Design and Implementation of a Furuta Pendulum Device for Benchmarking Non-Linear Control Methods

Furuta pendulum is an academic benchmark example for evaluating non-linear control algorithms. The main aim of this dissertation is to study this physical system, showing its dynamic model and several strategies for its control. An assortment of swing-up and upright control approaches is reported with its design and simulations. Besides, this document describes the project development which is being done at the FabLab of the Obuda University, whose objective is to design and manufacture a demonstration device that is capable to test and display various control strategies. Requirements and specications of the design, used tools and future work are described. The dissertation is structured in eight different chapters: (1) History of the Furuta pendulum, describing the origin of this system; (2) State-of-the-art in non-linear control, giving a background for the different control strategies; (3) Dynamic model of the Furuta pendulum; (4) Swing-up by energy control, based on the work of Astrom and Furuta; (5) Stabilizing local control, via full state feedback; (6) Hybrid control, which sums up the previous approaches; (7) Development project, which describes the work realized in the FabLab and (8) Conclusion, discussing the knowledge extracted from the development of this thesis

Non-conventional control of the flexible pole-cart balancing problem

Emerging techniques of intelligent or learning control seem attractive for applications in manufacturing and robotics. It is however important to understand the capabilities of such control systems. In the past the inverted pendulum has been used as a test case. The thesis begins with an examination of whether the inverted pendulum or polecart balancing problem is a representative problem for experimentation for learning controllers for complex nonlinear systems. Results of previous research concerning the inverted pendulum problem are presented to show that this problem is not sufficiently testing. This thesis therefore concentrates on the control of the inverted pendulum with an additional degree of freedom as a testing demonstrator problem for learning control system experimentation. A flexible pole is used in place of a rigid one. The transverse displacement of the flexible pole adds a degree of freedom to the system. The dynamics of this new system are more complex as the system needs additional parameters to be defIned due to the pole's elastic deflection. This problem also has many of the signifIcant features associated with flexible robots with lightweight links as applied in manufacturing. Novel neural network and fuzzy control systems are presented that control such a system both in simulation and real time. A fuzzy-genetic approach is also demonstrated that allows the creation of fuzzy control systems without the use of extensive knowledge

Experimental characterisation of the motion of an inverted pendulum

[EN] : In this paper, we present a home-made experimental set-up to study the falling movement of an inverted pendulum. The experimental set-up allows preparing a laboratory session for first year Physics or Engineering students. This set-up has been used in the Bachelor's Degree in Mechanical Engineering at the School of Design Engineering of the Universitat Politècnica de València. The experimental data are fitted to the theoretical equation of motion, obtaining a very good agreement between experiment and theory. In addition, direct measurement of the parameters involved in the equations was carried out, showing a very good agreement with the calculated parameters.Gómez Tejedor, JA.; Mollar, M.; Monsoriu Serra, JA. (2015). Experimental characterisation of the motion of an inverted pendulum. En 1ST INTERNATIONAL CONFERENCE ON HIGHER EDUCATION ADVANCES (HEAD' 15). Editorial Universitat Politècnica de València. 588-592. https://doi.org/10.4995/HEAD15.2015.331OCS58859

Adaptive dynamic programming with eligibility traces and complexity reduction of high-dimensional systems

This dissertation investigates the application of a variety of computational intelligence techniques, particularly clustering and adaptive dynamic programming (ADP) designs especially heuristic dynamic programming (HDP) and dual heuristic programming (DHP). Moreover, a one-step temporal-difference (TD(0)) and n-step TD (TD(λ)) with their gradients are utilized as learning algorithms to train and online-adapt the families of ADP. The dissertation is organized into seven papers. The first paper demonstrates the robustness of model order reduction (MOR) for simulating complex dynamical systems. Agglomerative hierarchical clustering based on performance evaluation is introduced for MOR. This method computes the reduced order denominator of the transfer function by clustering system poles in a hierarchical dendrogram. Several numerical examples of reducing techniques are taken from the literature to compare with our work. In the second paper, a HDP is combined with the Dyna algorithm for path planning. The third paper uses DHP with an eligibility trace parameter (λ) to track a reference trajectory under uncertainties for a nonholonomic mobile robot by using a first-order Sugeno fuzzy neural network structure for the critic and actor networks. In the fourth and fifth papers, a stability analysis for a model-free action-dependent HDP(λ) is demonstrated with batch- and online-implementation learning, respectively. The sixth work combines two different gradient prediction levels of critic networks. In this work, we provide a convergence proofs. The seventh paper develops a two-hybrid recurrent fuzzy neural network structures for both critic and actor networks. They use a novel n-step gradient temporal-difference (gradient of TD(λ)) of an advanced ADP algorithm called value-gradient learning (VGL(λ)), and convergence proofs are given. Furthermore, the seventh paper is the first to combine the single network adaptive critic with VGL(λ). --Abstract, page iv