Dynamic Stability of an Electric Monowheel System Using LQG-Based Adaptive Control

This paper presents the simulation and calculation-based aspect of constructing a dynamically stable, self-balancing electric monowheel from first principles. It further goes on to formulate a reference model-based adaptive control structure in order to maintain balance as well as the desired output. First, a mathematical model of the nonlinear system analyzes the vehicle dynamics, followed by an appropriate linearization technique. Suitable parameters for real-time vehicle design are calculated based on specific constraints followed by a proper motor selection. Various control methods are tested and implemented on the state-space model of this system. Initially, classical pole placement control is carried out in MATLAB to observe the responses. The LQR control method is also implemented in MATLAB and Simulink, demonstrating the dynamic stability and self-balancing system property. Subsequently, the system considers an extensive range of rider masses and external disturbances by introducing white noise. The parameter estimation of rider position has been implemented using Kalman Filter estimation, followed by developing an LQG controller for the system, in order to mitigate the disturbances caused by factors such as wind. A comparison between LQR and LQG controllers has been conducted. Finally, a reference model-assisted adaptive control structure has been established for the system to account for sudden parameter changes such as rider mass. A reference model stabilizer has been established for the same purpose, and all results have been obtained by running simulations on MATLAB Simulink.

Performance Improvement of Air Path Dynamics in Diesel Engines using LQR/LQG Optimal & Switching Control Techniques

The modern-day chalenge for the manipulate framework network is to enhance the exhibition of the kingdom elements associated enormous all spherical manner of diesel cars. The automobile enterprise is utilising Exhaust fuel Recirculation (EGR), Variable Geometry Turbine (VGT) and Fuelling has manipulate contributions of air elements. subsequently, on this paper analyses are executed whilst the framework is uncovered to deterministic and arbitrary commotions through structuring the controller utilizing Linear Quadratic Gaussian (LQG). The proposed controller is contrasted and Linear Quadratic Regulator (LQR) and Later, converting among LQG controllers is likewise proposed to approve the effects without changing.

Active suppression of freeplay aeroelastic vibrations of ailerons by robust control methods with incomplete measurements

Purpose The purpose of this paper is to analyze the active suppression of the nonlinear aeroelastic vibrations of ailerons caused by freeplay by robust H∞ and linear quadratic Gauss (LQG) methods of control in case of incomplete measurements of the state of the system. Design/methodology/approach The flexible wing with nonlinear aileron with freeplay is treated as a plant-controller system with H∞ and LQG controllers used to suppress the aeroelastic vibrations. The simulation approach was used for analyzing the impact of completeness of measurements on the efficiency and robustness of the controllers. Findings The analysis shows that the H∞ method can be effectively used for suppression of nonlinear aeroelastic vibrations of aileron, although its efficiency depends essentially on completeness and types of measurements. The LQG method is less effective, but it is also able to prevent aileron vibrations by reducing their amplitudes to acceptable, safe level. Research limitations/implications Only numerical analysis was carried out for the problem described; thus, the proposed solution is of theoretical value at this stage of analysis, and its application to the real suppression of aeroelastic vibrations requires further research. Practical implications The work presents a potentially useful solution to the problem of interest and results are a theoretical basis for further research. Social implications This work may lead to a hot debate on the advantages and drawbacks of the active suppression of vibrations in the aeroelasticians community. Originality/value The work raises the important questions of practical stabilizability of the nonlinear aeroelastic systems, their dependence on completeness and types of measurements and robustness of the controllers.

Modelling and control of non-linear tissue compression and heating using an LQG controller for automation in robotic surgery

Robot-assisted surgery is being widely used as an effective approach to improve the performance of surgical procedures. Autonomous control of surgical robots is essential for tele-surgery with time delay and increased patient safety. In order to improve safety and reliability of the surgical procedure of tissue compression and heating, a control strategy for simultaneously automating the surgical task is presented in this paper. First, the electrosurgical procedure such as vessel closure that involves tissue compression and heating has been modelled with a multiple-input–multiple-output (MIMO) non-linear system for automation simultaneous. After linearizing the models, the linear-quadratic Gaussian (LQG) is used to control the tissue compression process and tissue heating process, and the particle swarm optimization (PSO) algorithm was used to choose the optimal weighting matrices for the LQG controllers according to the desired controlling accuracy. The LQG controllers with optimal weights were able to track both the tissue compression and temperature references in finite time horizon and with minimal error (tissue compression – the max absolute error was [Formula: see text] m and temperature – the max absolute error is 0.4561°C). Compared with a LQG controller with weighting matrices chosen by trial and error, a PSO-based optimized controller provided the least error and faster convergence. We have developed a control framework for simultaneously automating the surgical tasks of tissue compression and heating in robotic surgery, and modelled the automation of electrosurgical task using LQG controller with optimal weighting matrix obtained using a PSO algorithm.