Volume 10, Number 3, 2013
Quantized fault detection for sensor/actuator faults of networked control systems (NCSs) with time delays both in the sensor-to-controller channel and controller-to-actuator channel is concerned in this paper. A fault model is set up based on the possible cases of sensor/actuator faults. Then, the model predictive control is used to compensate the time delay. When the sensors and actuators are healthy, an H stability criterion of the state predictive observer is obtained in terms of linear matrix inequality. A new threshold computational method that conforms to the actual situation is proposed. Then, the thresholds of the false alarm rate (FAR) and miss detection rate (MDR) are presented by using our proposed method, which are also compared with the ones given in the existing literatures. Finally, some numerical simulations are shown to demonstrate the effectiveness of the proposed method.
The position control system of an electro-hydraulic actuator system (EHAS) is investigated in this paper. The EHAS is developed by taking into consideration the nonlinearities of the system: the friction and the internal leakage. A variable load that simulates a realistic load in robotic excavator is taken as the trajectory reference. A method of control strategy that is implemented by employing a fuzzy logic controller (FLC) whose parameters are optimized using particle swarm optimization (PSO) is proposed. The scaling factors of the fuzzy inference system are tuned to obtain the optimal values which yield the best system performance. The simulation results show that the FLC is able to track the trajectory reference accurately for a range of values of orifice opening. Beyond that range, the orifice opening may introduce chattering, which the FLC alone is not sufficient to overcome. The PSO optimized FLC can reduce the chattering significantly. This result justifies the implementation of the proposed method in position control of EHAS.
In this paper, adaptive neural tracking control is proposed based on radial basis function neural networks (RBFNNs) for a class of multi-input multi-output (MIMO) nonlinear systems with completely unknown control directions, unknown dynamic disturbances, unmodeled dynamics, and uncertainties with time-varying delay. Using the Nussbaum function properties, the unknown control directions are dealt with. By constructing appropriate Lyapunov-Krasovskii functionals, the unknown upper bound functions of the time-varying delay uncertainties are compensated. The proposed control scheme does not need to calculate the integral of the delayed state functions. Using Young's inequality and RBFNNs, the assumption of unmodeled dynamics is relaxed. By theoretical analysis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded.
In this work, an adaptive control constraint system has been developed for computer numerical control (CNC) turning based on the feedback control and adaptive control/self-tuning control. In an adaptive controlled system, the signals from the online measurement have to be processed and fed back to the machine tool controller to adjust the cutting parameters so that the machining can be stopped once a certain threshold is crossed. The main focus of the present work is to develop a reliable adaptive control system, and the objective of the control system is to control the cutting parameters and maintain the displacement and tool flank wear under constraint valves for a particular workpiece and tool combination as per ISO standard. Using Matlab Simulink, the digital adaption of the cutting parameters for experiment has confirmed the efficiency of the adaptively controlled condition monitoring system, which is reflected in different machining processes at varying machining conditions. This work describes the state of the art of the adaptive control constraint (ACC) machining systems for turning. AISI4140 steel of 150 BHN hardness is used as the workpiece material, and carbide inserts are used as cutting tool material throughout the experiment. With the developed approach, it is possible to predict the tool condition pretty accurately, if the feed and surface roughness are measured at identical conditions. As part of the present research work, the relationship between displacement due to vibration, cutting force, flank wear, and surface roughness has been examined.
The mathematical model of a high-speed underwater vehicle getting catastrophe in the out-of-water course and a nonlinear sliding mode control with the adaptive backstepping approach for the catastrophic course are proposed. The speed change is large at the moment that the high-speed underwater vehicle launches out of the water to attack an air target. It causes motion parameter uncertainties and affects the precision attack ability. The trajectory angle dynamic characteristic is based on the description of the transformed state-coordinates, the nonlinear sliding mode control is designed to track a linear reference model. Furthermore, the adaptive backstepping control approach is utilized to improve the robustness against the unknown parameter uncertainties. With the proposed control of attitude tracking, the controlled navigational control system possesses the advantages of good transient performance and robustness to parametric uncertainties. These can be predicted and regulated through the design of a linear reference model that has the desired dynamic behavior for the trajectory of the high-speed underwater vehicle to attack its target. Finally, some digital simulation results show that the control system can be applied to a catastrophic course, and that it illustrates great robustness against system parameter uncertainties and external disturbances.
