Broadband Noise Control Using Predictive Techniques

Predictive controllers have found applications in a wide range of industrial processes. Two types of such controllers are generalized predictive control and deadbeat control. Recently, deadbeat control has been augmented to include an extended horizon. This modification, named deadbeat predictive control, retains the advantage of guaranteed stability and offers a novel way of control weighting. This paper presents an application of both predictive control techniques to vibration suppression of plate modes. Several system identification routines are presented. Both algorithms are outlined and shown to be useful in the suppression of plate vibrations. Experimental results are given and the algorithms are shown to be applicable to non- minimal phase systems

A Model-Free Predictive Control Method Based on Polynomial Regression

This paper proposes a model-free predictive control method for nonlinear systems on the basis of polynomial regression. In contrast to conventional model predictive control, model-free predictive control does not require mathematical models. Instead, it uses the previous recorded input/output datasets of the controlled system to predict an optimal control input so as to achieve the desired output. The novel point in this paper is the improvement of existing model-free predictive control by adopting polynomial regression, which is a generalization of the so-called Volterra series expansion of nonlinear functions. © 2016 The Society of Instrument and Control Engineers-SICE.2nd SICE International Symposium on Control Systems, ISCS 2016; Nagoya Campus, Nanzan UniversityNagoya; Japan; 7 March 2016 through 10 March 2016; Category numberCFP16TPH-ART; Code 12164

Model-Free Predictive Control for Nonlinear Systems

13301甲第4574号博士（工学）金沢大学博士論文本文Full 以下に掲載予定：Journal of Control, Measurement, and System Integration The Society of Instrument and Control Engineers. 共著者：Hongran LI, Shigeru Yamamot

An adaptive pitch axis autopilot design for an unstable nonminimum phase pitch axis model

An adaptive pitch axis autopilot design procedure is presented. The design procedure is applicable to both stable and unstable pitch axis models and to those having nonminimum phase. The design approach assumes the adaptive autopilot is activated after achieving level flight. It is shown a rate-feedback compensator can be designed to ensure stable level flight pitch axis operation for the entire desired flight regime. The adaptive control loop design utilizes a pole-placement algorithm. The closed-loop characteristic polynomial is designed to have dominant poles of that of an ideal second order system to obtain the desired transient response. The identification of the system uses a modified least-squares algorithm with a variable forgetting factor. The nonlinear pitch axis model is used in simulations to evaluate the design. Command response tests include the step response and the ramp command response. Simulation results indicate that the adaptive pitch axis autopilot is capable of tracking altitude commands after activation. The closed-loop system response is close to that of the ideal second order system having the dominant poles

Retrospective Cost-based Adaptive Spacecraft Attitude Control.

Fixed gain attitude control laws are sensitive to modeling errors and actuator nonlinearities. Adaptive control can solve many of these challenges. We present a retrospective cost-based adaptive spacecraft attitude controller designed using the system's impulse response as modeling information. The performance metric is based on rotation matrices and thus, the controller does not suffer from singularities or discontinuities present in vector attitude representations. We demonstrate robustness to inertia and actuator scaling as well as actuator misalignment and nonlinearities, unknown disturbances, sensor noise and bias for thrusters and reaction wheels through numerical simulations. We implement an averaged Markov parameter and decentralized control to address the problem of the singular input matrix of magnetic torquers. For control moment gyros, we develop a hybrid linearization and impulse response-based Markov parameter and present new guidelines to evaluate the feasibility of desired rest-to-rest maneuvers. Finally, we address the problem of angular velocity-free attitude control of a flexible spacecraft with noncollocated sensors and actuators. We present a new approach to controlling harmonic nonminimum-phase systems using the step and impulse response of the linearized system. We demonstrate robustness to model uncertainty through system analysis and numerical simulations.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111607/1/gecruz_1.pd

Advanced controllers for building energy management systems. Advanced controllers based on traditional mathematical methods (MIMO P+I, state-space, adaptive solutions with constraints) and intelligent solutions (fuzzy logic and genetic algorithms) are investigated for humidifying, ventilating and air-conditioning applications.

This thesis presents the design and implementation of control strategies for building energy management systems (BEMS). The controllers considered include the multi PI-loop controllers, state-space designs, constrained input and output MIMO adaptive controllers, fuzzy logic solutions and genetic algorithm techniques. The control performances of the designs developed using the various methods based on aspects such as regulation errors squared, energy consumptions and the settling periods are investigated for different designs. The aim of the control strategy is to regulate the room temperature and the humidity to required comfort levels. In this study the building system under study is a 3 input/ 2 output system subject to external disturbances/effects. The three inputs are heating, cooling and humidification, and the 2 outputs are room air temperature and relative humidity. The external disturbances consist of climatic effects and other stochastic influences. The study is carried out within a simulation environment using the mathematical model of the test room at Loughborough University and the designed control solutions are verified through experimental trials using the full-scale BMS facility at the University of Bradford