Lecture

Digital Control: Sampling Time Trade-offs

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In course

ME-323: Chemical process control

Provide the students with basic notions and tools for the modeling and analysis of dynamic systems. Show them how to design controllers and analyze the performance of controlled systems. ...

Description

This lecture provides a recap on selecting the sampling time in digital control systems, emphasizing the trade-offs between processing speed and avoiding quantization errors. The instructor explains the importance of finding the right balance to achieve optimal control performance and highlights the advantages of digital control systems over analog systems in terms of flexibility and cost-effectiveness. Additionally, the lecture covers the benefits of using microprocessors for implementing various control algorithms and performing system checks, ultimately concluding with the significance of considering sampling time and the effects of discretization and quantization to maximize the benefits of digital control.

Instructor

Ljubisa Miskovic

Ljubisa Miskovic earned his Ph.D. degree in Automatic Control from the Swiss Federal Institute of Technology (EPFL) under the co-supervision of Dominique Bonvin and Alireza Karimi, in 2006. He pursued his postdoctoral studies at the Centre for Systems Engineering and Applied Mechanics, Universite Catholique de Louvain with Michel Gevers before moving to the laboratory of Vassily Hatzimanikatis at the EPFL. In 2010, he became a research scientist. His research interests include systems biology, metabolic engineering, synthetic biology, data-driven control design, system identification, stochastic processes and estimation theory.

Official source

Related concepts (20)

Microprocessor

A microprocessor is a computer processor where the data processing logic and control is included on a single integrated circuit (IC), or a small number of ICs. The microprocessor contains the arithmetic, logic, and control circuitry required to perform the functions of a computer's central processing unit (CPU). The IC is capable of interpreting and executing program instructions and performing arithmetic operations.

Sampling (statistics)

In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt to collect samples that are representative of the population. Sampling has lower costs and faster data collection compared to recording data from the entire population, and thus, it can provide insights in cases where it is infeasible to measure an entire population.

Sampling error

In statistics, sampling errors are incurred when the statistical characteristics of a population are estimated from a subset, or sample, of that population. It can produced biased results. Since the sample does not include all members of the population, statistics of the sample (often known as estimators), such as means and quartiles, generally differ from the statistics of the entire population (known as parameters). The difference between the sample statistic and population parameter is considered the sampling error.

Soft microprocessor

A soft microprocessor (also called softcore microprocessor or a soft processor) is a microprocessor core that can be wholly implemented using logic synthesis. It can be implemented via different semiconductor devices containing programmable logic (e.g., ASIC, FPGA, CPLD), including both high-end and commodity variations. Most systems, if they use a soft processor at all, only use a single soft processor. However, a few designers tile as many soft cores onto an FPGA as will fit.

Stratified sampling

In statistics, stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. In statistical surveys, when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation (stratum) independently. Stratification is the process of dividing members of the population into homogeneous subgroups before sampling. The strata should define a partition of the population.

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