Diane Kennedy

Stabilizing control of a high-order generator model by adaptive feedback linearization

We present an adaptive feedback linearizing control scheme for excitation control and power system stabilization. The power system is a synchronous generator which is first modeled as an input-output nonlinear discrete-time system approximated by two neural networks. Then, the controller is synthesized to adaptively compute an appropriate feedback linearizing control law at each sampling instant using estimates provided by the neural system model. This formulation simplifies the problem to that of designing a linear pole-placement controller which is itself not a neural network but is adaptive in the sense that the neural estimator adapts itself online. Additionally, the requirement for exact knowledge of the system dynamics, full state measurement, as well as other difficulties associated with feedback linearizing control for power systems are avoided in this approach. Simulations demonstrate its application to a high-order single-machine system under various conditions.

Toward a systems- and control-oriented agent framework

This paper develops a systems- and control-oriented intelligent agent framework called the hybrid intelligent control agent (HICA), as well as its composition into specific kinds of multiagent systems. HICA is essentially developed around a hybrid control system core so that knowledge-based planning and coordination can be integrated with verified hybrid control primitives to achieve the coordinated control of multiple multimode dynamical systems. The scheme is applied to the control of teams of unmanned air and ground vehicles engaged in a pursuit-evasion war game. Results are demonstrated in simulation.

Integrating the Internet, multimedia components, and hands-on experimentation into problem-based control education

At the brink of a new millennium, a shift in pedagogy toward a learner-centered approach is emerging where critical thinking skills are fostered through problem based learning opportunities and innovative approaches to instruction and student work assessment. New media technologies are being effectively used to help achieve such goals in engineering education. The paper describes technology aided teaching of undergraduate engineering courses in control theory. Early results show that students are better motivated and their achievement improves compared to when more traditional methods of instruction are used in delivering the course content.

A practical servomotor project: combining the Web with simulation tools to solidify concepts in undergraduate control education

Current technology enables the lecturer to use computer tools to enhance the conceptualisation of lecture material. This can be especially useful in an engineering curriculum, as the course material can be rendered less abstract through visual illustration of difficult mathematical concepts. In this paper, we present an example of a multimedia enhanced course in linear control theory, taught by authors at Ryerson Polytechnic University (Toronto, Canada). In our implementation of the course, we combine software simulations of practical systems to illustrate control theory concepts with extensive online course notes to assist with comprehension. Followup analysis shows that the use of the WWW and computer tools to enhance the learning process leads to increased enthusiasm, comprehension, and information retention. A review of the process required to create the technology enabled learning environment shows that initially there is a great deal of work involved. However, we conclude that the effort is justified by allowing a positive qualitative change in the way we educate engineering students.

A class of intelligent agents for coordinated control of outdoor terrain mapping UGVs

This article develops a systems- and control-oriented intelligent agent framework called the hybrid intelligent control agent (HICA) and describes its composition into multiagent systems. It is essentially developed around a hybrid control system core so that knowledge-based planning and coordination can be integrated with verified hybrid control primitives to achieve the coordinated control of multiple multi-mode dynamical systems. The scheme is applied to the control of a team of unmanned ground vehicles (UGVs) engaged in an outdoor terrain mapping task. Results are demonstrated experimentally.

A study of supervisory constraints in a class of coordinated multiagent systems

We present a class of supervisory multiagent sys- tems in which each agent is based on an enhanced form of the intelligent agent paradigm called the Hybrid Intelligent Control Agent (HICA). A bound on supervisory input event rates neces- sary to preserve multiagent stability is obtained and studied in a pursuit-evasion simulation involving multiple autonomous un- manned vehicles.

Simultaneous stabilization with near-optimal model reference tracking

In this paper we consider the use of linear time-varying controllers for simultaneous stabilization and performance. We prove that for every finite set of plants, we can design a linear time-varying controller which provides not only closed-loop stability, but also near-optimal model reference tracking.

Multi-vehicle pursuit-evasion: an agent-based framework

We describe a pursuit-evasion scheme involving multiple coordinated vehicle teams in which each vehicle is modeled as a class of hybrid intelligent agents. This is the hybrid intelligent control agent (HICA), a control-oriented enhancement to the intelligent agent paradigm which conceptually wraps an agent around a hybrid control system core. The problem is formulated as two supervisory teams of HICA to synthesize relevant control/coordination logic. Simulations demonstrate the results of such an approach.

Simultaneous stabilization with near optimal model reference tracking

We consider the use of linear time-varying controllers for simultaneous stabilization and performance. We prove that for every finite set of plants, we can design a linear time-varying controller which provides not only closed loop stability, but also near optimal model reference tracking.