Research on Synchronous Control for Dual-Cylinder System with Unbalanced Loading

This paper is to develop an optimal electro-hydraulic dual-shaking table system with high waveform replication precision. The parameters of hydraulic cylinders, servo valves, hydraulic supply power and gravity balance system are designed and optimized in detail. To improve synchronization and tracking control precision, a hybrid control strategy is proposed. The cross-coupled control using a novel based on sliding mode control based on adaptive reaching law (ASMC), which can adaptively tune the parameters of sliding mode control (SMC), is proposed to reduce the synchronization error. To improve the tracking performance, the observer-based inverse control scheme combining the feed-forward inverse model controller and disturbance observer is proposed. The system model is identified applying the recursive least squares (RLS) algorithm and then the feed-forward inverse controller is designed based on zero phase error tracking controller (ZPETC) technique. To compensate disturbance and model errors, disturbance observer is used cooperating with the designed inverse controller. The combination of the novel ASMC cross-coupled controller and proposed observer-based inverse controller can improve the control precision noticeably. The dual-shaking table experiment system is built and various experiments are performed. The experimental results indicate that the developed system with the proposed hybrid control strategy is feasible and efficient and can reduce the tracking errors to 25% and synchronization error to 16% compared with traditional control schemes.

Variable pitch control on direct-driven PMSG for offshore wind turbine using Repetitive-TS fuzzy PID control

A well known method to control dynamic systems is the dynamic model inversion technique. Compensators obtained by this method may be used as feedforward controllers in the open-loop or closed-loop schemes. This technique has been widely used in the control of robotics arms. The method is conceptually simple but often requires a large amount of computation. In order to overcome this problem, a new method to design a fuzzy logic feedforward controller is presented in this paper. When designing a fuzzy logic controller, most of the time is spent in developing the fuzzy rules base to describe how the fuzzy controller should respond to the various inputs. Obtaining the rule base is usually a trial and error process. A starting set of rules are obtained and tested. The results are studied and the rules are then adjusted. The process is repeated until the desired results are achieved. An automated process would greatly simplify the design process of a fuzzy logic controller. In this paper, a two-level learning algorithm is used to obtain the inverse dynamics of a system. Changes to the learning algorithm that improves the performance of the existing algorithm are also presented. The learning algorithm allows the rules to be obtained by processing data collected from the system. The inverse dynamics are then implemented as a feedforward controller. To demonstrate the effectiveness of the learning algorithm, simulation results are presented.

This paper discusses the problem of suboptimal local piecewise H ∞ fuzzy control of quasi-linear spatiotemporal dynamic systems with control magnitude constraints. A Takagi-Sugeno fuzzy partial differential equation (PDE) model with space-varying coefficient matrices is first assumed to be derived for exactly describing nonlinear system dynamics. In the light of the fuzzy model, a local piecewise fuzzy feedback controller is then constructed to guarantee the exponential stability with a prescribed H ∞ disturbance attenuation level for the resulting closed-loop system, while the control constraints are also ensured. A sufficient condition on the existence of such fuzzy controller is developed by the Lyapunov direct method and an integral inequality and presented in terms of space algebraic linear matrix inequalities (LMIs) coupled with LMIs. By virtue of extreme value theorem, a suboptimal-constrained local piecewise H ∞ fuzzy control design in the sense of minimizing the disturbance attenuation level is formulated as a minimization optimization problem with LMI constraints. Finally, the proposed method is applied to solve the feedback control of a quasi-linear FitzHugh-Nagumo equation with space-varying coefficients, and simulation results show its effectiveness and merit.

