Ching-Cheng Teng

Identification and control of dynamic systems using recurrent fuzzy neural networks

Proposes a recurrent fuzzy neural network (RFNN) structure for identifying and controlling nonlinear dynamic systems. The RFNN is inherently a recurrent multilayered connectionist network for realizing fuzzy inference using dynamic fuzzy rules. Temporal relations are embedded in the network by adding feedback connections in the second layer of the fuzzy neural network (FNN). The RFNN expands the basic ability of the FNN to cope with temporal problems. In addition, results for the FNN-fuzzy inference engine, universal approximation, and convergence analysis are extended to the RFNN. For the control problem, we present the direct and indirect adaptive control approaches using the RFNN. Based on the Lyapunov stability approach, rigorous proofs are presented to guarantee the convergence of the RFNN by choosing appropriate learning rates. Finally, the RFNN is applied in several simulations (time series prediction, identification, and control of nonlinear systems). The results confirm the effectiveness of the RFNN.

Tracking control of unicycle-modeled mobile robots using a saturation feedback controller

The tracking control problem with saturation constraint for a class of unicycle-modeled mobile robots is formulated and solved using the backstepping technique and the idea from the LaSalles invariance principle. A global result is presented in which several constraints on the linear and the angular velocities of the mobile robot from recent literature are dropped. The proposed controller can simultaneously solve both the tracking and regulation problems of a unicycle-modeled mobile robot. With the proposed control laws, the robot can globally follow any path specified by a straight line, a circle or a path approaching the origin using a single controller. As demonstrated, the circular and parallel parking control problem are solved using the proposed controller. Computer simulations are presented which confirm the effectiveness of the proposed tracking control law. Practical experimental results validate the simulations.

Direct adaptive iterative learning control of nonlinear systems using an output-recurrent fuzzy neural network

In this paper, a direct adaptive iterative learning control (DAILC) based on a new output-recurrent fuzzy neural network (ORFNN) is presented for a class of repeatable nonlinear systems with unknown nonlinearities and variable initial resetting errors. In order to overcome the design difficulty due to initial state errors at the beginning of each iteration, a concept of time-varying boundary layer is employed to construct an error equation. The learning controller is then designed by using the given ORFNN to approximate an optimal equivalent controller. Some auxiliary control components are applied to eliminate approximation error and ensure learning convergence. Since the optimal ORFNN parameters for a best approximation are generally unavailable, an adaptive algorithm with projection mechanism is derived to update all the consequent, premise, and recurrent parameters during iteration processes. Only one network is required to design the ORFNN-based DAILC and the plant nonlinearities, especially the nonlinear input gain, are allowed to be totally unknown. Based on a Lyapunov-like analysis, we show that all adjustable parameters and internal signals remain bounded for all iterations. Furthermore, the norm of state tracking error vector will asymptotically converge to a tunable residual set as iteration goes to infinity. Finally, iterative learning control of two nonlinear systems, inverted pendulum system and Chuas chaotic circuit, are performed to verify the tracking performance of the proposed learning scheme.

A novel robust PID controllers design by fuzzy neural network

In the paper, we propose a robust PID tuning method using fuzzy neural network (FNN) based on robust gain and phase margin (GM/PM) specifications. The designed PID controller is available for the interval plant family. We can use the trained FNN system to determine the parameters of PID controllers that are based on the robust GM/PM. To determine the robust GM/PM, the Kharitonov 32 extreme systems are used. Therefore, the FNN system is able to automatically tune the PID controller parameters with different GM/PM specifications, so that neither numerical methods nor graphical methods have to be used. This makes it easy to tune the controller parameters to have the specified robustness and performance. Simulation results are shown to illustrate the effectiveness of the robust PID controller scheme.

Tracking control of mobile robots using saturation feedback controller

A general tracking control problem with saturation constraint for nonholonomic mobile robots is proposed and solved using the backstepping technique. A global result is given in which some artificial assumptions about the linear and the angular velocities of mobile robots from recent literature are dropped. The proposed controller can simultaneously solve both the tracking problem and the regulation problem of mobile robots. With the proposed control laws, mobile robots can now globally follow any path such as a straight line, a circle and the path approaching to the origin using a single controller. Computer simulations are presented which confirm the effectiveness of the tracking control laws. Moreover, practical experimental results concerning the tracking control are reported with saturation constraint for mobile robots.

Takagi-Sugeno recurrent fuzzy neural networks for identification and control of dynamic systems

In this paper, we propose a Takagi-Sugeno recurrent fuzzy neural network (TSRFNN) for the identification and control of nonlinear dynamic systems. The TSRFNN combines the recurrent multi-layered connectionist network with the dynamic Takagi-Sugeno (TS) fuzzy model. The temporal information is embedded in the recurrent structure by adding feedback connections between the state layer and the input layer of the fuzzy neural net (FNN). Based on the derived dynamic backpropagation (DBP) and recursive least squares (RLS) algorithms, the parameters in the TSRFNN are adjusted online. Compared with the traditional recurrent FNNs (RFNNs), the proposed TSRFNN not only has a smaller network structure and a smaller number of network parameters, but also a faster convergence speed and better learning performance.

Intelligent control of high-speed sensorless brushless DC motor for intelligent automobiles

For the last decade, the need for electric driven vehicle has risen rapidly due to the global warming problem. A high efficient motor need to be developed to replace traditional engine. In this paper, the authors present a fuzzy logic controller (FLC) for sensorless brushless dc (BLDC) motor. The proposed FLC is based on the terminal voltage measurement as the sensorless method for BLDC motor control. There are two modules designed in the speed control of BLDC motor, i.e. command and regulating modules. Command module is to find and issue commutation period and PWM duty cycle to the BLDC motor for desired speed. The regulating module is designed by applying a FLC with sensorless technology and is used to regulate the speed of BLDC motor under various disturbances, such as loading effect. Two resistances are adopted to detect the zero-crossing point (ZCP) of back-EMF signal on the unexcited phase instead of using the expensive Hall sensors. The regulating module can regulate accurately and quickly the PWM duty cycle by fuzzy reasoning which makes the BLDC motor rotate smoothly at desired rotation speed even there exists an external disturbance. In order to reduce the computational load of real-time FLC for the microcontroller C8051F330, we can perform the FLC computation off-line to cover nearly all cases. These data will be recorded and organized as a Look-Up Table (LUT). This paper shows that a regulating module designed by FLC is better than a none regulation module or a regulating module with a P controller.

Test Generation and Site of Fault for Combinational Circuits Using Logic Petri Nets

In this paper, we propose a novel Petri net model for solving test generation and site of fault and fired logical value for combinational circuits. In order to improve the logic fault efficiency, the transitions of general Petri nets (PNs) are modified according to the critical of truth table, called logic Petri net LPN. The LPN model can transfer complexity circuit problem to a local adjacent place and transition relational problem. Therefore, the site of fault and fired logical value problem is simplified and clearly. The LPN model has the properties of Boolean algorithm, collapsing fault with clear physical concepts, fast calculation speed, and high veracity. The approach contains site of a fault and fired logical value reasoning algorithm and test vector generation reasoning algorithm. Two examples are shown to demonstrate the effectiveness of our approach.