Robust controller with disturbance rejection for hydraulic servosystems

A controller is presented that improves the performance and reduces the sensitivity of hydraulic servo systems in the presence of parameter variations, nonlinear effects, and other disturbances. The control system has enhanced closed-loop stability and can recover from steady-state and transient error quickly and completely. It consists of a servo controller, an auxiliary controller, and a robust controller. Simulation and experimental results demonstrate the validity of the controller     

Robust parametric CMAC with self-generating design for uncertain nonlinear systems

In this paper, a robust parametric cerebellar model articulation controller (RP-CMAC) with self-generating design, called RPCSGD, is proposed for uncertain nonlinear systems. The proposed controller consists of two parts: one is the parametric CMAC with self-generating design (PCSGD), which is utilized to approximate the ideal controller and the other is the robust controller, which is designed to achieve a specified H^∞ robust tracking performance of the system. The corresponding memory size of the proposed controller can be suitably constructed via the self-generating design. Thus, the useless or untrained memories will not take possession of the space. Besides, the concept of sliding-mode control (SMC) is adopted so that the proposed controller has more robustness against the approximated error and uncertainties. The stability of the system can be guaranteed surely due to the derivations of the adaptive laws of the proposed RPCSGD based on the Lyapunov function. Finally, the proposed controller is applied to the second-order chaotic system and the one-link rigid robotic manipulator. The tracking performance and effectiveness of the proposed controller are verified by simulations of the computer.     

Robust stabilization for multivariable systems with constrained control: Frequency domain approach

In this paper, robust stabilization conditions based on theH ^∞-norm are derived for multivariable feedback systems under perturbations and constrained control. The parametrized controller of Youlaet al. is employed to treat this problem. In addition, a necessary and sufficient condition is derived for the solvability of the synthesis problem for a controller which achieves robust stability. Finally, a design procedure is proposed for selecting the parameters of the robust controller, and an illustrative example is given.     

Observer-Based Speed Estimation Method for Sensorless Vector Control Using Artificial Neural Network

This paper presents a novel approach to sensorless vector control of induction motor drives. The method is based on an adaptive flux observer in the rotorspeed reference frame in which an artificial neural network (ANN) is employed to modify the estimated rotor flux to improve the performance of speed estimation. The adopted ANN is a feed-forward neural network identified off-line. It uses the backpropagation learning process to update their weights. The data for training are obtained from a computer simulation and experimental data file of a vector control system. Then, the estimated rotor flux is used in the speed estimation that will feedback to the vector control system. The proposed method has the advantages of better accuracy at low speed range and speed following under heavy loads. Experimental results show the effectiveness of the proposed method.     

Grey-Fuzzy Implementation of Direct Torque Control of Induction Machines

A system with fast torque response is very beneficial in applications where direct torque control (DTC) is highly desirable. The principle of DTC is to control machine torque and flux directly by selecting the switching states of inverter according to the errors of torque and flux; however, the response of direct torque control is slower during start-up and during change in load. Fuzzy control with grey prediction strategy is proposed for the implementation of DTC to improve its slow response. Moreover, it uses less sensors than the conventional method. Experimental implementation of the fuzzy logic controller and the grey predictor is carried out to verify the behavior of the controller. The controller is implemented with a single board computer that uses a TMS320C30 DSP.     

A novel self-aligned fabrication process for nickel-indiffusedlithium niobate ridge optical waveguides

A novel self-aligned fabrication process for LiNbO₃:Ni ridge waveguides has been proposed. By using the self-aligned trilayered structure composed of Ni-Ti-Si, the fabrication process is significantly simplified, and takes advantage of suppression of the unwanted planar waveguides and high-coupling efficiency to a single-mode fiber as compared to the conventional processes. Detailed investigations into the characteristics of the ridge waveguides have also proved to be informative under different fabrication parameters. Moreover, the novel self-aligned fabrication process was applied to fabricate a ridge waveguide Mach-Zehnder modulator. The measured half-wave voltage and extinction ratio mere 20.5 V and 12 dB (@1.3 μm), and were 4.2 V and 8 dB (@0.6328 £gm)     

Design and Implementation of a DSP-Based Grey-Fuzzy Controller for Induction Motor Drive

A DSP-based magnetic flux controlled induction motor drive with a grey-fuzzy controller is designed and implemented. This paper investigates the possibility of applying grey theory and fuzzy algorithm in a DSP-based induction motor controller, which requires faster and more accurate response, compared with a traditional fuzzy controller. The proposed controller is composed of two parts. The first one is a grey prediction controller, and the other is a general fuzzy logic controller. The experiments are performed by controlling the induction machines, and the whole system is executed by a digital signal processor (TMS320C30). The experimental results show that the better dynamic speed performances and torque regulation characteristics are obtained by the proposed controller.     

Robust stability analysis for two-dimensional systems via eigenvalue sensitivity

This paper presents a robust stability analysis tool for the two-dimensional (2-D) discrete system by using eigenvalue sensitivity. It includes the 2-D system stability test via one-dimensional (1-D) system robust stability analysis. The merits of this method are fewer computations and good programmability. How to estimate the stability margin, on which the degree of stability and the spatial performance of the system depend, is also an important issue in this paper.Parts of this work have been presented at the IEEE International Conference On Acoustic, Speech, and Signal Processing, 1993 in Minnesota, USA. This work was supported by the National Science Council of the Republic of China under Grant NSC 81-E036-514.     

Design and analysis of direct-action CMAC PID controller

This paper is to propose a direct-action (DA) cerebellar model articulation controller (CMAC) proportional-integral-derivative (PID) controller. The proposed controller, termed the DAC-PID controller, can generate four simple types of the nonlinear functions and then determine a control effort from those functions to control the process. In addition, the real-coded genetic algorithm is used to tune the parameters of the DAC-PID controller such that we can optimize those parameters. The performance of the proposed controller is also discussed in the sense of quantitative analysis. Simulation results demonstrate that the DAC-PID controller is superior to the conventional PID controller tuned by Ziegler–Nichols method and, moreover, as better as the optimal PID controller and the optimal fuzzy-PID controller.