157 research outputs found

    INVERTED PENDULUM WITH LINEAR SYNCHRONOUS MOTOR SWING UP USING BOUNDARY VALUE PROBLEM

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    Research in the field of underactuated systems shows that control algorithms which take the natural dynamics of the system’s underactuated part into account are more energy-efficient than those utilizing fully-actuated systems. The purpose of this paper to apply the two-degrees-of-freedom (feedforward/feedback) control structure to design a swing-up manoeuver that involves tracking the desired trajectories so as to achieve and maintain the unstable equilibrium position of the pendulum on the cart system. The desired trajectories are obtained by solving the boundary value problem of the internal system dynamics, while the optimal state-feedback controller ensures that the desired trajectory is tracked with minimal deviations. The proposed algorithm is verified on the simulation model of the available laboratory model actuated by a linear synchronous motor, and the resulting program implementation is used to enhance the custom Simulink library Inverted Pendula Modeling and Control, developed by the authors of this paper

    Multi-mode control based on HSIC for double pendulum robot

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    Double pendulum robot has four equilibrium points: Down-Down, Down-Up, Up-Down, and Up-Up. Define the transfer control from one equilibrium point to another equilibrium point as acrobatic action of DPR, and there are total of 20 acrobatic actions. This paper proposes the multi-mode control algorithm based on Human Simulated Intelligent Control theory for the realization process of those acrobatic actions, which has the structure of multi sub-controllers and multi control modes. As an example, the acrobatic action from Down-Up to Up-Down is realized in simulation and real-time experiments, and the results demonstrate the effectiveness of the proposed algorithm

    Energy-Based Control for the Cart-Pole System in Implicit Port-Hamiltonian Representation

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    This master thesis is devoted to the design, analysis, and experimental validation of an energy-based control strategy for the well-known benchmark cart-pole system in implicit Port-Hamiltonian (PH) representation. The control scheme performs two tasks: swingup and (local) stabilization. The swing-up controller is carried out on the basis of a generalized energy function and consists of bringing the pendulum trajectories from the lower (stable) position to a limit cycle (homoclinic orbit), which passes by the upright (unstable) position, as well as the cart trajectories to the desired point. The (local) stabilizing controller is designed under a novel algebraic Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) technique and ensures the upright (asymptotic) stabilization of the pendulum as well as the cart at a desired position. To illustrate the effectiveness of the proposed control scheme, this work presents simulations and real-time experiments considering physical damping, i.e., viscous friction. The results are additionally contrasted with another energy-based control strategy for the cart-pole system in explicit Euler-Lagrange (EL) representation.Diese Masterarbeit widmet sich dem Entwurf, der Analyse und der experimentellen Validierung einer energiebasierten Regelstrategie für das bekannte Benchmarksystem des inversen Pendels auf einem Wagen in impliziter Port-Hamiltonscher (PH) Darstellung. Das Regelungssystem erfüllt zwei Aufgaben: das Aufschwingen und (lokale) Stabilisierung. Das Aufschwingen erfolgt auf Grundlage der generalisierten Energiefunktion und besteht darin, sowohl die Trajektorien des Pendels von der unteren (stabilen) Position in einen Grenzzyklus (homokliner Orbit) zu bringen, wobei die (instabile) aufrechte Lage passiert wird, als auch den Wagen in einer gewünschten Position einzustellen. Die (lokale) Regelung zur Stabilisierung ist nach einer neuartigen algebraischen Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) Methode konzipiert und gewährleistet die aufrechte (asymptotische) Stabilisierung des Pendels sowie die Positionierung des Wagens an einem gewünschten Referenzpunkt. Um die Funktionalität des entworfenen Regelungssystems zu veranschaulichen, werden in dieser Masterarbeit Simulationen und Echtzeit-Experimente unter Berücksichtigung der physikalischen Dämpfung, d.h. der viskosen Reibung, vorgestellt. Die Ergebnisse werden zusätzlich mit einem weiteren energiebasierten Regelungsansatz für das System des inversen Pendels auf einem Wagen in expliziter Euler-Lagrange (EL) Darstellung verglichen.Tesi

    Control of a modified double inverted pendulum using machine learning based model predictive control

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    Abstract: A machine learning-based controller (MLC) has been developed for a modified double inverted pendulum on a cart (MDIPC). First, the governing differential equations of the system are derived using the Lagrangian method. Then, a dataset is generated to train and test the machine learning-based models of the plant. Different types of machine learning models such as artificial neural networks (ANN), deep neural networks (DNN), long-short-term memory neural networks (LSTM), gated recurrent unit (GRU), and recurrent neural networks (RNN) are employed to capture the system’s dynamics. DNN and LSTM are selected due to their superior performance compared to other models. Finally, different variations of the Model Predictive Controller (MPC) are designed, and their performance is evaluated in terms of running time and tracking error. The proposed control methods are shown to have an advantage over the conventional nonlinear and linear model predictive control methods in simulation.Communication présentée lors du congrès international tenu conjointement par Canadian Society for Mechanical Engineering (CSME) et Computational Fluid Dynamics Society of Canada (CFD Canada), à l’Université de Sherbrooke (Québec), du 28 au 31 mai 2023

    On the Benefits of Surrogate Lagrangians in Optimal Control and Planning Algorithms

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    This paper explores the relationship between numerical integrators and optimal control algorithms. Specifically, the performance of the differential dynamical programming (DDP) algorithm is examined when a variational integrator and a newly proposed surrogate variational integrator are used to propagate and linearize system dynamics. Surrogate variational integrators, derived from backward error analysis, achieve higher levels of accuracy while maintaining the same integration complexity as nominal variational integrators. The increase in the integration accuracy is shown to have a large effect on the performance of the DDP algorithm. In particular, significantly more optimized inputs are computed when the surrogate variational integrator is utilized

    Exploration of Neural Structures for Dynamic System Control

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    Biological neural systems are powerful mechanisms for controlling biological sys- tems. While the complexity of biological neural networks makes exact simulation intractable, several key aspects lend themselves to implementation on computational systems. This thesis constructs a discrete event neural network simulation that implements aspects of biological neural networks. A combined genetic programming/simulated annealing approach is utilized to design network structures that function as regulators for continuous time dynamic systems in the presence of process noise when simulated using a discrete event neural simulation. Methods of constructing such networks are analyzed including examination of the final network structure and the algorithm used to construct the networks. The parameters of the network simulation are also analyzed, as well as the interface between the network and the dynamic system. This analysis provides insight to the construction of networks for more complicated control applications
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