Development of a fuzzy-state feedback regulator for stabilizing a flexible inverted pendulum system

Accurate modeling and efficient control of inverted pendulums have always been a challenge for researchers. So, the current research aims to achieve the following objectives: (I) proposing a comprehensive dynamic model for the inverted pendulums which accounts for the flexibility of the pendulum bar and (II) suggesting an appropriate supervisory fuzzy-pole placement control strategy for stabilizing the pendulum system. Using a Lagrangian formulation, the equations of motion are derived and linearized. Then, a state feedback controller with a reduced-order observer is designed to stabilize the system. Closed-loop simulations reveal that at least six modes shall be considered in the dynamic equations. To improve the quality of the transient response, a novel fuzzy system is developed for real-time assignment of the controller poles. Simulation results demonstrate that the control quality is significantly improved by adding a supervisory fuzzy system to the control loop. The developed approach for dynamic modeling of the system, and the idea of multi-level fuzzy-pole placement control architecture developed in this paper, may be successfully applied to improve the response specifications in other dynamic systems.

Research on UDE control based on load torque compensation of PMSM

In order to solve the problem that the traditional uncertainty and disturbance estimator (UDE) control needs to increase the filter order to keep good performance when facing rapid disturbance changes, thus lead to cost increase in implementing the system, a speed control strategy of permanent magnet synchronous motor (PMSM) driver based on reduced order observer compensation is proposed. The designed control strategy is robust to the system with internal parameter variation and external torque disturbance. Through the compensation of load torque, the pressure of UDE controller is relieved, and then the tracking error of high-frequency component in load torque is eliminated, and the control performance of the system is improved more effectively. This paper proves the superiority of the new compound controller through comparison of simulation. results

Autoencoder-Based Reduced Order Observer Design for a Class of Diffusion-Convection-Reaction Systems

The application of autoencoders in combination with Dynamic Mode Decomposition for control (DMDc) and reduced order observer design as well as Kalman Filter design is discussed for low order state reconstruction of a class of scalar linear diffusion-convection-reaction systems. The general idea and conceptual approaches are developed following recent results on machine-learning based identification of the Koopman operator using autoencoders and DMDc for finite-dimensional discrete-time system identification. The resulting linear reduced order model is combined with a classical Kalman Filter for state reconstruction with minimum error covariance as well as a reduced order observer with very low computational and memory demands. The performance of the two schemes is evaluated and compared in terms of the approximated L2 error norm in a numerical simulation study. It turns out, that for the evaluated case study the reduced-order scheme achieves comparable performance with significantly less computational load.

Observer Based Multi-Level Fault Reconstruction for Interconnected Systems

The problem of local fault (unknown input) reconstruction for interconnected systems is addressed in this paper. This contribution consists of a geometric method which solves the fault reconstruction (FR) problem via observer based and a differential algebraic concept. The fault diagnosis (FD) problem is tackled using the concept of the differential transcendence degree of a differential field extension and the algebraic observability. The goal is to examine whether the fault occurring in the low-level subsystem can be reconstructed correctly by the output at the high-level subsystem under given initial states. By introducing the fault as an additional state of the low subsystem, an observer based approached is proposed to estimate this new state. Particularly, the output of the lower subsystem is assumed unknown, and is considered as auxiliary outputs. Then, the auxiliary outputs are estimated by a sliding mode observer which is generated by using global outputs and inverse techniques. After this, the estimated auxiliary outputs are employed as virtual sensors of the system to generate a reduced-order observer, which is caplable of estimating the fault variable asymptotically. Thus, the purpose of multi-level fault reconstruction is achieved. Numerical simulations on an intensified heat exchanger are presented to illustrate the effectiveness of the proposed approach.