Active disturbance rejection control

Active disturbance rejection control (or ADRC) is a model-free control technique used for designing controllers for systems with unknown dynamics and external disturbances. This approach only necessitates an estimated representation of the system's behavior to design controllers that effectively counteract disturbances without causing any overshooting. ADRC has been successfully used as an alternative to PID control in many applications, such as the control of permanent magnet synchronous motors, thermal power plants and robotics. In particular, the precise control of brushless motors for joint motion is vital in high-speed industrial robot applications. However, flexible robot structures can introduce unwanted vibrations, challenging PID controllers. ADRC offers a solution by real-time disturbance estimation and compensation, without needing a detailed model. Disturbance rejection To achieve robustness, ADRC is based on extension of the system model with an additional and

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Data-Driven Distributed Reactive Power Sharing in Microgrids

Alireza Karimi, Seyed Sohail Madani

In this paper, reactive power sharing for Photovoltaic (PV) units in islanded microgrids has been formulated as a robust control design problem and is solved using convex optimization method. In addition to reactive power sharing, the disturbance rejection for voltage and active power have been formulated using infinity-norm constraints on the sensitivity functions and considered in the design. The proposed method uses only the measurement data of the power system with no need for a parametric model of the power grid equipment. The size of the problem is independent of the order of the plant which makes it applicable to power systems including a high number of buses and equipment such as synchronous generators, batteries and inverters. In the proposed method, the communication system can be considered in the control design process for centralized, distributed and decentralized structures. The proposed method has been validated through simulation of a microgrid encompassing synchronous generator, switching inverters and storage system. The results show that this method has successfully shared reactive power among different PV units while providing disturbance rejection for voltage and active power.

IEEE2020

Chargement

Data-Driven Passivity-Based Current Controller Design for Power-Electronic Converters of Traction Systems

Alireza Karimi, Seyed Sohail Madani

In this paper, a data-driven method for controller design with constraints on the positive-realness of closed-loop transfer functions over an arbitrary set of frequencies is proposed. The positive-realness of a closed-loop transfer function is represented by a set of convex constraints involving only the frequency response data of the plant model and the parameters of a fixed structure controller. The new convex constraints, are then integrated into a recently developed data-driven robust control framework that can consider other control performance and robustness specifications. The proposed method is applied to the current controller design in traction systems. According to the field standards, the real part of the input admittance of the converters should be positive for a specific range of frequencies. The existing methods in the literature are based on the passivity approaches using a parametric model of the system and usually require a disturbance observer and additional sensors. In the proposed method, only the measurement data is needed for controller design and there is no requirement of additional sensors that reduces the costs and increases the reliability. The effectiveness of the proposed method is validated through numerical simulation including switching converters. The results show that the proposed controller provides required positive-realness as well as good performance in tracking and disturbance rejection.

IEEE2020

Chargement

Combining a 3D Reflex Based Neuromuscular Model with a State Estimator Based on Central Pattern Generators

Tycho Jan Henricus Brug, Florin Dzeladini, Auke Ijspeert, Amy Roning Wu

A neuromuscular model (NMC) presented by H. Geyer and extended by S. Song shows very interesting similarities with real human locomotion. The model uses a combination of reflex loops to generate stable locomotion and is able to cope with external disturbances and adapt to different conditions. However, to our knowledge no works exist on the capability of the model to handle sensory noise. In this paper, we present a method for designing Central Pattern Generators (CPG) as feedback predictors, which can be used to handle large amount of sensory noise. We show that the whole system (NMC + CPG) is able to cope with a very large amount of noise, much larger than what the original system (NMC) could handle.

Springer International Publishing Ag2017

Chargement

ME-273: Introduction to control of dynamical systems

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ME-323: Chemical process control

Apporter aux étudiants les connaissances de base nécessaires à la modélisation et à l'analyse des systèmes dynamiques. Leur apprendre à concevoir des régulateurs et à analyser la performance des systèmes commandés.

EE-611: Linear system theory

The course covers control theory and design for linear time-invariant systems : (i) Mathematical descriptions of systems (ii) Multivariables realizations; (iii) Stability ; (iv) Controllability and Observability; (v) Minimal realizations and coprime fractions; (vi) Pole placement and model matching.