A Novel Hyperchaotic Financial System with Sinusoidal Hyperbolic Nonlinearity: From Theoretical Analysis to Adaptive Neural Fuzzy Controller Method

It is essential to implement effective compliance control measures to ensure the safe and reliable operation of robots and automated systems. In this paper, zone barrier Lyapunov function is presented to the practical nonlinear system with adaptive fuzzy neural network control design. Output-constraint is considered by zone barrier Lyapunov function (zBLF), which is designed to enforce safety constraints and offer flexibility when the system states remain within a safe region of operation, even in the presence of nonlinear uncertainties. Adaptive fuzzy neural control is adopted to approximate the nonlinear uncertainties for practical stability analysis and design, which aims to adjust the control parameters online according to the system's behaviour. The fuzzy control based on zBLF symmetric output constraint is further extended to the asymmetric case. The output state converges to the trajectory and moves freely in a region around the origin, and then it is demonstrated the developed design method guarantees the boundedness of the states of a closed-loop system. Moreover, the system's residual set for each condition can be identified respectively. The simulation results on euler-lagrange system are to validate the effectiveness of the proposed scheme.

In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command‐filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command‐filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed‐loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.

A new type of adaptive neural network fuzzy controller based on the stability for the double inverted pendulum control problem is introduced. The method uses a fusion function to reduce the dimension of the system, reducing the number of input variables to solve the fuzzy rule explosion problem. In order to optimize and amend the front-part and later-part parameter of Takagi-Sugeno fuzzy model, a mixed algorithm of backward propagation (BP) and least square method (LSE) algorithm are used. Using the collected original input and output data to establish adaptive neural network fuzzy inference system (ANFIS), and to control the double inverted pendulum system. Simulation results show that the controller is better than the other controller.

This chapter presents a robust Adaptive Fuzzy Neural Controller (AFN C) suitable for identification and control of uncertain Multi-Input-Multi-Output (MIMO) nonlinear systems. The proposed controller has the following salient features: (1) Self-organizing fuzzy neural structure, i.e. fuzzy control rules can be generated or deleted automatically; (2) Online learning ability of uncertain MIMO nonlinear systems; (3) Fast learning speed; (4) Fast convergence of tracking errors; (5) Adaptive control, where structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; (6) Robust control, where global stability of the system is established using the Lyapunov approach. Two simulation examples are used to demonstrate excellent performance of the proposed controller.

This paper presents a robust adaptive fuzzy neural controller (AFNC) suitable for motion control of multilink robot manipulators. The proposed controller has the following salient features: (1) self-organizing fuzzy neural structure, i.e., fuzzy control rules can be generated or deleted automatically according to their significance to the control system and the complexity of the mapped system and no predefined fuzzy rules are required; (2) fast online learning ability, i.e., no prescribed training models are needed for online learning and weights of the fuzzy neural controller are modified without any iterations; (3) fast convergence of tracking errors, i.e., manipulator joints can track the desired trajectories very quickly; (4) adaptive control, i.e., structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; and (5) robust control, where asymptotic stability of the control system is established using the Lyapunov theorem. Experimental evaluation conducted on an industrial selectively compliant assembly robot arm demonstrates that excellent tracking performance can be achieved under time-varying conditions.

Radial basis function (RBF) network can be viewed as a fuzzy rule base with specified membership functions and fuzzy inference operations. However, there is a trade-off between the approximation performance of RBF network and the number of hidden neurons. To tackle this problem, this paper proposes a dynamic RBF (DRBF) network with a constructive learning. This DRBF network not only can create the new hidden neurons, but also can prune the insignificant hidden neurons. Then, an adaptive dynamic RBF fuzzy neural control (ADRFNC) system, including a neural controller and a saturation compensator, is developed. The neural controller uses a DRBF network to on-line mimic an ideal controller and the saturation compensator is designed to dispel the approximation error introduced by the neural controller. Finally, the proposed ADRFNC system is applied to a chaotic circuit and a DC motor control system. Simulation and experimental results show the proposed ADRFNC system can achieve a favorable tracking performance when the controller’s parameters have been learned and the network structure has been constructed by the proposed learning algorithm.

ENHANCING THE FLIGHT SIMULATOR FEELING BY A MINIMISING BACKLASH-EFFECTS PROCESS

Renewable energy systems, exemplified by solar and wind power, are increasingly integrated into modern power grids to mitigate environmental impact and reduce reliance on fossil fuels. However, the inherent intermittency and unpredictability of renewable energy sources pose challenges to grid stability and reliability. Energy Storage Systems (ESS) offer a promising solution to address these challenges by smoothing out power fluctuations and ensuring a consistent power supply. Among various ESS technologies, Flywheel Energy Storage Systems (FESS) have emerged as a noteworthy contender due to their rapid response times, low operating costs, and extended lifespan. This paper focuses on investigating the operation of a novel unit comprising a solar power system integrated with a Flywheel Energy Storage System (PV-FESS). The aim is to develop an effective control algorithm utilizing adaptive fuzzy neural networks and model predictive control (ANFIS-MPC) to manage power fluctuations stemming from renewable energy sources within the grid. The proposed control strategy aims to optimize the operation of the PV-FESS system by dynamically adjusting the energy absorption or release of the flywheel to maintain grid stability. Simulation studies conducted using Matlab-Simulink/Simcape software validate the efficacy of the ANFIS-MPC algorithm in mitigating abnormal fluctuations from renewable energy sources. The results demonstrate that the PV-FESS system effectively balances power fluctuations, ensuring a stable and reliable power output to the grid.