In this paper, we consider generalized delay-dependent stability conditions of Takagi-Sugeno fuzzy systems with discrete and distributed time-delays. For fuzzy systems with discrete time-delays, both delay-independent stability conditions and delay-dependent stability conditions have already been obtained. However, no stability condition for fuzzy systems with distributed time-delays has appeared in the literature. This is also true in case of the robust stability for uncertain fuzzy systems with distributed time-delays. With the aid of the stability theorems of linear systems with distributed time-delays, we employ a similar Lyapunov functional for the stability of fuzzy time-delay systems. Then we introduce many free matrices to such a Lyapunov functional, in order to obtain delay-dependent stability conditions. These techniques lead to generalized and less conservative stability conditions. In fact, delay-dependent stability conditions thus obtained are given in linear matrix inequalities(LMIs) and guarantee a wide stability region. We also consider the robust stability of fuzzy time-delay systems with uncertain parameters. Applying the same techniques made on the stability conditions, we obtain delay-dependent sufficient conditions for the robust stability of uncertain fuzzy systems with discrete and distributed time-delays. Moreover, we consider the state feedback stabilization. Based on stability and robust stability conditions, we obtain conditions for the state feedback controller to stabilize the fuzzy time-delay systems. Finally, we take some examples to illustrate our results.

Ball handling and dribbling are basics soccer techniques. Therefore, handling and dribbling mechanism becomes important component for MSL (Middle Size League) robot soccer. The implementation of active handling and dribbling systems aims to enable the robot to perform dribbling mechanism when maneuvering, while in idle position use handling mechanism. Feedforward and feedback applied as control method in active ball handling and dribbling system. Feedforward control method will give a response to the dribbler wheel based on the motion of robot movement against x , y , and θ axes. Whereas, Feedback control method applied fuzzy inference system by reading the displacement of the arm angle from sensor databased on angle reference of dribbler arm to keep the ball in an ideal position. Implementation geometric calculation for feedforward control toward x , y , and θ axes movement of the robot that it has resulted RMSE value for left motor is 5.75 and for right motor is 7.01. Fuzzy inference system method is used as a feedback control to manipulate the speed and direction of wheel’s rotation as a result ball remains in the ideal position. The value of arm angle displacement and the combination of the two-dribbler arms are used as input for fuzzy inference system. The results of this research show that the application of the combined feedforward and feedback control methods in active handling and dribbling systems can provide robot-dribbling capabilities. Consequently, the robot has a good ability in handling and dribbling mechanism.

Interference-enhanced micro-vision-based single-shot imaging of five degrees-of-freedom error motions for ultra-precision rotary axes

The authors propose a reinforcement neural-network-based fuzzy logic control system (RNN-FLCS) for solving various reinforcement learning problems. RNN-FLCS is best applied to learning environments where obtaining exact training data is expensive. It is constructed by integrating two neural-network-based fuzzy logic controllers (NN-FLCs), each of which is a connectionist model with a feedforward multilayered network developed for the realization of a fuzzy logic controller. One NN-FLC functions as a fuzzy predictor and the other as a fuzzy controller. Using the temporal difference prediction method, the fuzzy predictor can predict the external reinforcement signal and provide a more informative internal reinforcement signal to the fuzzy controller. The fuzzy controller implements a stochastic exploratory algorithm to adapt itself according to the internal reinforcement signal. During the learning process, the RNN-FLCs can construct a fuzzy logic control system automatically and dynamically through a reward-penalty signal or through very simple fuzzy information feedback. Structure learning and parameter learning are performed simultaneously in the two NN-FLCs. Simulation results are presented. >

A hybrid control strategy is introduced that renders a compact set uniformly globally asymptotically stable for a continuous-time plant by switching between a Lyapunov-certified feedback controller and an uncertified controller. This control strategy allows for the opportunistic use of a controller that has desirable performance but lacks a Lyapunov certificate. A pair of tunable threshold functions determine conditions for switching between the controllers. To establish global uniform asymptotic stability, a nonsmooth Lyapunov function is constructed for the closed-loop hybrid system using an auxiliary memory variable and the Lyapunov certificate associated with the certified controller. Examples illustrate improvements to control effort and rate of convergence resulting from the proposed hybrid control strategy when applied to state-feedback and model-predictive control.

Efficient fuzzy logic load–frequency controller

In order to study the handling stability of four-wheel steering vehicle, the vehicle is simplified into a linear two-degree-of-freedom model. The fuzzy control technology is used to study the proportional feedforward + fuzzy feedback control strategy of four-wheel steering vehicle, and the fuzzy control toolbox in Matlab/Simulink is used to establish the four-wheel steering control system. The simulation results show that the fuzzy control can effectively improve the response of rear wheel steering, improve the maneuverability of the vehicle at low speed, reduce the turning radius, and improve the handling stability of the vehicle at high speed.

