Robust PI Controller Design Satisfying Sensitivity and Uncertainty Specifications

This paper presents a control design method for determining proportional-integral-type controllers satisfying specifications on gain margin, phase margin, and an upper bound on the (complementary) sensitivity for a finite set of plants. The approach can be applied to plants that are stable or unstable, plants given by a model or measured data, and plants of any order, including plants with delays. The algorithm is efficient and fast, and as such can be used in near real-time to determine controller parameters (for online modification of the plant model including its uncertainty and/or the specifications). The method gives an optimal controller for a practical definition of optimality. Furthermore, it enables the graphical portrayal of design tradeoffs in a single plot, highlighting the effects of the gain margin, complementary sensitivity bound, low frequency sensitivity and high frequency sensor noise amplification

Robust PI Controller Design Satisfying Gain and Phase Margin Constraints

This paper presents a control design algorithm for determining PI-type controllers satisfying specifications on gain margin, phase margin, and an upper bound on the (complementary) sensitivity for a finite set of plants. Important properties of the algorithm are: (i) it can be applied to plants of any order including plants with delay, unstable plants, and plants given by measured data, (ii) it is efficient and fast, and as such can be used in near real-time to determine controller parameters (for on-line modification of the plant model including its uncertainty and/or the specifications), (iii) it can be used to identify the optimal controller for a practical definition of optimality, and (iv) it enables graphical portrayal of design tradeoffs in a single plot (highlighting tradeoffs among the gain margin, complementary sensitivity bound, low frequency sensitivity and high frequency sensor noise amplification)

Design of PID Controllers Satisfying Gain Margin and Sensitivity Constraints on a Set of Plants

This paper presents a method for the design of PID-type controllers, including those augmented by a filter on the D element, satisfying a required gain margin and an upper bound on the (complementary) sensitivity for a finite set of plants. Important properties of the method are: (i) it can be applied to plants of any order including non-minimum phase plants, plants with delay, plants characterized by quasi-polynomials, unstable plants and plants described by measured data, (ii) the sensors associated with the PI terms and the D term can be different (i.e., they can have different transfer function models), (iii) the algorithm relies on explicit equations that can be solved efficiently, (iv) the algorithm can be used in near real-time to determine a controller for on-line modification of a plant accounting for its uncertainty and closed-loop specifications, (v) a single plot can be generated that graphically highlights tradeoffs among the gain margin, (complementary) sensitivity bound, low-frequency sensitivity and high-frequency sensor noise amplification, and (vi) the optimal controller for a practical definition of optimality can readily be identified

Performance and robustness issues in the compensation of FOLPD processes with PI and PID controllers

This paper will discuss the compensation of first order lag plus time delay (FOLPD) processes using PI and PID controllers whose parameters are specified using appropriate tuning rules. The gain margin, phase margin and maximum sensitivity of the compensated system as the ratio of time delay to time constant of the process varies, are used as ways of judging the performance and robustness of the system

Genetic Algorithm Based Improved Sub-Optimal Model Reduction in Nyquist Plane for Optimal Tuning Rule Extraction of PID and PI{\lambda}D{\mu} Controllers via Genetic Programming

Genetic Algorithm (GA) has been used in this paper for a new Nyquist based sub-optimal model reduction and optimal time domain tuning of PID and fractional order (FO) PI{\lambda}D{\mu} controllers. Comparative studies show that the new model reduction technique outperforms the conventional H2-norm based reduced order modeling techniques. Optimum tuning rule has been developed next with a test-bench of higher order processes via Genetic Programming (GP) with minimum value of weighted integral error index and control signal. From the Pareto optimal front which is a trade-off between the complexity of the formulae and control performance, an efficient set of tuning rules has been generated for time domain optimal PID and PI{\lambda}D{\mu} controllers.Comment: 6 pages, 9 figure

Improved model reduction and tuning of fractional-order PI(λ)D(μ) controllers for analytical rule extraction with genetic programming

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Genetic algorithm (GA) has been used in this study for a new approach of suboptimal model reduction in the Nyquist plane and optimal time domain tuning of proportional-integral-derivative (PID) and fractional-order (FO) PI(λ)D(μ) controllers. Simulation studies show that the new Nyquist-based model reduction technique outperforms the conventional H(2)-norm-based reduced parameter modeling technique. With the tuned controller parameters and reduced-order model parameter dataset, optimum tuning rules have been developed with a test-bench of higher-order processes via genetic programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by a weighted sum of error index and controller effort. From the reported Pareto optimal front of the GP-based optimal rule extraction technique, a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP-based tuning rules has been compared with the original GA-based control performance for the PID and PI(λ)D(μ) controllers, handling four different classes of representative higher-order processes. These rules are very useful for process control engineers, as they inherit the power of the GA-based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three-dimensional plots of the required variation in PID/fractional-order PID (FOPID) controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP-based tuning rules has also been shown with credible simulation examples.This work has been supported by the Department of Science and Technology (DST), Government of India, under the PURSE programme

Sviluppo di tecniche di monitoraggio delle prestazioni di processi chimici controllati

La tesi proposta tratta del monitoraggio delle prestazioni dei controllori in processi chimici. Diverse sono le cause di malfunzionamento: da valvole con attrito, a regolatori sintonizzati impropriamente alla propagazione di disturbi negli impianti. Con questa tesi si vuole illustrare una metodologia per individuare le cause di mancata prestazione in modo da poterle classificare ed intraprendere le necessarie contromisure. In particolare é stato approfondito il problema della sintonizzazione dei regolatori ed è stata proposta una tecnica di identificazione basata sullo studio dei disturbi, evitando quindi ulteriori sollecitazioni agli impianti per variazioni di set-point. Inoltre è stato affrontato il problema dell’attrito sulle valvole utilizzando diverse tecniche di individuazione automatica originali e già presentetate in letteratura. Il tutto è stato organizzato in un software sviluppato in ambiente Matlab

Control of fluctuating renewable energy sources: energy quality & energy filters

This doctoral study discusses how to control fluctuating renewable energy sources at converter, unit, and system layers to deliver smoothed power output to the grid. This is particularly relevant to renewable power generation since the output power of many kinds of renewable energy sources have huge fluctuations (e.g. solar, wind and wave) that needs to be properly treated for grid integration. In this research, the energy quality is developed to describe the friendliness and compatibility of power flows/waveforms to the grid, by contrast with the well-known concept of power quality which is used to assess the voltage and current waveforms. In Chapter 1 & 2, a background introduction and a literature review of studied subjects are presented, respectively. In Chapter 3, the problem of determining the PI parameters in dq decoupling control of voltage source converter (VSC) is studied based on a state-space model. The problems of the conventional method when there is insufficient interface resistance are addressed. New methods are proposed to overcome these drawbacks. In Chapter 4 & 5, energy quality and the energy filters (EFs) are proposed as tools to assess and manage power fluctuations of renewable energy sources. The proposed EFs are energy storage control systems that could be implemented on a variety of energy storage hardware. EFs behave like low-pass filters to the power flows. Finally, in Chapter 6, as an application example of renewable power plant with energy filter control and smoothed power output, a master-slave wave farm system is proposed. The wave farm system uses enlarged rotor inertia of electric machines as self-energy storage devices