Control Of Inverted Pendulum System Research Articles

Fuzzy PID control based on genetic algorithm optimization inverted pendulum system

Abstract For the first-order inverted pendulum control system, a fuzzy PID control system based on the optimization of the genetic algorithm is proposed. The traditional genetic algorithm has the problem that the difference in the fuzzy subset parameter leads to a decrease in the interpretative ability of the fuzzy system. The main problem of the current genetic algorithm is the complexity of the computation and the low efficiency. Based on this problem, this paper proposes an improved genetic algorithm, i.e., it adopts the variance operator and adaptive change of the variance index and elite retention strategy, which solves the premature and local convergence problems of the standard genetic algorithm, in order to optimize the fuzzy system. The experimental results show that the optimized genetic algorithm gives full play to the advantages of fuzzy control in terms of interpretability and robustness, and at the same time guarantees the prediction accuracy, which provides a new research idea in the field of artificial intelligence control.

The Analytical Design and Implementation of Inverted Pendulum Control System using Linear Quadratic Regulator

The Analytical Design and Implementation of Inverted Pendulum Control System using Linear Quadratic Regulator

Robust Control of An Inverted Pendulum System Based on Policy Iteration in Reinforcement Learning

This paper is primarily focused on the robust control of an inverted pendulum system based on policy iteration in reinforcement learning. First, a mathematical model of the single inverted pendulum system is established through a force analysis of the pendulum and trolley. Second, based on the theory of robust optimal control, the robust control of the uncertain linear inverted pendulum system is transformed into an optimal control problem with an appropriate performance index. Moreover, for the uncertain linear and nonlinear systems, two reinforcement-learning control algorithms are proposed using the policy iteration method. Finally, two numerical examples are provided to validate the reinforcement learning algorithms for the robust control of the inverted pendulum systems.

Tikhonov-Tuned Sliding Neural Network Decoupling Control for an Inverted Pendulum

This paper introduces the concept of intelligent control using Tikhonov regularization for nonlinear coupled systems. This research is driven by the increasing demand for advanced control techniques and aims to explore the impact of Tikhonov regularization on these systems. The primary objective is to determine the optimal regularization term and its integration with other control methods to enhance intelligent control for nonlinear coupled systems. Tikhonov regularization is a technique employed to adjust neural network weights and prevent overfitting. Additionally, the incorporation of ReLU activation function in the neural network simplifies thearchitecture, avoiding issues like gradient explosion, and optimizes controller performance. Furthermore, sliding surfaces are designed to improve control system stability and robustness. The proposed Tikhonov-tuned sliding neural network (TSN) controller ensures both stability and superior system performance. The methodology emphasizes the importance of determining optimal neural network weights and regularization terms to prevent overfitting, facilitating accurate predictions in inverted pendulum control system applications. To assess the functionality and stability of TSN, this paper employs simulations and experimental implementations to control both the rotary inverted pendulum and the arm-driven inverted pendulum. The results indicate that the proposed TSN methodologies are effective and feasible.

Research on the Deep Deterministic Policy Algorithm Based on the First-Order Inverted Pendulum

With the mature development of artificial intelligence technology, the application of intelligent control algorithms in control systems has become a trend to meet the high-performance requirements of modern society. This paper proposes a deep deterministic policy gradient (DDPG) controller design method based on deep reinforcement learning to improve system control performance. Firstly, the optimal control policy of the DDPG algorithm is derived from the Markov decision process and the Actor–Critic algorithm. Secondly, in order to avoid local optima in traditional control systems, the capacity and the settlement method of the DDPG experience pool are adjusted to absorb positive experience to accelerate convergence and to complete efficient training. In response, and to solve the overestimation of the Q value in DDPG, the overall structure of the Critic network is changed to shorten the convergence period of DDPG at low learning rates. Finally, a first-order inverted pendulum control system was constructed in a simulation environment to verify the control effectiveness of PID, DDPG, and improved DDPG. The simulation results reveal that the improved DDPG controller has a faster response to disturbances, smaller displacement, and angular displacement of the first-order inverted pendulum. The simulation further proves that the improved DDPG algorithm has better stability and convergence and stronger anti-interference ability and stability recovery. This control method provides a certain reference for the application of reinforcement learning in traditional control systems.

Modeling and Optimization of Security Robot Gait Balance Based on the Principle of Inverted Pendulum

Robots have been increasingly applied to security protection. The modeling and optimal control of robot gait balance were discussed in this study based on a double inverted pendulum system. First, a trolley-type double inverted pendulum system was obtained by abstracting human gaits, a mathematical model corresponding to this system was constructed through the analytical dynamics method. Then a double inverted pendulum system controller was designed using the linear quadratic type control algorithm, a gain matrix K with the optimal performance was obtained by combining the experience piece-try method with regional dichotomy, and the effectiveness of modeling and optimization was demonstrated by comparing the typical value with optimized value in the end. This study is expected to provide a reference for enhancing the gait balance of security robots.

