.23 An illustration of the response curves generated by the opto-coupled sensor system proposed by Wakami et al. [1996].
Muddy dirt Time Voltage
proportional to light transmission
Oily dirt
(a)
Time Voltage
proportional to light transmission
(b)
Light dirt
Heavy dirt tmud toil
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utilizing a beam-scanning fuzzy inference architecture. The proposed architecture, Figure 2.25, uses a product-sum-gravity method with Gaussian membership functions instead of the conventional min-max gravity method with triangular membership functions [Kosko, 1997].
The beam-scanning laser diode (BSLD) of the single-input, single-output (SISO) system receives a current signal as an input, i1, from a sensor monitoring the response of a physical process and a reference FIGURE 2.24 The knowledge-based controller used to determine the desired wash time (tw) from the sensor system output as described by Wakami et al. [1996]. The controller inputs, ( τs, tw), are fuzzy variables and the output (tw) is a real number.
FIGURE 2.25 The opto-electronic implementation of a SISO fuzzy logic controller as proposed by Itoh et al. [1997].
In the above illustration the fuzzy membership functions are PL, positive large; PM, positive medium; PS, positive small; ZE, zero; NS, negative small; NM, negative medium; and NL, negative large.
Desired Washing Time
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input given by the current signal, i2. The light beam emitted by the BSLD diverges by the angle θ [Itoh et al., 1997]:
θ=β(i2− i1) (2.11)
where β is the fixed system gain. The angle θ represents the basic premise for each rule in the fuzzy inference engine.
The angular shift in the beam direction can also be achieved using an acousto-optic deflector (AOD) or a spatial light modulator (SLM) [Gur et al., 1998]. For the first alternative, the laser light beam must be reshaped and Fourier-transformed prior to entering the acousto-optic cell located in the Fourier plane (Figure 2.26). The transformed light beam is then multiplied by an acoustic wave that is orthogonal to the beam-propagation direction. The frequency of the acoustic wave is proportional to the input (i2 – i1), resulting in a deflected beam angle of θ. In contrast, the spatial light modulator can be placed at either the laser output or at the Fourier plane of a 4f setup and used to directly shift the angle of the beam. In many control applications a low-resolution SLM might be sufficient; however, the SLM’s slow response time is often the limiting factor in control speed.
Once the beam is deflected based on the input difference it strikes an array of photodetectors (PDs).
Each PD represents a membership function for the angle θ. For example, the central PD represents angles close to zero, and the edge PDs represent large angles, either positive large (PL) or negative large (NL).
Each PD produces a current whose value is proportional to the match between θ and the PD location.
Each constituent PD in the array drives a separate beam-scanning laser diode. When activated each BLSD emits a Gaussian beam with an intensity profile proportional to the driving current generated by the activated PD. The resulting beams from neighboring laser diodes project onto a position-sensitive detector (PSD). The output current of the PSD is proportional to the center of gravity (CoG) of the incident beams. Several Gaussian beams that originated from different inputs can reach the single-output PSD, and the detector will determine the total CoG for all inputs [Gur et al., 1998].
The main advantage of the optical inference engine proposed by Itoh et al. [1997] is its simplicity and modularity for extending the basic structure for multiple inputs. For example, several SISO controllers can be placed on top of each other to simultaneously control several systems. Furthermore, the simple optical and electronic components used by this computing architecture should enable several controllers to be developed on an integrated optic circuit for product miniaturization.
FIGURE 2.26 An acousto-optic deflector used for the light beam displacement [Gur et al., 1998] in an opto-electronic implementation of a fuzzy logic controller.
AOD
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The basic controller design has several limitations. First, the beam-scanning laser diode and other signal deflection methods generate beams with Gaussian profiles. Reshaping the beam [Leger, 1997] to represent other types of membership functions is not a trivial task. Solutions to this problem include the use of an amplitude mask, reshaping the beam in the Fourier domain with a phase-only filter, or placing an amplitude-coded mask in the Fourier plane. A second lim-tation to this optical implementation of a fuzzy logic controller is that this optic circuit requires optical–electronic–optical conversions. As a result of these signal conversions, the original information can easily become distorted and lead to control error.
2.6 Conclusions
This chapter described the process of opto-mechatronic design. Opto-mechatronics stresses technology integration for enhanced system performance. In essence, it is not a specific technology but rather a design philosophy that promotes the creation of high-quality “smart” products and processes. Opto-mechatronic systems will often exhibit a number of important characteristics such as the functional interaction between optical, electronic, and mechanical components; spatial integration of subsystems into a single physical unit; utilization of multifunctional devices; and exploitation of embedded control.
This chapter summarized some of the unique and important features of optical and electronic technol-ogies that contribute to the performance of an opto-mechatronic system. Specifically, optical sensors and actuators were examined because these devices provide a mechanism for robust, low-cost solutions that enable high-precision and rapid signal-processing operations. Several examples were provided to illustrate how these simple devices could perform complex functions.
Defining Terms
concurrent engineering: The simultaneous evolution of the product and the manufacturing process required to produce it.
design: The creative process used to solve open-ended or ill-defined problems with numerous satisfac-tory solutions.
Design for Excellence (DFX): A knowledge-based approach that attempts to design products or processes by maximizing all desirable characteristics such as high quality, reliability, serviceability, safety, user friendliness, and short time-to-market while, at the same time, minimizing lifetime costs.
embedded controller: A microprocessor with combined memory and various input/output (I/O) fea-tures on a single integrated chip. These devices do not have an operator I/O interface.
integrated optics: Analogous to integrated electronic circuits but where the movement of photons replaces electrons.
ray sketching: A method for rapidly drawing light rays to determine approximate distances and dimen-sions of a proposed optical system.
ray tracing: A method used to evaluate the performance of an optical system by calculating the paths of one or more light rays through the constituent components.
system: A term used to describe a product or process that is viewed as a box with inputs and outputs;
also, a mathematical function describing the relationship between inputs and outputs.
transducer: A device that transforms energy from one form into another; it does not matter whether the energy belongs to different domains or the same domains. Transducers may be sensors or actuators.
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Gur, E., Mendlovic, D., and Zalevsky, Z., Optical implementation of fuzzy logic controllers: Part I, Applied Optics, 37(29), 6937–6945, 1998.
Itoh, H., Yamada, T., Mukai, S., Watanabe, M., and Brandl, D., Optoelectronic implementation of real-time control of an inverted pendulum by fuzzy-logic-control units based on a light-emitting-diode array and a position-sensing device, Appl. Opt., 36(4), 808–812, 1997.
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Zhang, S. and Karim, M. A., Optical triangular-partition fuzzy systems with on-memory-matrix fuzzy associative memory, Appl. Opt., 24(7), 484–486, 1999.
For Further Information
Information on mechatronic system design and opto-mechatronics is included in several professional society journals and conference proceedings. A variety of the articles describing interesting applications are found in Mechatronics, Journal of Robotics and Mechatronics, IEEE Transactions on Mechatronics, Journal of Micromechatronics, Journal of the Optical Society of America, and Optical Engineering. The proceedings of the Opto-Mechatronic Systems Conference are published annually by SPIE, the Inter-national Society for Optical Engineering. These proceedings document the latest developments in the field of optical-based products and processes each year.
A number of introductory texts and reference books on mechatronics systems have been published in recent years. Two reference books that provide a unique perspective are:
HMT Limited, Mechatronics and Machine Tools, McGraw-Hill, New York, 1999
Popovic, D. and Vlacic, L., Mechatronics in Engineering Design and Product Development, Marcel Dekker, New York, 1999
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