Peter J. Kennedy, PE

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Presents a SunCam continuing education series on the topic of:

Concepts in Mechanical, Industrial and Electrical Engineering

Contact author at:
peterkennedy@optonline.net
Wall, New Jersey

Mr. Peter Kennedy, Systems Engineer, Northrop Grumman Electronic Systems, received his BS Mechanical Engineering from Lafayette College in 1971, MS in Industrial Engineering from Lehigh University in 1973, and MS in Electrical Engineering from Fairleigh Dickinson University in 1978.

Mr. Kennedy has over forty five years of experience in precision pointing and stabilization control systems [1], infrared countermeasure (IRCM) system design and analysis, and IR missile tracking and guidance techniques. Specific experience includes design and development of directional IRCM and electro-optical CM systems for both airborne platforms and ground vehicles; test and evaluation of IR missile seeker and Anti-Tank Guided Missile tracker susceptibilities; and design and evaluation of stabilized pointing systems.

He has worked for Northrop Grumman Electronic Systems, De Chiaro Schweickert Consultants Inc. (DSCI), IT&T Exelis Electronics Systems, David H Pollock Consultants (DHPC), Scitec Inc., AT&T Bell Laboratories, and the US Army Electronic Warfare Laboratory. He is a licensed Professional Engineer in New Jersey since 1976.

1. Kennedy, P, Kennedy, R. , Stabilizing the Line of Sight, LuLu Publishing Company, ISBN-13: 978-0692273562, 2014, www.lineofsightstabilization.com

Course Title Rating Hours Price

182-Feedback Control System Fundamentals

(4.2) 12 Reviews

4 $90.00

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

This course discusses many fundamental concepts associated with classical feedback control theory. Feedback control measures the state of a physical system or device with a sensing system. The measured state is fed-back and compared to a desired state and the error used by a controller to reduce the difference between the actual and desired states. An example of a feedback control system is the central heating and air conditioning system for a home, or building. A thermostat or temperature sensor is the feedback sensor that measures the room temperature and compares it to the desired temperature or set point, calculating a difference or error. If the temperature is less than the set point, the error is used by the controller to force more heat into the room. When the set point is reached, the error is zero or below an error threshold and the controller will stop heating the room. Another example is the speed control in most of today's automobiles. The speed of the vehicle is measured and compared to a desired speed. Based on the difference between actual speed and the set point, acceleration or braking is applied to the automobile drive to null the error and maintain the desired speed.

Classical control deals directly with the differential equations that describe the dynamics of a plant or process. These equations are transformed into frequency dependent transfer functions. The transfer function is the ratio of two frequency dependent polynomials whose roots describe the response of the plant in a frequency domain. The controller or compensator shapes the closed feedback loop response, given the plant response, to achieve the control performance objectives. Classical feedback control design and analysis tends to require a good foundation in mathematics, however the purpose of this course is not to dwell on the math, although examples are provided, but to provide the basic design and analysis concepts.

The topics covered begin with a description of the basic block diagram in section 2. The relationships between time and frequency domain representations of the block diagram elements are discussed in section 3 followed by the key feedback relationships derived from the block diagram algebra in section 4. Control loop stability and methods to determine stability margins are described in section 5 followed by a discussion of specifying control loop performance in section 6. A couple of control loop design methods are provided in section 7. The basic theory is then applied to two examples; a home heating system in section 8 and motion control applications in section 9. Converting to a digital sample data controller is discussed in section 10; as related to the motion control example in section 9.

291-Proportional, Integral, and Derivative Controller Design Part 1

(4.3) 6 Reviews

4 $90.00

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

In this course, the design and application of Proportional plus Integral plus Derivative (PID) controller's is discussed. Some familiarity with feedback control may help in providing a better understanding of the course material. PID control is a technique used extensively in feedback control systems. Its origins date back to the 19th century, being used for governor speed control, and since then in numerous applications with a wide variety of actuators and sensors. The controller is simple structure; being the sum of three terms as the name implies. The PID structure provides for a fairly wide range of tuning adjustment in a feedback control loop, especially for relatively simple processes. A PID uses the error, it's integral and derivative to derive a control signal driving the error to a null state. The controller can be structured in many configurations; P-only, PI, PD, PID, plus others to be discussed. PID control is central to most process control systems; but can also be found in numerous applications other than process control ranging from positioning control loops to pointing, tracking and platform stabilization control loops. The PID can also be integrated with higher level control strategies such as model predictive control, adaptive controllers and fuzzy logic control described in Part 2 of the course.

