Swinging up and stabilization of a real inverted pendulum (original) (raw)
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Fuzzy Control of a Real Time Inverted Pendulum System
In this study, a real-time control of the cart-pole inverted pendulum system was developed using fuzzy logic controller. Swing-up and stabilization of the inverted pendulum were implemented directly in fuzzy logic controller. The fuzzy logic controller designed in the Matlab-Simulink environment was embedded in a dSPACE DS1103 DSP controller board. Swing-up algorithm brings the pendulum near to its inverted position in 10 seconds from downward position. In order to test the robustness of the fuzzy logic controller internal (changing model parameters) and external disturbances (applying external forces) were applied on the inverted pendulum. The inverted pendulum system was shown to be robust to the external and internal disturbances. The maximum errors of the pendulum angle to the impulse input were between 1.89˚ and 4.6449˚ in the robustness tests.
Electronics and Electrical Engineering, 2014
This paper presents the design and practical implementation of a hybrid fuzzy logic and adaptive linearquadratic controller (LQR) for a real inverted short pendulum system. We present an extended swing-up approach using fuzzy controller and then discuss an adaptive LQR realization which takes into account nonlinearities while passing the transient process to the upward position of the short pendulum which is mounted on a cart. So long as the cart's configuration space is restricted by boundary conditions the controller also solves the positioning task, during which the cart returns to the centre of cart's configuration space. We also discuss the practical realization of such controller logic, embedded into 32-bit microcontroller with the algorithm reaction of 1 ms.
Stabilization fuzzy control of inverted pendulum systems
A new fuzzy controller for stabilization control of inverted pendulum systems is presented based on the Single Input Rule Modules (SIRMs) dynamically connected fuzzy inference model. The fuzzy controller has four input items, each with a SIRM and a dynamic importance degree. The SIRMs and the dynamic importance degrees are designed such that pendulum angular control has priority over cart position control. It is made clear that the fuzzy controller performs the pendulum angular control and the cart position control in parallel, and switching between the two controls is realized by automatically tuning the dynamic importance degrees according to control situations. The simulation results show that the proposed fuzzy controller has a high generalization ability to stabilize completely a wide range of the inverted pendulum systems within 9.0 s for an initial angle up to 30.0.
Intelligent Control of Single Inverted Pendulum
IFAC Proceedings Volumes, 1997
The paper deals with neuro-fuzzy tools for non-linear system control. Inverted pendulum was chosen as a typical non-linear model for control. The control task was defined as lifting inverted pendulum from upside down position into standing position by a cart movement and keep this position of the pendulum stable. Two different controllers were observed, ANFIS (Adaptive Neural Inference System) and NARA (Neural Approximate Reasoning Approach). Finally a simple neuro-controller based on feed-forward neural network with classical back-propagation adaptation was studied. Results show that ANFIS seems to be a powerful tool for this kind of task. In the paper an extensive report about experiments with pendulum control is presented.
THE STABILIZATION OF FORCED INVERTED PENDULUM VIA FUZZY CONTROLLER
In the field of nonlinear control engineering, the inverted pendulum can be considered as a bench mark problem. For an inverted pendulum, there are mainly two types of equilibrium which are categorized as stable equilibrium and unstable equilibrium. The stable equilibrium is the one in which the pendulum is in normal pendent position and not requires any control force since because it is naturally stable. Under the influence of an external force, the stable equilibrium loses its stability and there comes the need of a stabilizing controller. Therefore unstable equilibrium refers to the pendulum in upright position strictly under the influence of a stabilizing controller. The inverted pendulum is strictly nonlinear, under actuated system; challenging task comes with the stability analysis. A forced inverted pendulum is considered which has been modeled with respect to the cart motion. To improve the performance and stabilize the system, a fuzzy controller is designed for the respective system. Simulation results validate the fact that the stabilization is achieved through out and the perfect result is obtained for the system.
IEEE Access
This paper investigates the efficacy of an optimized fuzzy logic controller for real-time swing-up control and stabilization to a rigidly coupled twin-arm inverted pendulum system. The proposed fuzzy controller utilizes Lyapunov criteria for controller design to ensure system stability. The membership functions are further optimized based on the entropy function. The controller design is based on the black-box approach, eliminating the need for an accurate mathematical model of the system. The experimental results shows an improvement in the transient and steady-state response of the controlled system as compared to other state-of-the-art controllers. The proposed controller exhibits a small settling time of 4.0 s and reaches the stable swing-up position within 5 oscillations. Various error indices are evaluated that validates an overall improvement in the performance of the system.
Design of a Fuzzy Logic Controller for the Double Pendulum Inverted on a Cart
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The double-inverted pendulum (DIP) constitutes a classical problem in mechanics, whereas the control methods for stabilizing around the equilibrium positions represent the classic standards of control system theory and various control methods in robotics. For instance, it functions as a typical model for the calculation and stability of walking robots. The present study depicts the controlling of a double-inverted pendulum (DIP) on a cart using a fuzzy logic controller (FLC). A linear-quadratic controller (LQR) was used as a benchmark to assess the effectiveness of our method, and the results showed that the proposed FLC can perform significantly better than the LQR under a variety of initial system conditions. This performance is considered very important when the reduction of the peak system output is concerned. The proposed controller equilibration and velocity tracking performance were explored through simulation, and the results obtained point to the validity of the control met...
Applied Sciences, 2020
In this paper an adaptive fuzzy controller is proposed to solve the trajectory tracking problem of the inverted pendulum on a cart system. The designed algorithm is featured by not using any knowledge of the dynamic model and incorporating a full-state feedback. The stability of the closed-loop system is proven via the Lyapunov theory, and boundedness of the solutions is guaranteed. The proposed controller is heuristically tuned and its performance is tested via simulation and real-time experimentation. For this reason, a tuning method is investigated via evolutionary algorithms: particle swarm optimization, firefly algorithm and differential evolution in order to optimize the performance and verify which technique produces better results. First, a model-based simulation is carried out to improve the parameter tuning of the fuzzy systems, and then the results are transferred to real-time experiments. The optimization procedure is presented as well as the experimental results, which ...
Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System
International Journal of Electrical and Computer Engineering (IJECE), 2018
The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The non-linear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulation and real experiment. The ANFIS controller could reach its desired new destination in 1.5 s and could stabilize the entire system in 2.2 s in the simulation, while in the experiment it took 1.7 s to reach stability. Results from the simulation and experiment showed that ANFIS had better performance compared to the Sugeno-fuzzy controller as it provided faster and smoother response and much less steady-state error. Keyword: Adaptive neuro-fuzzy inference system (ANFIS) Intelligent control Inverted pendulum (IP) Linear quadratic regulator (LQR) Sugeno FIS
Hybrid Fuzzy Control of Nonlinear Inverted Pendulum System
ArXiv, 2019
Complexity and nonlinear behaviours of inverted pendulum system make its control design a very challenging task. In this paper, a hybrid fuzzy adaptive control system using model reference approach is designed for inverted-pendulum system control. The proposed method is developed to achieve position control and later simultaneous control of position and pendulum angle in the same control loop. Also, the control algorithm is applied to achieve control objective of reference tracking, disturbance rejection and robustness to parameter variation. The performance of the proposed control scheme was compared with conventional PID and LQR controllers. The simulation results showed that the proposed control scheme provides high-performance dynamic characteristics and is robust with regard to parametric variations, disturbance and reference tracking compared to the comparatives