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Nonlinear Optimal Control for a Gas Compressor Actuated by a Five-Phase Induction Motor

Rigatos Gerasimos.G., Abbaszadeh M., Sari B., Siano P.

Power Electronics and Drives

2023

Vol. 8 (43)

196-218

The article proposes a nonlinear optimal control method for the dynamic model of a gas centrifugal compressor being actuated by a five-phase induction motor (5-phase IM). To achieve high torque and high power in the functioning of gas compressors, 5-phase IM appear to be advantageous in comparison to three-phase synchronous or asynchronous electric machines. The dynamic model of the integrated compression system, which comprises the gas compressor and the 5-phase IM, is first written in a nonlinear and multivariable state-space form. It is proven that the electrically driven gas-compression system is differentially flat. Next, this system is approximately linearised around a temporary operating point that is recomputed at each sampling interval. The linearisation is based on first-order Taylor series expansion and uses the computation of the Jacobian matrices of the state-space model of the integrated system. For the linearised state-space description of the compressor and 5-phase IM, a stabilising optimal (H-infinity) feedback controller is designed. This controller achieves a solution to the nonlinear optimal control problem of the compressor and 5-phase IM system under model uncertainty and external perturbations. The feedback gains of the controller are computed by solving an algebraic Riccati equation at each iteration of the control method. Lyapunov analysis is used to demonstrate global stability for the control loop. Additionally, the H-infinity Kalman filter is used as a robust state estimator, which allows for implementing sensorless control for the gas compression system.

Flatness-based adaptive fuzzy control of spark-ignited engines

Rigatos G.G., Siano P.

Journal of Artificial Intelligence and Soft Computing Research

2014

Vol. 4, No. 4

231--242

An adaptive fuzzy controller is designed for spark-ignited (SI) engines, under the constraint that the system’s model is unknown. The control algorithm aims at satisfying the H∞ tracking performance criterion, which means that the influence of the modeling errors and the external disturbances on the tracking error is attenuated to an arbitrary desirable level. After transforming the SI-engine model into the canonical form, the resulting control inputs are shown to contain nonlinear elements which depend on the system’s parameters. The nonlinear terms which appear in the control inputs are approximated with the use of neuro-fuzzy networks. It is shown that a suitable learning law can be defined for the aforementioned neuro-fuzzy approximators so as to preserve the closed-loop system stability. With the use of Lyapunov stability analysis it is proven that the proposed adaptive fuzzy control scheme results in H∞ tracking performance. The efficiency of the proposed adaptive fuzzy control scheme is checked through simulation experiments.