Controller Design

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The book explores the complexities of modern control systems, particularly advanced controllers like LQG, in light of recent advancements in control theory and computing. It addresses the challenges of tuning sophisticated controllers, which often involve numerous parameters that users may not fully understand. By employing Bayesian estimation, the author proposes a design algorithm that accommodates uncertainty by providing parameters as probability density functions, enabling more effective tuning across a range of potential models and real-world applications.