This paper investigates the observer-based H∞ fuzzy control problem for a class of discrete-time fuzzy mixed delay systems with random communication packet losses and multiplicative noises, where the mixed delays comprise both discrete time-varying and distributed delays. The random packet losses are described by a Bernoulli distributed white sequence that obeys a conditional probability distribution, and the multiplicative disturbances are in the form of a scalar Gaussian white noise with unit variance. In the presence of mixed delays, random packet losses and multiplicative noises, sufficient conditions for the existence of an observer-based fuzzy feedback controller are derived, such that the closed-loop control system is asymptotically mean-square stable and preserves a guaranteed H∞ performance. Then a linear matrix inequality approach for designing such an observer-based H∞ fuzzy controller is presented. Finally, a numerical example is provided to illustrate the effectiveness of the developed theoretical results.

In this study, we will present the novel application of Type-2 (T2) fuzzy control into the popular video game called flappy bird. To the best of our knowledge, our work is the first deployment of the T2 fuzzy control into the computer games research area. We will propose a novel T2 fuzzified flappy bird control system that transforms the obstacle avoidance problem of the game logic into the reference tracking control problem. The presented T2 fuzzy control structure is composed of two important blocks which are the reference generator and Single Input Interval T2 Fuzzy Logic Controller (SIT2-FLC). The reference generator is the mechanism which uses the bird's position and the pipes' positions to generate an appropriate reference signal to be tracked. Thus, a conventional fuzzy feedback control system can be defined. The generated reference signal is tracked via the presented SIT2-FLC that can be easily tuned while also provides a certain degree of robustness to system. We will investigate the performance of the proposed T2 fuzzified flappy bird control system by providing comparative simulation results and also experimental results performed in the game environment. It will be shown that the proposed T2 fuzzified flappy bird control system results with a satisfactory performance both in the framework of fuzzy control and computer games. We believe that this first attempt of the employment of T2-FLCs in games will be an important step for a wider deployment of T2-FLCs in the research area of computer games.

Using VSC-MTDC for wind farm integration is considered to be a promising solution. In order to provide a stable ac voltage control and avoid active power control conflicts, the amplitude-phase control strategy, rather than the active/reactive power decoupled control strategy, is generally applied for the VSC connected to wind farms. However, using such a control strategy, the flexibility of power flow control will be partly lost. As the positive and negative pole dc networks in the bipolar VSC-MTDC can operate independently, a novel hybrid control strategy is proposed in this paper. In the proposed hybrid control strategy, the amplitude-phase control and active/reactive power decoupled control can be applied to the positive and negative poles, respectively. Hence, the hybrid strategy combines the advantages of the amplitude-phase control and active/reactive power decoupled control and can be adapted to bipolar VSC-MTDC for large-scale wind farm integration. Simulation results using PSCAD show that the hybrid control strategy can provide the stable ac voltage control and active/reactive power flow control for the integration of wind farms; moreover, such a hybrid control strategy is helpful to avoid the overload of dc lines, stabilize the dc voltage, and enhance the power transfer capability.

This book presents a systematic framework targeting at fuzzy modeling and fuzzy control of nonlinear systems with uncertainties. The book is organized into three major parts incorporating 13 chapters. The first part contains four chapters focusing on the modeling of nonlinear dynamical systems by using fuzzy logic. The second part includes five chapters in which fuzzy inference and control techniques are addressed. The final part consists of four chapters, which covers several advanced topics in fuzzy control ranging from controller and filter design to chaotification of fuzzy systems and feedforward fuzzy control of nonlinear systems via Fourier integrals. One of the major contributions of this book is that it presents the latest original research developments in the field. It provides rich examples and references, too. It is a valuable resource for those researchers and practitioners interested in expanding their knowledge from fuzzy logic and applications to nonlinear dynamical systems.