Switched Unfalsified Multicontroller

In this paper, we present a controller design strategy for the implementation of a multicontroller structure for single-input single-output (SISO) plants. The overall control system can be viewed as a feedback interconnection of a SISO plant, a set of candidate controllers and a switched selection scheme that supervises the switching process among the candidate controllers. The switching scheme is designed without explicit assumptions on the plant model, based on the unfalsified control concept introduced by Safonov [1,2]. A switched multicontroller structure is implemented and experimental results are presented.publishersversionpublishe

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

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance

A Data-Driven Quadratic Stability Criterion and its Application

This thesis focuses on developing a cognition-oriented robust controller to realize quadratic stabilization of unknown nonlinear systems. The core of the proposed control relies on a data-driven stability criterion, which is established according to the geometric interpretation of the quadratic Lyapunov stability condition. By properly arranging soft-computing techniques and the proposed stability criterion within a cognitive framework serving as basic cognitive functions and expert knowledge base, respectively, the proposed method can build the internal representation of the knowledge about the quadratic stability and the dynamics of the current motion of the system to be controlled. Based on these representations, suitable control input can be generated by the planning module of the proposed controller to stabilize the motion of the unknown systems. Due to these reasons, the propose controller can be classified into cognitive approaches. At the end of this thesis two numerical examples are shown to demonstrate the successful application and performance of the proposed control method

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