Performance Degradation Monitoring and Recovery of Vision-Based Control Systems

As an emerging need in vision-based control that is been intensively deployed in various applications, a systematic way of automatic performance recovery after system maintenance/control performance degradation is highly desired. On the one hand, vision-based control is an interdisciplinary field and the control performance of vision-based control systems is affected by not only uncertain environmental conditions but also the image processing latency, as well as the coordination of control and image processing algorithms. On the other hand, integration of image processing, control, and performance degradation recovery remains an open problem. In light of these observations, this brief deals with performance degradation monitoring and recovery with respect to the aforementioned factors. We first introduce a performance indicator by postfiltering the residual signal with the stable image representation of the feedback controller. It reveals a new interpretation of performance degradation recovery in the input- and output-recovery context. From this perspective, a real-time performance recovery approach is presented to enhance system performance over the lifetime. In the end, a vision-based inverted pendulum control system is implemented to verify the effectiveness of the proposed approach.

Electro Pneumatic Control System for Inverted Pendulum

Abstract The paper concerns the inverted pendulum control system with using pneumatic cylinder. A mathematical model of the pendulum used to derive the LQG controller was presented. Prepared laboratory stand was presented and described in detail. The main purpose of the work was experimental researches. A number of control process tests were conducted with variable model parameters such as additional mass, injected disturbances and so on. The results were shown on the time plots of the control object states.

Research on Visual Control System of Inverted Pendulum Based on Pixel Displacement

In inverted pendulum visual servo control system (IPVSCSs), how to achieve the fast and effective measurement of the state information of the inverted pendulum is an important task. In this paper, the characteristics of the inverted pendulum are considered and a series of image processing and positioning algorithm of the pendulum are proposed. Furthermore, the inverted pendulum control system model based on the pixel displacement is established and the LQR controller is designed. Finally, the feasibility and superiority of the proposed method are verified by real-time control experiments.

Design and Implementation of a Fuzzy Logic Controller for Inverted Pendulum System Based on Evolutionary Optimization Algorithms

The inverted pendulum is a standard classical problem in the branch of control and systems. If a cart is bushed by force then its position and angle of the pendulum will be changed. Several controllers may employed, keeping the pendulum arm upright by controlling at the cart location. In this search paper, the fuzzy-like PID (FPID) controller has been used to control the inverted pendulum, and the parameters of the controller are tuned with several evolutionary optimization algorithms like a genetic algorithm (GA), ant colony optimization (ACO), and social spider optimization (SSO.) The result of tuned FPID with evolutionary optimization is compared with conventional PID, and it shows that FPID with SSO has been given the best result.

Improved Switched System Approach to Networked Control Systems With Time-Varying Delays

An improved switched system approach is proposed for the stability and stabilization of networked control systems with time-varying delays. The approach features a mode-dependent state feedback controller guaranteeing the exponential stability of the closed-loop system, which is implemented within the packet-based control framework. The effectiveness of the proposed approach is finally demonstrated experimentally by a networked inverted pendulum control system.

An improved nonlinear proportional-integral-differential controller combined with fractional operator and symbolic adaptation algorithm

Parameter adjustment is usually applied for designing the proportional-integral-differential (PID) controllers. However, the ability to improve control performance by adjusting parameters is limited. Hence, with the goal to achieve ideal closed-loop response, this paper takes advantage of a structural optimization method for modifying the controller model. A symbolic adaptation algorithm for fractional order PID (FOPID) controller is employed to obtain precise nonlinear controller model. Firstly, a modeling comparison for nonlinear duffing system is carried out to highlight the efficiency of the symbolic adaptation algorithm. The case study indicates the proposed algorithm can establish compact dynamic models by amending the shortcomings of symbolic regression. Secondly, the proposed controller is restructured with the linear FOPID controller and its nonlinearity is increased by adjusting controllers’ components in symbolic form. The proposed controllers are simulated in an unstable second-order system, a time-delay system and a nonlinear VanderPol system. Compared with the IOPID and the FOPID controller, the symbolic adaptation algorithm improves the structural flexibility of these linear controllers. Meanwhile, the system response can better approximate the desired response and the structural integrity of the nonlinear controller model is guaranteed simultaneously. Finally, the nonlinear FOPD controllers for trajectory tracking experiments are carried out on a rotary inverted pendulum control system.

Simultaneous fault detection and antisaturated control based on dynamic observer for inverted pendulum control system

SummaryThis paper focuses on the problem of simultaneous fault detection and antisaturated control for an inverted pendulum control system. The system input is the acting control voltage that generates the force on the cart. However, the acting control voltage is obviously limited by the voltage amplitude. This means that the actuator of the constructed system model is saturated. Hence, the detector/controller unit produces two signals: the detection and antisaturated control. In this process, the effects of disturbances and noises on detection and control purposes need to be suppressed. Finally, two simulation examples verify the effectiveness of the method.

