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Lecture

Motor control systems

In course

CH-234: Organic functions and reactions II

To develop basic understanding of the reactivity of aromatic and heteroaromatic compounds. To develop a knowledge of a class of pericyclic reactions. To apply them in the context of the synthesis. ...

Description

This lecture covers the fundamentals of motor control systems, including topics such as motor control algorithms, sensor integration, and system optimization. It also discusses practical applications in robotics and industrial automation, emphasizing the importance of precise control for efficient operation.

Instructors (2)

Nicolai Cramer

Nicolai Cramer was born in Stuttgart, Germany; he studied chemistry at the University of Stuttgart where he graduated in 2003, and earned his PhD in 2005 under the guidance of Professor Sabine Laschat. After a research stage at Osaka University, Japan, he joined the group of Professor Barry M. Trost at Stanford University as a postdoctoral fellow in 2006. From 2007 on, he worked on his habilitation at the ETH Zurich associated to the chair of Professor Erick M. Carreira and recieved the venia legendi in 2010. In 2010, he started as Assistant Professor at the EPF Lausanne and was promoted to Associate Professor in 2013 and to Full Professor in 2015. His main research program encompasses enantioselective metal-catalyzed transformations and their implementation for the synthesis of biologically active molecules. Author profile (Angew. Chem. Int. Ed.) CV

Felix Daniel Bobbink

Official source

Related concepts (24)

Mathematical optimization

Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.

Ant colony optimization algorithms

In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Artificial ants stand for multi-agent methods inspired by the behavior of real ants. The pheromone-based communication of biological ants is often the predominant paradigm used. Combinations of artificial ants and local search algorithms have become a method of choice for numerous optimization tasks involving some sort of graph, e.

Multidisciplinary design optimization

Multi-disciplinary design optimization (MDO) is a field of engineering that uses optimization methods to solve design problems incorporating a number of disciplines. It is also known as multidisciplinary system design optimization (MSDO), and Multidisciplinary Design Analysis and Optimization (MDAO). MDO allows designers to incorporate all relevant disciplines simultaneously. The optimum of the simultaneous problem is superior to the design found by optimizing each discipline sequentially, since it can exploit the interactions between the disciplines.

Multi-objective optimization

Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective is a type of vector optimization that has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives.

Combinatorial optimization

Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects, where the set of feasible solutions is discrete or can be reduced to a discrete set. Typical combinatorial optimization problems are the travelling salesman problem ("TSP"), the minimum spanning tree problem ("MST"), and the knapsack problem. In many such problems, such as the ones previously mentioned, exhaustive search is not tractable, and so specialized algorithms that quickly rule out large parts of the search space or approximation algorithms must be resorted to instead.

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