In this paper, we study the design of iterative learning controllers for nonaffine nonlinear interconnected systems with repeatable control tasks. The interaction between each subsystem can be a general type of unknown nonlinear function if a bounding condition is satisfied. An error model is derived such that only local subsystem information is required for the controller design. An adaptive iterative learning controller for each subsystem is constructed based on a fuzzy neural learning component and a robust learning component. The fuzzy neural learning component designed by an output recurrent fuzzy neural network is utilized as an approximator to approximate the system nonaffine nonlinearities and interconnections. The approximation error due to the fuzzy neural learning component will be then compensated by a robust learning component. Stable adaptive laws are derived to update the control parameters in order to guarantee the stability and convergence. We show that the internal signals are bounded during the learning process and the state tracking errors of each subsystem converge asymptotically along the iteration axis to a tunable residual set.Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

A robust Sugeno type adaptive fuzzy neural network (RSAFNN) backstepping control for a two-axis motion control system is proposed in this paper. The adopted two-axis motion control system is composed of two permanent magnet linear synchronous motors (PMLSMs). The single-axis motion dynamics with the introduction of a lumped uncertainty, which includes parameter variations, external disturbances, cross coupled interference between the two PMLSMs and fiction force, is derived first. Then, a backstepping control approach is proposed to compensate the lumped uncertainty occurred in the two-axis motion control system. Moreover, to improve the control performance in the tracking of the reference contours, a RSAFNN backstepping control is proposed where a Sugeno type adaptive fuzzy neural networks (SAFNN) is employed to estimate the lumped uncertainty directly. Furthermore, the proposed control algorithms are implemented in a TMS320C32 DSP-based control computer.

To maintain the vibrations of the gyroscope proof mass, an adaptive super-twisting sliding mode control (STSMC) of a micro gyroscope based on a two-loop recursive fuzzy neural network (TLRFNN) is designed. In order to estimate the unknown parameters of the nonlinear system online, an adaptive technology is adopted based on the parameter linearization of the nonlinear model. In response to the problem of system chattering caused by ordinary sliding mode control, the STSMC with the advantages of high order sliding mode and conventional sliding mode is introduced. Aiming at the problem of uncertainty in system parameters, a two-loop recursive fuzzy neural network approximation with the competence over the storage of a priori information is utilized. All adaptive laws are obtained under the Lyapunov stability framework which contributes to the stability of the system. The simulation research shows the system exhibits good tracking performance and maintains a small tracking error under the control of the suggested control system. The effect is measured by calculating the root mean square error (RMSE) parameter of the tracking error and the proposed control system achieves a tracking effect of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{1}}{\ ext{0.8590}} \ imes {\ ext{1}}{{\ ext{0}}^{ - 4}}$$\\end{document} and \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{2}}{\ ext{0.0831}} \ imes {\ ext{1}}{{\ ext{0}}^{ - 4}}$$\\end{document} in the x and y directions, respectively.

The design of robust controllers to handle noise and system variation is a challenging problem and research in this area continues; one approach which has gained success is to employ a neural network to learn about the unknown plant and fuzzy inference to compensate for the uncertainty (GANFIS control). As with most controllers, the design of GANFIS controllers requires proper parameter selection as well as tuning due to variations in the plant or environment. Evolutionary learning has been shown to be feasible to tune these parameters, but there may be some difficulty in selecting the learning factors. Previously, we addressed this problem by designing a fitness function containing both prediction and heuristics for measuring system performance and extending the approach by integrating simulated annealing features for faster convergence. In this paper, we employ a tuning strategy based upon the Hamming distance to select the key convergence parameters of the evolutionary learning algorithm: the cross-over and mutation rates. To study the approach, we compare the case when simulated annealing is applied for constant values; then an adaptive tuned version of the simulated annealing algorithms is applied. Results show that by tuning, the GANFIS controllers produce an overall tracking performance with a lower error trajectory, thus illustrating the feasibility of this additional feature of the GANFIS controller.

Fuzzy wavelet neural adaptive finite-time self-triggered fault-tolerant control for a quadrotor unmanned aerial vehicle with scheduled performance

This paper aims to investigate the formation control of multi-robot systems, where the first-order kinematic model of a differential wheeled robot is considered. Based on the graph theory and consensus algorithm, an adaptive neural fuzzy formation controller is designed with the capability of on-line learning. The learning rules of controller parameters can be derived from the analyzing of Lyapunov stability. Simulations are adopted to verify the feasibility of proposed techniques. From simulation results, the proposed adaptive neural fuzzy controller can provide better formation responses compared to conventional consensus algorithm.

Hydraulic system is widely used in industry where large actuation forces are needed such as electro-hydraulic positioning system, active suspension control, and so on. One of the main problem in hydraulic system is the nontriviality, for instance, the deadbands in valve operation, frictions of links, saturations, pressure dynamics, etc. both system unknown nonlinearities and external disturbances should be considered in controller design. For underwater hydraulic manipulator, because of the environment of sea and the large nonlinearities of the hydraulic manipulator system, both system unknown nonlinearities and external disturbances should be considered in controller design. A lot of control methods have been proposed to solve the problem properly, such as sliding mode control, adaptive control, fuzzy adaptive control, adaptive robust control, ect. However, due to limited knowledge about hydraulic manipulator system, it is hard to precisely estimate the nonlinearities for a possible higher performance. In this paper, a neural network adaptive robust control method is proposed to help control an underwater hydraulic manipulator. In order to achieve better performance using adaptive robust control strategy, unknown nonlinearities in the system are approximated by the outputs of radial basis function neural network (RBFNNs). A projection law has been used to bound the NN weights when they are tuning on-line to avoid possible adaptive divergence, and the tracking transient performance is guaranteed by robust control law. Simulation results illustrate that manipulator has a good tracking performance.