A FPGA-based state space controller

For many years digital controllers have been implemented using microprocessors, microcontrollers, digital signal processors etc. Recently, field programmable gate arrays (FPGA) is found to be an alternative because of its high processing speed. In this context, this paper presents the implementation of state space controller (SSC) on FPGA. SSC contains an observer to estimate the system unknown states variables. Matrix multiplications involved in this estimation take most of the computation time of the controller. In order to make SSC much more competent for controlling fast dynamic systems, a new FPGA based parallel architecture is proposed. The reduction in computation time with optimal hardware resources is achieved by developing an efficient multistage matrix multiplication algorithm (MMM). The performance of FPGA-based SSC is validated by implementing it in an inverted pendulum control system. The design is mapped on xilinx virtex-5 FPGA device with a maximum frequency of 449.438MHz.

A FPGA-based state space controller

For many years digital controllers have been implemented using microprocessors, microcontrollers, digital signal processors etc. Recently, field programmable gate arrays (FPGA) is found to be an alternative because of its high processing speed. In this context, this paper presents the implementation of state space controller (SSC) on FPGA. SSC contains an observer to estimate the system unknown states variables. Matrix multiplications involved in this estimation take most of the computation time of the controller. In order to make SSC much more competent for controlling fast dynamic systems, a new FPGA based parallel architecture is proposed. The reduction in computation time with optimal hardware resources is achieved by developing an efficient multistage matrix multiplication algorithm (MMM). The performance of FPGA-based SSC is validated by implementing it in an inverted pendulum control system. The design is mapped on xilinx virtex-5 FPGA device with a maximum frequency of 449.438MHz.

Novel Method of Friction Identification with Application for Inverted Pendulum Control System

Abstract This text covers a novel method of friction identification for control systems. The friction function in the inverted pendulum model is described by means of a cubic polynomial. The method has been tested using the data recorded on a real inverted pendulum. It has been proven that the proposed cubic model offers the same level of accuracy as the Coulomb model. However, all the difficulties caused by Coulomb’s model discontinuity are omitted.

Optimization of the Inverted Pendulum Controller with Friction Compensation by Means of the New Method of Lyapunov Exponents Estimation

Abstract This text covers optimization of an inverted pendulum control system with friction compensator. The control system is tuned with respect to a performance index based on the novel method of the Largest Lyapunov Exponent estimation. The detailed description of the method is provided. Model of the control object is presented. A simple controller is proposed. Two control systems are compared: the one with compensator and the one without. Parameters of both controllers are optimized with respect to the novel criterion by means of the Differential Evolution method. Results of numerical simulations are presented. It is shown that the new criterion can be successfully applied to both: typical linear regulators and controllers with compensators.

Friction Compensation in the Inverted Pendulum Controller by Means of a Neural Network

Abstract This paper presents an experimental confirmation of the novel method of friction modelling and compensation. The method has been applied to an inverted pendulum control system. The practical procedure of data acquisition and processing has been described. Training of the neural network friction model has been covered. Application of the obtained model has been presented. The main asset of the presented model is its correctness in a wide range of relative velocities. Moreover, the model is relatively easy to build.

Kinematic, kinetic, and electromyographic characteristics during quiet standing on a balance exercise assist robot.

[Purpose] The balance exercise assist robot is a training device based on a personal transport assistance robot ridden in the standing position. The personal transport assistance robot uses an inverted pendulum control system and moves in response to movements of the user’s center of gravity. The purpose of this study was to describe the characteristics of postural control during the action of stopping the personal transport assistance robot. [Participants and Methods] Eleven healthy male participants were required to maintain a standing position for 30 s; each task was performed 10 times. The measurement conditions were as follows: (1) on the floor; (2) on the robot, touching the handlebars; and (3) on the robot, not touching the handlebars. [Results] During the robotic tasks, the total locus lengths of the center of gravity and total joint momentums of the hip, knee, and ankle joints were larger, and the amount of displacement of the center of pressure was smaller than that during the floor task. Posture control on the robot was performed actively by mechanical interaction of the ankle, knee, and hip joints within a small base of support. [Conclusion] The balance exercise assist robot can be useful for postural control exercises because maintaining a standing position on the personal transport assistance robot required active postural control.

Optimal Control of an Inverted Pendulum Based on the New Method of Lyapunov Exponents Estimation

Abstract This text covers optimization of an inverted pendulum control system according to the new control performance assessment criterion based on the optimal control theory. The novel control performance index is founded on the method of the Largest Lyapunov Exponent estimation. The detailed description of the new method is provided. Model of the control object is presented. A simple controller is proposed. Parameters of the controller are optimized with respect to the novel criterion by means of the Differential Evolution method. Results of numerical simulations are presented. It is shown that the new criterion can be successfully applied when the regulation time is crucial, whereas somewhat larger overshoot is acceptable.