Rey-Chue Hwang

A self-tuning PID control for a class of nonlinear systems based on the Lyapunov approach

Abstract In this paper, we will propose a self-tuning method for a class of nonlinear PID control systems based on Lyapunov approach. The three PID control gains are adjustable parameters and will be updated online with a stable adaptation mechanism such that the PID control law tracks certain feedback linearization control, which is previously designed. The stability of closed-loop nonlinear PID control system is analyzed and guaranteed by introducing a supervisory control and a modified adaptation law with projection. Finally, a tracking control of an inverted pendulum system is illustrated to demonstrate the control performance by using the proposed method.

Optimal PID speed control of brush less DC motors using LQR approach

A novel optimal PID control design is proposed in this paper. The methodology of linear quadratic regulator is utilized to search the optimal parameters of the PID controller. The augmented state vector of performance measure involves output signals only. The weighting functions are determined through poles assignment. The existence criteria of the optimal PID controller are derived. The new PID tuning algorithm is applied to the speed control of BLDC motors. Computer simulations and experimental results show that the performance of the optimal PID controller is better than that of the traditional PID controller.

A new artificial intelligent peak power load forecaster based on non-fixed neural networks

In this paper, a new artificial intelligent peak power load forecaster constructed by non-fixed neural networks (NNs) is developed. Several techniques, including gray analysis and stochastic back-propagation learning rule with dynamic learning rate and momentum, are used in this forecaster in order to attain more accurate prediction in forecasting operation. As a comparison, several models including recursive time series model, ANNSTLF module and fixed size NNs with constant learning rate and momentum are also performed for demonstrating the advantages of our proposed forecaster.

Application of an auto-tuning neuron to sliding mode control

This paper presents a control strategy that incorporates an auto-tuning neuron into the sliding mode control (SMC) in order to eliminate the high control activity and chattering due to the SMC. The main difference between the auto-tuning neuron and the general one is that a modified hyperbolic tangent function with adjustable parameters is employed. In this proposed control structure, an auto-tuning neuron is then used as the neural controller without any connection weights.. The control law will be switched from the sliding control to the neural control, when the state trajectory of system enters in some boundary layer. In this way, the chattering phenomenon will not occur. The results of numerical simulations are provided to show the control performance of our proposed method.

A multivariable on-line adaptive PID controller using auto-tuning neurons

Abstract In this paper, we present a new PID control technique based on auto-tuning neurons for multivariable systems. The main difference between an auto-tuning neuron and a general neuron is that there are adjustable parameters of the activation function used in an auto-tuning neuron. In this paper, a modified hyperbolic tangent function is used as the activation function of an auto-tuning neuron, which provides two adjustable parameters to flexibly determine the magnitude and the shape of function. We then use such auto-tuning neurons to find gains of the multivariable PID controller, which is tuned on-line according to certain adaptation laws. Finally, two illustrative examples will be used to compare the performance by using our proposed method and other methods.

A passive auto-focus camera control system

This paper presents a passive auto-focus camera control system which can easily achieve the function of auto-focus with no necessary of any active component (e.g., infrared or ultrasonic sensor) in comparison with the conventional active focus system. To implement the technique we developed, the hardware system including the adjustable lens with CMOS sensor and servo motor, an 8051 image capture micro-controller, a field programmable gate array (FPGA) sharpness measurement circuit, a pulse width modulation (PWM) controller, and a personal digital assistant (PDA) image displayer was constructed. The discrete wavelet transformation (DWT), the morphology edge enhancement sharpness measurement algorithms, and the self-organizing map (SOM) neural network were used in developing the control mechanism of the system. Compared with other passive auto-focus methods, the method we proposed has the advantages of lower computational complexity and easier hardware implementation.

Artificial intelligent analyzer for mechanical properties of rolled steel bar by using neural networks

In this paper, an artificial intelligent (AI) analyzer for mechanical properties of rolled steel bar by using neural networks was proposed. Based on the learning capability of neural network, the nonlinear and complex relationships among the steel bars properties, the billet compositions and the control parameters of manufacture could be automatically developed. Such an AI analyzer could help the technician to precisely set the related control parameters on the bar rolling process. Not only the quality of steel bar could be improved, the production cost of the bar could also be greatly reduced.

An effective learning of neural network by using RFBP learning algorithm

In this paper, an effective learning of neural network by using random fuzzy back-propagation (RFBP) learning algorithm is developed. Based on this new learning algorithm, neural network not only has an accurate learning capability, but also can increase the probability of escaping from the local minimum while neural network is training. For demonstrating the new algorithm we developed has its outperformance, the classifications of the non-convex in two dimensions (NC2) problem are simulated. For comparison, the same simulations by using conventional back-propagation (BP) learning algorithm with constant pairs of learning rate (α = 0.1-0.9) and momentum (ξ = 0.1-0.9) and stochastic BP learning are also performed.

The non-stationary signal prediction by using quantum NN

In this paper, the non-stationary power signal prediction by using quantum neural network (QNN) is proposed. The signals with fuzziness are expected to be classified clearly for enhancing the learning efficiency of neural network due to the hidden units with various graded levels in QNN structure. For a comparison, all experiments are also performed using the conventional neural network (CNN) structure.

A Watermarking Technique Based on the Frequency Domain

A watermarking technique based on the frequency domain is presented in this paper. The one of the basic demands for the robustness in the watermarking mechanism should be able to dispute the JPEG attack since the JPEG is a usually file format for transmitting the digital content on the network. Thus, the proposed algorithm can used to resist the JPEG attach and avoid the some weaknesses of JPEG quantification. And, the information of the original host image and watermark are not needed in the extracting process. In addition, two important but conflicting parameters are adopted to trade-off the qualities between the watermarked image and the retrieve watermark. The experimental results have demonstrated that the proposed scheme has satisfied the basic requirements of watermarking such as robustness and imperceptible.