Starting with an introduction in section 1.0, topics covered are a description of the basic feedback control loop block diagram in section 2 and how the PID relates to the control loop. The relationships between time and frequency domain representations of the block diagram elements are discussed in section 3 followed by the key feedback relationships derived from the block diagram algebra in section 4. The PID control algorithm is described in section 5 which includes the frequency domain characterization of the PID (5.1), the effect of each PID term has on response (5.2) and finally different forms of the PID used in actual applications. In section 6 a discussion of specifying control loop performance is presented. PID control loop design methods are provided in section 7. The basic theory is applied to an example; a home heating system, in section 8.

292-Proportional, Integral, and Derivative Controller Design Part 2

(4.5) 6 Reviews

4 $90.00

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

As discussed in Part 1, PID controllers are used in many control applications; possibly the most common form of feedback control compensation. The versatility of the PID may reside is a fairly simple control structure, easy to implement in software or hardware, offering loop gain adjustment, an integrator to reduce or null servo error, and the phase lead of a derivative improve loop stability or act as a predictive element. This PID structure provides for a fairly wide range of tuning adjustment in a feedback control loop, especially for relatively simple processes. The controller can be configured in many configurations; P-only, PI, PD, PID, plus others are discussed. This part of the course focusses on the digital implementation of the PID controller and its implementation with higher level control strategies; adaptive controllers and fuzzy logic control.

Starting with an introduction in section 1.0, topics covered are a summary of the basic feedback control loop block diagram relationships in section 2. The PID control algorithm, as presented in Part 1, is summarized in section 3 followed by the digital implementation of the PID within the constraints of a sampled control system. The building temperature control example, used in Part 1, is analyzed again in section 4.0 but now using a digital PI controller. Section 5.0 provides another example for a motion control application using a digital PD controller. Finally section 6.0 describes implementation of the PID within a model reference adaptive control (MRAC) architecture and also configured with a fuzzy logic controller (FLC).

358-Sightline Control Basics for Geo-Pointing and Locating - Part 1

(5) 1 Review

4 $90.00

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Course Objectives: Describe theory and system level architectures

Course Description:

This is a three part course discussing geo-pointing and locating. With the proliferation of unmanned aerial vehicles (UAV) coupled with advancements in camera and inertial measurement sensor technology; this is fast becoming a technology used in many applications requiring geo-referenced imagery. Historical applications for military and civilian surveillance and navigation are fairly well documented and continue to grow. But the technology is now the cornerstone for numerous situational awareness applications such as environmental, fire protection, road/bridge surveillance, maintenance and protection. It may even be part of your pizza delivery service, which for those of you with bevy of children could be a sobering thought.

The course is organized as three topics; the first addresses generic sightline control (SLC). As geo-locating requires geo-pointing and pointing requires maintaining a stable line of sight (LOS) to a targeted object or area, then an understanding of SLC is important. The second topic focusses on the geo-pointing problem given a stable sightline to the object to be geo-located. Finally the basics of geo-location, using direct and image geo-registration, are described. Many SLC sections in Part 1.0 require some background in control theory as well as mathematical operations with vectors and matrices. For those interested in geo-pointing and location at a system level; these sections will be somewhat tedious. However, it is not essential to follow all the math but it is important to understand the need for it and how it plays into an overall solution. Similarly there is a lot of detailed discussion on pointing techniques, for example one section is dedicated to using mirrors for pointing. The details may not be critical, but it is important to understand that with all the benefits obtained using a pointing mirror, they also have characteristics that must be understood and accounted for in the design or one will be in for a rather unpleasant surprise. Pointing design should follow a top down design procedure; beginning with requirements through HW and SW design and implementation. However given cost and schedule constraints, one is often forced into an off the shelf design with compromised performance. Understanding the design requirements, however, should not be compromised so related performance can be quantified and improved in future designs. The purpose of the course is to lay a framework for understanding this design process. There should be sufficient math detail for those interested at the equation level but hopefully adequate course structure for those not so inclined to still follow the overall design process. Test questions are at a system level. The course has a two part structure; Part 1.0 covers SLC basics and geo-pointing, Part 2.0 provides a brief review of Part 1.0 followed by a focus on geo-locating, and finally Part 3.0 describes camera sensor characteristics and requirements for geo-locating.

