The problem of acoustic noise is becoming increasingly serious with the growing use of industrial and medical equipment, appliances, and consumer electronics. Active noise control (ANC), based on the principle of superposition, was developed in the early 20th century to help reduce noise. However, ANC is still not widely used owing to the effectiveness of control algorithms, and to the physical and economical constraints of practical applications. In this paper, we briefly introduce some fundamental ANC algorithms and theoretical analyses, and focus on recent advances on signal processing algorithms, implementation techniques, challenges for innovative applications, and open issues for further research and development of ANC systems.

/pdf/recent-advances-on-active-noise-control-open-issues-and-1wg2ydx57i.pdf

Recent Advances on Active Noise Control: Open Issues and Innovative Applications

Control strategies for crane systems: A comprehensive review

Digital image libraries and other multimedia databases have been dramatically expanded in recent years. In order to effectively and precisely retrieve the desired images from a large image database, the development of a content-based image retrieval (CBIR) system has become an important research issue. However, most of the proposed approaches emphasize on finding the best representation for different image features. Furthermore, very few of the representative works well consider the user's subjectivity and preferences in the retrieval process. In this paper, a user-oriented mechanism for CBIR method based on an interactive genetic algorithm (IGA) is proposed. Color attributes like the mean value, the standard deviation, and the image bitmap of a color image are used as the features for retrieval. In addition, the entropy based on the gray level co-occurrence matrix and the edge histogram of an image are also considered as the texture features. Furthermore, to reduce the gap between the retrieval results and the users' expectation, the IGA is employed to help the users identify the images that are most satisfied to the users' need. Experimental results and comparisons demonstrate the feasibility of the proposed approach.

A User-Oriented Image Retrieval System Based on Interactive Genetic Algorithm

When modeling cranes, the hook and the suspended cargo are usually regarded roughly as one mass point for simplicity, ie, the cargo swing is modeled as that of a single pendulum However, in practice, when the hook mass is nonnegligible or the cargo has a large size, the crane always exhibits double-pendulum swing dynamics, which is much more complicated and makes most existing control methods unapplicable In addition, all existing closed-loop controllers for (double-pendulum) cranes require full state feedback, while velocities are unavailable in most cases Moreover, they need to linearize the nonlinear crane model and cannot respect the actuator's practical saturation constraint, which may probably lead to actuator saturation and badly degrade the control performance (even unstable) In response to these practical issues, we suggest a novel amplitude-saturated output feedback (OFB) control approach for underactuated crane systems exhibiting double-pendulum effects We provide explicit Lyapunov-based analysis to rigorously prove that the equilibrium point of the closed-loop system is almost globally asymptotically stable, without any approximation to the original nonlinear dynamics As far as we know, this paper presents the first closed-loop control method that can achieve control for an underactuated double-pendulum crane with merely OFB and theoretically-guaranteed saturated control efforts Hardware experimental results demonstrate the superior performance of the proposed approach over existing methods and its strong robustness as well

Amplitude-Saturated Nonlinear Output Feedback Antiswing Control for Underactuated Cranes With Double-Pendulum Cargo Dynamics

In real-world applications, industrial cranes commonly suffer from effects caused by the so-called double-pendulum phenomenon in many situations. However, at present, the double-pendulum phenomenon is usually directly roughly neglected when designing control methods. For double-pendulum cranes, most currently available approaches are open loop control; the existing feedback methods are mostly developed based on linearized dynamic models (around the equilibrium point) or designed without adding integral terms in the control laws, which may cause positioning errors in the presence of unmodeled dynamics. To address these problems, this paper proposes a new quasi-proportional integral derivative control method to effectively control underactuated double-pendulum crane systems. Then, we provide rigorous theoretical analysis for the equilibrium point of the closed-loop system based on the original nonlinear dynamic equations. To our knowledge, this paper gives the first plant-parameter-free controller that incorporates both integral action and actuating constraints without any linearizing operations during controller design or closed-loop analysis, which theoretically ensures that the controller can work well in the presence of unmodeled dynamics (e.g., insufficient friction compensation), actuating constraints, and large swing angles (i.e., not satisfying linearization conditions). Finally, hardware experimental results are provided to examine the effectiveness of the suggested control method.

Transportation Control of Double-Pendulum Cranes With a Nonlinear Quasi-PID Scheme: Design and Experiments

This paper presents an adaptive genetic algorithm (AGA) for an active noise control (ANC) system. The conventional ANC system often implements the filtered extended least mean square (FXLMS) algorithm to update the coefficients of the linear finite-impulse response (FIR) and nonlinear Volterra filters, owing to its simplicity; meanwhile, the FXLMS algorithm may converge to local minima. In this paper, the FXLMS algorithm is replaced with an AGA to prevent the local minima problem. Additionally, the proposed AGA method does not require identifying the secondary path for the ANC, explaining why no plant measurement is necessary when designing an AGA-based ANC system. Simulation results demonstrate that the effectiveness of the proposed AGA method can suppress the nonlinear noise interference under several situations without clearly identifying the secondary path.

Active Noise Cancellation Without Secondary Path Identification by Using an Adaptive Genetic Algorithm

This paper presents an efficient method to capture the dynamic movement of a three-dimensional (3-D) overhead crane, enabling it to be controlled by visual feedback in real time with two cheap handy cameras. Two tracking areas and one positioning block in each frame are used to search image features and determine the useful vision information. The depth information of 3-D crane system in images can be also obtained by a tag on the dynamic plant. The presented visual tracking method involves comparison of the lightest or darkest points in the tracking or positioning area of a dynamic object and then computes the necessary trolley position and load swing in 3-D space. Upon tracking, the sensing data are sent to an adaptive fuzzy sliding-mode controller (AFSMC) to derive control power for the crane system. Accordingly, the merits of this AFSMC approach, including robustness of the sliding-mode control and the model-free property of the fuzzy logic for the 3-D crane system, are confirmed. Experimental results verify the improvement of the proposed methodology.

Efficient Visual Feedback Method to Control a Three-Dimensional Overhead Crane

A novel control for a nonlinear two-dimensional (2-D) overhead crane is proposed. Instead of the complex design procedures used in classic methods, the proposed scheme combines the principles of ne...

Parallel neural network combined with sliding mode control in overhead crane control system

Have you ever faced a situation where you really wanted to enjoy your favorite music in a very noisy environment? To overcome this problem, you can either block the noise or increase the music level to mask the annoying sounds. This begets the question, "Is there a better solution?" In recent years, the technology industry has grown enormously, especially in the areas of smart, mobile, and wearable electronic products. Many companies are trying to gain consumers' attention by developing high-quality products equipped with advanced noise reduction. Among them, several companies have successfully used active noise control (ANC) techniques to cope with noise problems.

Cheng-Yuan Chang

Papers

Active Noise Cancellation Without Secondary Path Identification by Using an Adaptive Genetic Algorithm

Efficient Visual Feedback Method to Control a Three-Dimensional Overhead Crane

Parallel neural network combined with sliding mode control in overhead crane control system

Listening in a Noisy Environment: Integration of active noise control in audio products.

Secondary path modeling for narrowband active noise control systems