Iterative Learning Control Algorithms and Experimental Benchmarking 1st Edition

Capture the forefront of precision control with Iterative Learning Control Algorithms and Experimental Benchmarking, 1st Edition by Eric Rogers, Bing Chu, Christopher Freeman, and Paul Lewin. This concise, authoritative volume brings rigorous theory and real-world verification together for engineers, researchers, and advanced students seeking reliable, repeatable performance in repetitive tasks.

Grounded in clear explanations and comparative analysis, the book walks readers through modern iterative learning control (ILC) methods, from foundational convergence proofs to robust, adaptive schemes designed for noisy, uncertain environments. Detailed experimental benchmarking highlights practical strengths and limitations across a range of platforms—industrial robots, precision manufacturing systems, aerospace actuators, and medical devices—helping you select and tune algorithms with confidence.

What sets this edition apart is its balance of mathematics and engineering intuition: algorithmic derivations are paired with performance metrics, implementation guidance, and comparative results that translate theory into measurable improvement. Whether you’re optimizing motion profiles on a production line in North America, refining robotic repeatability in Europe, or deploying adaptive controllers across the Asia-Pacific, this book provides actionable insight that accelerates development and reduces commissioning time.

Ideal for control engineers, systems integrators, and academic labs, this text is an essential reference for anyone focused on enhancing accuracy and efficiency through learning-based control. Take the next step toward better, faster, and more reliable repetitive performance—order your copy of Iterative Learning Control Algorithms and Experimental Benchmarking today and start turning theory into tangible gains.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.