359-Sightline Control Basics for Geo-Pointing and Locating - Part 2

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4 $90.00

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Course Objectives: Describe theory and system level architectures

Course Description:

This part of the course will apply the sightline control (SLC) fundamentals described in Part 1.0 to the geo-pointing and location problem. Initially Section 1.0 Part 1.0 is reviewed, particularly pointing performance requirements which directly impact geo-pointing and location performance. Geo-pointing is then described, effectively delving deeper into the material begun in section 8.0 in Part 1.0 of the course. Geo-pointing errors are related back to the SLC pointing problem with its limitations serving as a foundation for pursuing different geo-location approaches. Geo-location techniques are generally categorized as either direct or image geo-registration derived. The errors associated with the pointing solution for direct geo-pointing provide a basis for examining geo-location techniques that use image geo-registration to improve performance.

Image geo-registration is also used in many applications that require geo-referenced sensed imagery as well as location; discussed in Section 4.0 of Part 2.0 of the course. As this is effectively a technology in itself, only the salient aspects of the process are reviewed but should provide a source for further study and investigation, if of interest. Regardless of the geo-location technique used, however, geo-pointing will generally be part of the solution. If not the solution, it will provide coarse location estimates for the image geo-registration process. A substantial amount of image spatial processing is required to obtain an accurate solution to an image geo-registered location and the processing is described at a functional system level to capture the overall design process. The benefits that image geo-registration provides beyond that of location are significant since it can used to obtain situational awareness as defined for many applications. Military and civilian surveillance is an obvious application, but even the use of image information within the transportation infrastructure for highway and bridge maintenance management, damage and structural deterioration assessment, traffic pattern analysis and control, etc. is a growing application. The goal of this part of the course is to provide a system level functional description of the geo-location process and how performance relates back to that of geo-pointing with SLC. The camera sensor requirements for image geo-registration location are discussed in Part 3.0, the last part of the course.

360-Sightline Control Basics for Geo-Pointing and Locating - Part 3

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2 $45.00

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Course Objectives: Provide design algorithm approach for geo-pointing and locating providing a basis more detailed design

Course Description:

This final part of the course discusses sensor characteristics and requirements for geo-location image geo-registration, as described in Part 2.0 section 4.0 of the course. The focus is primarily on cameras and sensors whose response is in the visible and near infrared wavelength spectrum. Sensor characteristics that drive sensitivity and noise as well as camera sensor and optics parameters that determine scene coverage and resolution are all reviewed in this section. In total the three part course should provide an overview of geo-pointing and locating sufficient to plan for such an application and detailed design areas that require further investigation and study to perform an actual design.

409-Understanding Sensors Part 1 - Sensor Technology

(4.8) 17 Reviews

4 $90.00

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

Sensors provide status information on our environment, homes, cars, and equipment we use. They are a part of nearly all walks of life and essential elements of control and safety systems. Part 1 of the course discusses sensor technology while Part 2 describes sensor networks and fusion of sensor network data. The sensor is a device that detects and/or measures the state of a physical quantity such as temperature, pressure, force, flow, or level. Measurements are converted to an observation media such as an electrical signal or mechanical, hydraulic, or pneumatic motion providing knowledge of the physical quantity’s state. They may also interface directly to an actuator. The sensor measurement function is performed by several components that constitute a sensing system. This system, termed a sensor node when integrated into a network, is comprised of a sensing element, signal conditioning and possibly processing components, power supply and some form of output; a simple display, meter, or now with the internet many sensors and associated processing interconnect through a wireless communication network. Sensor technology, networking, and fusion is of growing importance in most engineering and scientific applications and this two-part course discusses these topics.

410-Understanding Sensors Part 2 - Sensor Networks

(5) 7 Reviews

4 $90.00

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Course Objectives: This continuing education course is written specifically for professional engineers with the objective of relating to and enhancing the practice of engineering.

Course Description:

Part 2 begins with a description of two additional sensors that will continue to play an important role in the evolving sensor networking technology; micro electro-mechanical systems (MEMS) and fiber optic sensors. Then sensor networking architecture and the transmission of measured data to a sensor fusion algorithm is examined. Finally, sensor fusion technology is described. Finally, sensor fusion technology is described. Sensor fusion enhances the knowledge base of the quantities of interest as well as the interaction between them. This part of the course will cover all aspects of the networked sensing system; the sensor node, the communication network, network topologies and wireless sensor networks (WSN), communication network layered protocols, and finally fusion algorithms and processing techniques. Spurred by innovations in the smart phone, the Internet, MEMS, the IoT and the Cloud, devices are becoming smart and capable of user control, monitoring, and communication from remote locations. Network connectivity, person to person, machine to machine, device to device and combinations thereof is expanding at a fast rate making an understanding of sensor networks and fusion of growing importance.