A LOW-COST APPROACH TO DATA-DRIVEN FUZZY CONTROL OF SERVO SYSTEMS

Servo systems become more and more important in control systems applications in various fields as both separate control systems and actuators. Ensuring very good control system performance using few information on the servo system model (viewed as a controlled process) is a challenging task. Starting with authors’ results on data-driven model-free control, fuzzy control and the indirect model-free tuning of fuzzy controllers, this paper suggests a low-cost approach to the data-driven fuzzy control of servo systems. The data-driven fuzzy control approach consists of six steps: (i) open-loop data-driven system identification to produce the process model from input-output data expressed as the system step response, (ii) Proportional-Integral (PI) controller tuning using the Extended Symmetrical Optimum (ESO) method, (iii) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller in terms of the modal equivalence principle, (iv) closed-loop data-driven system identification, (v) PI controller tuning using the ESO method, (vi) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller. The steps (iv), (v) and (vi) are optional. The approach is applied to the position control of a nonlinear servo system. The experimental results obtained on laboratory equipment validate the approach

Gain tuning of proportional integral controller based on multiobjective optimization and controller hardware-in-loop microgrid setup

Proportional integral (PI) control is a commonly used industrial controller framework. This PI controller needs to be tuned to obtain desired response from the process under control. Tuning methods available in literature by and large need sophisticated mathematical modelling, and simplifications in the plant model to perform gain tuning. The process of obtaining approximate plant model conceivably become time consuming and produce less accurate results. This is due to the simplifications desired by the power system applications especially when power electronics based inverters are used in it. Optimal gain selection for PI controllers becomes crucial for microgrid application. Because of the presence of inverter based distributed energy resources. In the proposed approach, a multi-objective genetic algorithm is used to tune the controller to obtain expected step response characteristics. The proposed approach do not need simplified mathematical models. This prevents the need for obtaining unfailing plant models to maintain the fidelity of modelling. Microgrid system and the PI controller are modelled in different software, hardware platform and tuned using the proposed approach. Gain values for PI controller in these different platform are tuned using the same objective function and multi-objective optimization. This proves the re-usability, scalability, and modularity of the proposed tuning algorithm. Three different combination of software, hardware platform are proposed. First, the process and the PI controller are modelled in a computer based hardware. In order to increase the speed of the multi-objective optimization in the computer based hardware parallel computing is employed. This is a natural fit for paralleling the GA based optimization. Second, both the plant and control representation are modelled in the real time digital simulator (RTDS). Finally, a controller hardware in loop platform is used. In this platform, the plant will be modelled in RTDS and the PI controller will be modelled in an FPGA based hardware platform. Results indicate that the proposed approach has promising potentials since it does not need to simplify the switching model and can effectively solve the complicated tuning procedure using parallel computing. Similar advantage could be said for RTDS based tuning because RTDS simulates the models in real time

Data-Driven Model-Free Sliding Mode and Fuzzy Control with Experimental Validation

The paper presents the combination of the model-free control technique with two popular nonlinear control techniques, sliding mode control and fuzzy control. Two data-driven model-free sliding mode control structures and one data-driven model-free fuzzy control structure are given. The data-driven model-free sliding mode control structures are built upon a model-free intelligent Proportional-Integral (iPI) control system structure, where an augmented control signal is inserted in the iPI control law to deal with the error dynamics in terms of sliding mode control. The data-driven model-free fuzzy control structure is developed by fuzzifying the PI component of the continuous-time iPI control law. The design approaches of the data-driven model-free control algorithms are offered. The data-driven model-free control algorithms are validated as controllers by real-time experiments conducted on 3D crane system laboratory equipment

Topics in Automotive Rollover Prevention: Robust and Adaptive Switching Strategies for Estimation and Control

The main focus in this thesis is the analysis of alternative approaches for estimation and control of automotive vehicles based on sound theoretical principles. Of particular importance is the problem rollover prevention, which is an important problem plaguing vehicles with a high center of gravity (CG). Vehicle rollover is, statistically, the most dangerous accident type, and it is difficult to prevent it due to the time varying nature of the problem. Therefore, a major objective of the thesis is to develop the necessary theoretical and practical tools for the estimation and control of rollover based on robust and adaptive techniques that are stable with respect to parameter variations. Given this background, we first consider an implementation of the multiple model switching and tuning (MMST) algorithm for estimating the unknown parameters of automotive vehicles relevant to the roll and the lateral dynamics including the position of CG. This results in high performance estimation of the CG as well as other time varying parameters, which can be used in tuning of the active safety controllers in real time. We then look into automotive rollover prevention control based on a robust stable control design methodology. As part of this we introduce a dynamic version of the load transfer ratio (LTR) as a rollover detection criterion and then design robust controllers that take into account uncertainty in the CG position. As the next step we refine the controllers by integrating them with the multiple model switched CG position estimation algorithm. This results in adaptive controllers with higher performance than the robust counterparts. In the second half of the thesis we analyze extensions of certain theoretical results with important implications for switched systems. First we obtain a non-Lyapunov stability result for a certain class of linear discrete time switched systems. Based on this result, we suggest switched controller synthesis procedures for two roll dynamics enhancement control applications. One control design approach is related to modifying the dynamical response characteristics of the automotive vehicle while guaranteeing the switching stability under parametric variations. The other control synthesis method aims to obtain transient free reference tracking of vehicle roll dynamics subject to parametric switching. In a later discussion, we consider a particular decentralized control design procedure based on vector Lyapunov functions for simultaneous, and structurally robust model reference tracking of both the lateral and the roll dynamics of automotive vehicles. We show that this controller design approach guarantees the closed loop stability subject to certain types of structural uncertainty. Finally, assuming a purely theoretical pitch, and motivated by the problems considered during the course of the thesis, we give new stability results on common Lyapunov solution (CLS) existence for two classes of switching linear systems; one is concerned with switching pair of systems in companion form and with interval uncertainty, and the other is concerned with switching pair of companion matrices with general inertia. For both problems we give easily verifiable spectral conditions that are sufficient for the CLS existence. For proving the second result we also obtain a certain generalization of the classical Kalman-Yacubovic-Popov lemma for matrices with general inertia

