An investigation into the merits of fuzzy logic control versus classical control.

A project report submitted to the Faculty of Engineering, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Engineering.Up to now the benefits and problems with fuzzy control have not been fully identified and its role in the control domain needs investigation. The past trend has been to show that a fuzzy controller can provide better control than classical control, without examining what is actually being achieved. The aim in this project report is to give a fair comparison between classical and fuzzy control. Robustness, disturbance rejection, noise suppression" nonminimurn phase and dead time are examined for both controllers. The comparison is performed through computer simulation of classical and fuzzy controlled plant models. Fuzzy control has the advantage of non-linear performance and the ability to capture linguistic information. Translating quantitative information into the fuzzy domain is difficult; therefore when the system is easily mathematically modelled and linear, classical control is usually better. Which controller should be used depends on the application, control designer and information available.Andrew Chakane 201

Relay Feedback and Multivariable Control

This doctoral thesis treats three issues in control engineering related to relay feedback and multivariable control systems. Linear systems with relay feedback is the first topic. Such systems are shown to exhibit several interesting behaviors. It is proved that there exist multiple fast relay switches if and only if the sign of the first non-vanishing Markov parameter of the linear system is positive. It is also shown that these fast switches can appear as part of a stable limit cycle. A linear system with pole excess one or two is demonstrated to be particularly interesting. Stability conditions for these cases are derived. It is also discussed how fast relay switches can be approximated by sliding modes. Performance limitations in linear multivariable control systems is the second topic. It is proved that if the top left submatrices of a stable transfer matrix have no right half-plane zeros and a certain high-frequency condition holds, then there exists a diagonal stabilizing feedback that makes a weighted sensitivity function arbitrarily small. Implications on control structure design and sequential loop-closure are given. A novel multivariable laboratory process is also presented. Its linearized dynamics have a transmission zero that can be located anywhere on the real axis by simply adjusting two valves. This process is well suited to illustrate many issues in multivariable control, for example, control design limitations due to right half-plane zeros. The third topic is a combination of relay feedback and multivariable control. Tuning of individual loops in an existing multivariable control system is discussed. It is shown that a specific relay feedback experiment can be used to obtain process information suitable for performance improvement in a loop, without any prior knowledge of the system dynamics. The influence of the loop retuning on the overall closed-loop performance is derived and interpreted in several ways

Non-Iterative Data-Driven Model Reference Control

In model reference control, the objective is to design a controller such that the closed-loop system resembles a reference model. In the standard model-based solution, a plant model replaces the unknown plant in the design phase. The norm of the error between the controlled plant model and the reference model is minimized. The order of the resulting controller depends on the order of the plant model. Furthermore, since the plant model is not exact, the achieved closed-loop performance is limited by the quality of the model. In recent years, several data-driven techniques have been proposed as an alternative to this model-based approach. In these approaches, the order of the controller can be fixed. Since no model is used, the problem of undermodeling is avoided. However, closed-loop stability cannot, in general, be guaranteed. Furthermore, these techniques are sensitive to measurement noise. This thesis treats non-iterative data-driven controller tuning. This controller tuning approach leads to an identification problem where the input is affected by noise, and not the output as in standard identification problems. A straightforward data-driven tuning scheme is proposed, and the correlation approach is used to deal with measurement noise. For linearly parameterized controllers, this leads to a convex optimization problem. The accuracy of the correlation approach is compared to that of several solutions proposed in the literature. It is shown that, if the order of the controller is fixed, both the correlation approach and a specific errors-in-variables approach can be used. The model reference controller-tuning problem is extended with a constraint that ensures closed-loop stability. This constraint is derived from stability conditions based on the small-gain theorem. For linearly parameterized controllers, the resulting optimization problem is convex. The proposed constraint for stability is conservative. As an alternative, a non-conservative a posteriori stability test is developed based on similar stability conditions. The proposed methods are applied to several numerical and experimental examples

Vibration suppression in multi-body systems by means of disturbance filter design methods

This paper addresses the problem of interaction in mechanical multi-body systems and shows that subsystem interaction can be considerably minimized while increasing performance if an efficient disturbance model is used. In order to illustrate the advantage of the proposed intelligent disturbance filter, two linear model based techniques are considered: IMC and the model based predictive (MPC) approach. As an illustrative example, multivariable mass-spring-damper and quarter car systems are presented. An adaptation mechanism is introduced to account for linear parameter varying LPV conditions. In this paper we show that, even if the IMC control strategy was not designed for MIMO systems, if a proper filter is used, IMC can successfully deal with disturbance rejection in a multivariable system, and the results obtained are comparable with those obtained by a MIMO predictive control approach. The results suggest that both methods perform equally well, with similar numerical complexity and implementation effort

