Ball and Beam Control using Adaptive PID based on Q-Learning

The ball and beam system is one of the most used systems for benchmarking the controller response because it has nonlinear and unstable characteristics. Furthermore, in line with the increasing of computation power availability and artificial intelligence research intensity, especially the reinforcement learning field, nowadays plenty of researchers are working on a learning control approach for controlling systems. Due to that, in this paper, the adaptive PID controller based on Q-Learning (Q-PID) was used to control the ball position on the ball and beam system. From the simulation result, Q-PID outperforms the conventional PID and heuristic PID controller technique with the swifter settling time and lower overshoot percentage

Stable and robust fuzzy control for uncertain nonlinear systems

Author name used in this publication: F. H. F. LeungAuthor name used in this publication: P. K. S. Tam2000-2001 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Neurocontroller Alternatives for Fuzzy Ball-and-Beam Systems with Nonuniform Nonlinear Friction

The ball-and-beam problem is a benchmark for testing control algorithms. Zadeh proposed (1994) a twist to the problem, which, he suggested, would require a fuzzy logic controller. This experiment uses a beam, partially covered with a sticky substance, increasing the difficulty of predicting the ball\u27s motion. We complicated this problem even more by not using any information concerning the ball\u27s velocity. Although it is common to use the first differences of the ball\u27s consecutive positions as a measure of velocity and explicit input to the controller, we preferred to exploit recurrent neural networks, inputting only consecutive positions instead. We have used truncated backpropagation through time with the node-decoupled extended Kalman filter (NDEKF) algorithm to update the weights in the networks. Our best neurocontroller uses a form of approximate dynamic programming called an adaptive critic design. A hierarchy of such designs exists. Our system uses dual heuristic programming (DHP), an upper-level design. To our best knowledge, our results are the first use of DHP to control a physical system. It is also the first system we know of to respond to Zadeh\u27s challenge. We do not claim this neural network control algorithm is the best approach to this problem, nor do we claim it is better than a fuzzy controller. It is instead a contribution to the scientific dialogue about the boundary between the two overlapping disciplines

Invited Review: Recent developments in vibration control of building and bridge structures

This paper presents a state-of-the-art review of recent articles published on active, passive, semi-active and hybrid vibration control systems for structures under dynamic loadings primarily since 2013. Active control systems include active mass dampers, active tuned mass dampers, distributed mass dampers, and active tendon control. Passive systems include tuned mass dampers (TMD), particle TMD, tuned liquid particle damper, tuned liquid column damper (TLCD), eddy-current TMD, tuned mass generator, tuned-inerter dampers, magnetic negative stiffness device, resetting passive stiffness damper, re-entering shape memory alloy damper, viscous wall dampers, viscoelastic dampers, and friction dampers. Semi-active systems include tuned liquid damper with floating roof, resettable variable stiffness TMD, variable friction dampers, semi-active TMD, magnetorheological dampers, leverage-type stiffness controllable mass damper, semi-active friction tendon. Hybrid systems include shape memory alloys-liquid column damper, shape memory alloy-based damper, and TMD-high damping rubber

High dimentional neural fuzzy controller for nonlinear systems

De nos jours, la théorie de contrôle joue un rôle significatif dans presque tous les domaine de la science et de l'ingénierie. Les contrôleurs linéaires PID sont les applications principales de la théorie de contrôle, et ils se basent sur les systèmes de contrôle simples. Mais beaucoup de vrais systèmes possèdent des caractéristiques non-linéaires. Dans la pratique, il est nécessaire de faire beaucoup de linéarisations. Quand nous employons le contrôleur classique dans un système non-linéaire fortement complexe, les difficultés augmentent exponentiellement. Pour éviter les imperfections, on peut employer des contrôleurs flous. Le contrôleurs flous se basent sur le système de connaisance. Ce sont des outils importants dans le domaine de l'automatique. Ils possèdent beaucoup plus d'avantages que les contrôleurs classiques"PID", mais ils ont besoin d'experts pour concevoir les règles de base. La limite principale des contrôleurs flous est la difficulté d'établir les règles de base. Maintenant, beaucoup de recherches sont consacrées à la fusion des réseaux de neurones et de systèmes flous dans une nouvelle structure (les réseaux de neuro-floue). Cette approche combine les avantages de deux paradigmes puissants dans une capsule simple, et fournit un cadre puissant pour extraire des règles floues des données numériques. Cependant, cette technologie n'est pas parfaite. Il reste quelques difficultés: beaucoup de règles floues sont nécessaires, les algorithmes sont complexes et la fiabilité est basse (Par exemple, pour un même modèle ou fonction, les résultats dépendent des ensembles d'apprentissage). Pour éviter les difficultés, ce mémoire présente une nouvelle méthode, appelée"inférence neuro-floue de haute-dimension". L'idée fondamentale de cette méthode proposé est de considérer chaque donné dans ce système comme point avec la haute dimension. Chaque dimension d'entrée sera traitée en même temps dans les mêmes sous-ensembles de haute dimension. L'algorithme proposé a été examiné sur différentes applications, et les résultats ont été comparés aux données éditées sur trois problèmes de repère. Cet algorithme est simple à employer, et les résultats expérimentaux prouvent que le nombre de faisceaux exigés est inférieur à ceux rapportés dans la littérature. L'exactitude de rendement est bonne dans beaucoup d'applications

Design and implementation of Adaptive Neuro-Fuzzy Inference system for the control of an uncertain Ball on Beam Apparatus

Controlling an uncertain mechatronic system is challenging and crucial for its automation. In this regard, several control-strategies are developed to handle such systems. However, these control-strategies are complex to design, and require in-depth knowledge of the system and its dynamics. In this study, we are testing the performance of a rather simple control-strategy (Adaptive Neuro-Fuzzy Inference System) using an uncertain Ball and Beam System. The custom-designed apparatus utilizes image processing technique to acquire the position of the ball on the beam. Then, desired position is achieved by controlling the beam angle using Adaptive Neuro-Fuzzy and PID control. We are showing that adaptive neuro-fuzzy control can effectively handle the system uncertainties, which traditional controllers (i.e., PID) cannot handle

Comparative Study of Takagi-Sugeno-Kang and Madani Algorithms in Type-1 and Interval Type-2 Fuzzy Control for Self-Balancing Wheelchairs

This study examines the effectiveness of four different fuzzy logic controllers in self-balancing wheelchairs. The controllers under consideration are Type-1 Takagi-Sugeno-Kang (TSK) FLC, Interval Type-2 TSK FLC, Type-1 Mamdani FLC, and Interval Type-2 Mamdani FLC. A MATLAB-based simulation environment serves for the evaluation, focusing on key performance indicators like percentage overshoot, rise time, settling time, and displacement. Two testing methodologies were designed to simulate both ideal conditions and real-world hardware limitations. The simulations reveal distinct advantages for each controller type. For example, Type-1 TSK excels in minimizing overshoot but requires higher force. Interval Type-2 TSK shows the quickest settling times but needs the most force. Type-1 Mamdani has the fastest rise time with the lowest force requirement but experiences a higher percentage of overshoot. Interval Type-2 Mamdani offers balanced performance across all metrics. When a 2.7 N control input cap is imposed, Type-2 controllers prove notably more efficient in minimizing overshoot. These results offer valuable insights for future design and real-world application of self-balancing wheelchairs. Further studies are recommended for the empirical testing and refinement of these controllers, especially since the initial findings were limited to four-wheeled self-balancing robotic wheelchairs

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p