The conventional discrete wavelet transform (DWT) introduces artifacts during denoising of images containing smooth curves. Finite ridgelet transform (FRIT) solved this problem by mapping the curves in terms of small curved ridges. However, blind application of FRIT all over an image is computationally heavy. Finite curvelet transform (FCT) selectively applies FRIT only to the tiles containing small portions of a curve. In this work, a novel curvelet transform named as 4-quadrant finite curvelet transform (4QFCT) based on a new concept of 4-quadrant finite ridgelet transform (4QFRIT) has been proposed. An image is band pass filtered and the high frequency bands are divided into small non-overlapping square tiles. The 4QFRIT is applied to the tiles containing at least one curve element. Unlike FRIT, the 4QFRIT takes 4 sets of radon projections in all the 4 quadrants and then averages them in time and frequency domains after denoising. The proposed algorithm is extensively tested and benchmarked for denoising of images with Gaussian noise using mean squared error (MSE) and peak signal to noise ratio (PSNR). The results confirm that 4QFCT yields consistently better denoising performance quantitatively and visually.
Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a setup which results in expensive black-box optimization problems. Such problems introduce unique challenges, which has motivated the application of metamodel-assisted computational intelligence algorithms to solve them. Such algorithms combine a computational intelligence optimizer which employs a population of candidate solutions, with a metamodel which is a computationally cheaper approximation of the expensive computer simulation. However, although a variety of metamodels and optimizers have been proposed, the optimal types to employ are problem dependant. Therefore, a priori prescribing the type of metamodel and optimizer to be used may degrade its effectiveness. Leveraging on this issue, this study proposes a new computational intelligence algorithm which autonomously adapts the type of the metamodel and optimizer during the search by selecting the most suitable types out of a family of candidates at each stage. Performance analysis using a set of test functions demonstrates the effectiveness of the proposed algorithm, and highlights the merit of the proposed adaptation approach.
Type-1 fuzzy sets cannot fully handle the uncertainties. To overcome the problem, type-2 fuzzy sets have been proposed. The novelty of this paper is using interval type-2 fuzzy logic controller (IT2FLC) to control a flexible-joint robot with voltage control strategy. In order to take into account the whole robotic system including the dynamics of actuators and the robot manipulator, the voltages of motors are used as inputs of the system. To highlight the capabilities of the control system, a flexible joint robot which is highly nonlinear, heavily coupled and uncertain is used. In addition, to improve the control performance, the parameters of the primary membership functions of IT2FLC are optimized using particle swarm optimization (PSO). A comparative study between the proposed IT2FLC and type-1 fuzzy logic controller (T1FLC) is presented to better assess their respective performance in presence of external disturbance and unmodelled dynamics. Stability analysis is presented and the effectiveness of the proposed control approach is demonstrated by simulations using a two-link flexible-joint robot driven by permanent magnet direct current motors. Simulation results show the superiority of the IT2FLC over the T1FLC in terms of accuracy, robustness and interpretability.
In this paper, the projective lag synchronization of a new hyperchaotic system with certain/uncertain parameters is addressed. Based on Lyapunov stability theory, a generic and simple controller is designed for the projective lag synchronization. Furthermore, with LaSalle's invariance principle, an adaptive method is proposed to identify the unknown parameters of the new hyperchaotic system based on the projective lag synchronization. Finally, numerical simulations are given to support the analytical approach.
In this paper, through constructing some novel Lyapunov-Krasovskii functional (LKF) terms and using some effective techniques, two sufficient conditions are derived to guarantee a class of discrete-time time-delay systems with distributed delay to be asymptotically and robustly stable, in which the linear fractional uncertainties are involved and the information on the time-delays is fully utilized. By employing the improved reciprocal convex technique, some important terms can be reconsidered when estimating the time difference of LKF, and the criteria can be presented in terms of linear matrix inequalities (LMIs). Especially, these derived conditions heavily depend on the information of time-delay of addressed systems. Finally, three numerical examples demonstrate that our methods can reduce the conservatism more efficiently than some existing ones.