Complex Dynamics in Fed-Batch Systems: Modeling, Analysis and Control of Alcoholic Fermentations

Modeling and control of fed-batch fermentation processes has been a subject of great interest to realize high productivity and yields from the fermentation technique. The goal of this dissertation was to gain insights into how the complex dynamic behaviors exhibited in fed-batch fermentation systems affect the stability of standard single-loop as well as non-standard feedback control structures. Novel PID stability theorems were established to help construct the controller stabilizing regions

Nonlinear continuous-time generalised predictive control

The development of the nonlinear version of the Continuous-time Generalised Predictive Control (NCGPC) is presented. Unlike the linear version, the nonlinear version is developed in state-space form and shown to include Nonlinear Generalised Minimum Variance (NGMV), and a new algorithm, Nonlinear Predictive Generalised Minimum Variance (NPGMV), as special cases. Through simulations, it is demonstrated that NCGPC can deal with nonlinear systems whose relative degree is not well defined and nonlinear systems with unstable zero dynamics. Geometric approaches, such as exact linearisation, are shown to be included in the NCGPC as special cases

Iso-m based adaptive fractional order control with application to a soft robotic neck

This article proposes an adaptive fractional feedback control using recursive least squares algorithm for plant identification and a recent real-time method (iso-m) for fractional controller tuning. The combination of both methods allows keeping the same original performance specifications invariant, combining adaptability and robustness in a single scheme. Thanks to the robust controller, the system performance is maintained around a specified operating point, and due to the adaptive scheme, this operating point is adjusted depending on plant changes. Extensive experimentation of the proposal is carried out in a real platform with non-linear time varying properties, a soft robotic neck made of 3D printer soft materials. The experiments proposed consist in the neck inclination control using tilt sensors installed on the tip. According to expectations, an invariant performance despite plant parameter changes was observed throughout the experiments. The good results obtained in the proposed test platform suggest that the benefits of this control scheme are suitable for other nonlinear time varying applications.This work was supported in part by the Spanish Ministry of Economy and Competitiveness through the Exoesqueleto para Diagnostico y Asistencia en Tareas de Manipulación Spanish Research Project under Grant DPI2016-75346-R and the HUMASOFT Project under Grant DPI2016-75330-P, in part by the Programas de Actividades I+D en la Comunidad de Madrid, RoboCity2030-DIH-CM, through the Madrid Robotics Digital Innovation Hub (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos, Fase IV) under Grant S2018/NMT-4331, and in part by the Structural Funds of the EU

Adaptive Input Reconstruction with Application to Model Refinement, State Estimation, and Adaptive Control.

Input reconstruction is the process of using the output of a system to estimate its input. In some cases, input reconstruction can be accomplished by determining the output of the inverse of a model of the system whose input is the output of the original system. Inversion, however, requires an exact and fully known analytical model, and is limited by instabilities arising from nonminimum-phase zeros. The main contribution of this work is a novel technique for input reconstruction that does not require model inversion. This technique is based on a retrospective cost, which requires a limited number of Markov parameters. Retrospective cost input reconstruction (RCIR) does not require knowledge of nonminimum-phase zero locations or an analytical model of the system. RCIR provides a technique that can be used for model refinement, state estimation, and adaptive control. In the model refinement application, data are used to refine or improve a model of a system. It is assumed that the difference between the model output and the data is due to an unmodeled subsystem whose interconnection with the modeled system is inaccessible, that is, the interconnection signals cannot be measured and thus standard system identification techniques cannot be used. Using input reconstruction, these inaccessible signals can be estimated, and the inaccessible subsystem can be fitted. We demonstrate input reconstruction in a model refinement framework by identifying unknown physics in a space weather model and by estimating an unknown film growth in a lithium ion battery. The same technique can be used to obtain estimates of states that cannot be directly measured. Adaptive control can be formulated as a model-refinement problem, where the unknown subsystem is the idealized controller that minimizes a measured performance variable. Minimal modeling input reconstruction for adaptive control is useful for applications where modeling information may be difficult to obtain. We demonstrate adaptive control of a seeker-guided missile with unknown aerodynamics.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91520/1/amdamato_1.pd

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