Overview

This topic will cover the modelling and control of dynamic systems in noisy environments utilising state-space representations. Moving beyond single input single output (SISO) controllers, such as proportional-integral derivative (PID) and lead-lag, it will include both the theoretical and practical implementations of multi-input multi-output (MIMO) control systems with nested single-input … For more content click the Read More button below. The topic will also cover how autonomous systems (in the virtual or in the real world) use their noisy measurements to learn the best course of actions (policy) to take. This topic will teach how to get robots to incorporate uncertainty into estimating and learning from a dynamic and changing world. Robust estimation techniques are employed to produce estimated values that tend to be closer to the true values of the system compared to individual measurements and their associated calculated values and are an essential part of the development of advanced navigation systems. Upon completion students will be able to design an optimal controller and estimator for an arbitrarily complex linear time invariant plant operating in a noisy environment, as well as know how to linearly approximate non-linear systems around operating points.

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Tuition pattern

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Aims

This topic aims to provide students with a comprehensive understanding of the principles of advanced sensory and control systems theory and technology as applied to the design, analysis and control of modern autonomous systems. Students will be introduced to the principles, theory and practice of making decisions and estimates based on noisy or unreliable data. Specifically, making estimations and actions based on the interpretation of noisy sensor data from any source. The performance of the sensors, including precision, accuracy, repeatability, sensitivity, linearity and dynamic performance, will be analysed. Calibration and estimation techniques are also examined along with modern control approaches.

Assessments

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Current students should refer to FLO for detailed assessment information, including due dates. Assessment information is accurate at the time of publishing.

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Requisites information

Pre-requisites:

Assumed knowledge

Linear Algebra, Calculus, Laplace Transform, Bode Plots, z-Transform, Vector-Matrix Analysis, Signals and Systems, Conventional Control Systems such as can be obtained in ENGR2711 Engineering Mathematics and ENGR2722 Analysis of Engineering Systems OR MATH2711 Several Variable Calculus and MATH2702 Linear Algebra and Differential Equations.