Model-based and data-based frequency domain design of fixed structure robust controller: a polynomial optimization approach

L'abstract è presente nell'allegato / the abstract is in the attachmen

Payload analysis and control of manipulators for human interactive environments

Esta tesis doctoral presenta los resultados de simulaciones numéricas y algunos análisis experimentales de tres aspectos principales: el modelamiento dinámico de manipuladores de múltiples grados de libertad (GdL) (n 2 GdL), el cálculo de la capacidad dinámica de carga asociada al manejo de dicha carga, y el análisis y diseño de controladores no lineales incluyendo el Control Adaptativo por Desfalsificación (CAD). Se desarrollaron análisis de dos (2) casos de estudio: el SCORBOT ER V PLUS fabricado por Intelitech Corp. de 5 grados de libertad y el manipulador redundante de 7 grados de libertad conocido como el Whole Arm Manipulator (WAM) fabricado por Barrett Technology Inc. y que cuenta con características de seguridad intrínseca, manipulación inversa y docilidad, y es aplicable en la interacción humano-robot (IHR). Inicialmente, se calculó y validó el modelado dinámico de los casos de estudio. Los modelos dinámicos inverso y directo del SCORBOT ER V PLUS fueron validados numéricamente. Luego, una validación experimental para el WAM presenta una comparación entre los datos numéricos y experimentales, identificando la necesidad de un mejor modelo de la fricción seca. Después, se propuso y evaluó una metodología para el cálculo de la capacidad dinámica de carga en el espacio de trabajo completo de manipuladores para diferentes tipos de controladores. Luego, para el análisis del Control Adaptativo por Desfalsificación con factor de olvido para manipuladores de múltiples grados de libertad, se realizó una comparación con un controlador adaptativo tradicional basado en el modelo y se aplicó al modelo del manipulador SCORBOR ER V PLUS. Finalmente, la técnica de Control por Desfalsificación fue exitosamente aplicada al modelo del WAM. En conclusión, este trabajo puede contribuir al uso de técnicas de control no lineal avanzado y manejo de carga para manipuladores redundantes con manipulación inversa, aplicables en ambientes de interacción con humanosAbstract : This doctoral thesis presents the results of numerical simulations and some experimental analysis of three main topics: the dynamical modeling of multiple degree of freedom (MDoF) manipulators (n 2 DoF), dynamic load carrying capacity computation (DLCC) for the payload handling issue and nonlinear control analysis and design including Unfalsified Adaptive Control (UAC). We performed analysis of two (2) cases of study: the 5 DoF SCORBOT ER V PLUS manufactured by Intelitech Corp. and the 7 DoF redundant Whole Arm Manipulator (WAM) manufactured by Barrett Technology Inc. with intrinsic safety, backdrivable and compliant characteristics and suitable for human-robot interaction (HRI). Initially, we computed and validated the dynamical model of the cases of study. The inverse and direct dynamical models of the SCORBOT ER V PLUS were numerically validated. Then, an experimental validation of inverse dynamical model of the WAM presents a comparison between numerical and experimental data, identifying the need for better friction models. After that, we proposed and evaluated a methodology for DLCC computation in the entire workspace of manipulators for different types of controllers. Then, for the analysis of the data-driven UAC with fading memory for multiple DoF manipulators, we performed a comparison with a traditional modelbased Adaptive Controller and applied to the SCORBOT ER V PLUS manipulator. Finally, the Unfalsified Control technique was successfully applied to the WAM model for a similar simulation setup. In conclusion, this work may contribute to the use of advanced nonlinear control and payload handling techniques for redundant backdrivable multiple DoF manipulators, suitable for human interactive environmentsDoctorad

Uncertainty remodeling for robust control of linear time-invariant plants

The paper proposes a measure of robust performance based on frequency domain experimental data that allows non-conservative modeling of uncertainty. Given the nominal model of the plant and closed-loop performance specifications the iterative control design and remodeling of model uncertainty based on that measure leads to a controller with improved robust performance. The structured dynamic uncertainty is allowed to act on the nominal model in a linear fractional transformation (LFT) form. The proposed method is a modification of the structured singular value with implicit constraints on model consistency. The usefulness of the method is demonstrated on a vehicle control simulation example

Adaptive Data-Driven Control for Linear Time Varying Systems

In this paper, we propose an adaptive data-driven control approach for linear time varying systems, affected by bounded measurement noise. The plant to be controlled is assumed to be unknown, and no information in regard to its time varying behaviour is exploited. First, using set-membership identification techniques, we formulate the controller design problem through a model-matching scheme, i.e., designing a controller such that the closed-loop behaviour matches that of a given reference model. The problem is then reformulated as to derive a controller that corresponds to the minimum variation bounding its parameters. Finally, a convex relaxation approach is proposed to solve the formulated controller design problem by means of linear programming. The effectiveness of the proposed scheme is demonstrated by means of two simulation examples