To realize trajectory tracking and obstacle avoidance, two distributed CCD (charge-coupled device) cameras are constructed to obtain the dynamic poses of the car-like mobile robots (CLMRs) and the obstacles. Based on the control authority of these two CCD cameras, a suitable reference command including desired steering angle and translation velocity for the fuzzy decentralized variable structure control (FDVSC) in the client computer is on-line planned. Due to the delay of signal transmission through an internet network and wireless local area network (WLAN), suitable sampling time of the FDVSC is determined by the Quality of Service (QoS). The proposed control can track an on-line planning reference command without the requirement of a mathematical model of the CLMR. Only the information of the upper bound of system knowledge (including the dynamics of the CLMR, the delay feature of internet network and WLAN) is required to select the suitable scaling factors and the coefficients of switching surface so that an acceptable performance is achieved.

In this paper, a navigation system is developed. The system includes path tracking and obstacle avoidance apparatus for a car-like wheeled robot (CLWR) within an Internet-based smart-space (IBSS) using fuzzy-neural adaptive control (FNAC). Two distributed charge-coupled device (CCD) cameras are installed to capture both the dynamic pose of the CLWR and the obstacle. Based on the control authority of these two CCD cameras, a suitable reference command that contains the desired steering angle and angular velocity for the FNAC built into the client computer is planned. Because of the delay encountered by the transmission through the Internet network (IN) and the wireless local area network (WLAN) and the nonlinear coupling features of the CLWR, a weighted combination ofNlinear subsystems that are described by a state-space model with average-delay is implemented to approximate the dynamics of an IBSS-CLWR. The proposed FNAC contains a neural network consisting of a radial basis function (RBFNN) to learn the uncertainties due to the fuzzy-model error (e.g., the random time-varying delays and the slippage of the CLWR) and the interactions caused by other subsystems. The stability of the overall system is then investigated by adopting the Lyapunov stability theory. Finally, a sequence of experiments including the control of the off-ground CLWR (i.e., the CLWR does not make contact with the ground) and the navigation of the IBSS-CLWR as compared with the conventional proportional-integral-derivative (PID) control is performed to demonstrate the advantage of the proposed control system.

In this paper, a partially known nonlinear dynamic system with time-varying delays of the input and state is approximated by N fuzzy-based linear subsystems described by a state-space model with average delay. To shape the response of the closed-loop system, a set of fuzzy reference models is established. Similarly, the same fuzzy sets of the system rule are employed to design a fuzzy neural-based control. The proposed control contains a radial-basis function neural network to learn the uncertainties caused by the approximation error of the fuzzy model (e.g., time-varying delays and parameter variations) and the interactions resulting from the other subsystems. As the norm of the switching surface is inside of a defined set, the learning law starts; in this situation, the proposed method is an adaptive control possessing an extra compensation of uncertainties. As it is outside of the other set, which is smaller than the aforementioned set, the learning law stops; under this circumstance, the proposed method becomes a robust control without the compensation of uncertainties. A transition between robust control and adaptive control is also assigned to smooth the possible discontinuity of the control input. No assumption about the upper bound of the time-varying delays for the state and the input is required. However, two time-average delays are needed to simplify the controller design: 1) the stabilized conditions for every transformed delay-free subsystem must be satisfied; and 2) the learning uncertainties must be relatively bounded. The stability of the overall system is verified by Lyapunov stability theory. Simulations as compared with a linear transformed state feedback with integration control are also arranged to consolidate the usefulness of the proposed control.

In this paper, the trajectory tracking of a two-dimensional piezo-driven system (2DPDS) using Internet-based fuzzy decentralized microprocessor control (IBFDMC) was developed. It was known that the piezoelectric actuator contained hysteresis, which was not one-to-one mapping and memory nonlinearity. Due to this nonlinearity and the coupling characteristic of 2DPDS, an effective decentralized control was difficult to design. From the very beginning, suitable coefficients of switching surface were assigned to stabilize the switching surface and to shape the response of tracking error. Based on the data of input-output, two scaling factors are employed to normalize the switching surface and its derivative. According to the concept of if-then rule, an appropriate rule table for theithsubsystem was then achieved. Finally, a sequence of experiments was carried out to confirm the usefulness of the proposed control.

In this paper, the trajectory tracking and obstacle avoidance of a car-like mobile robot (CLMR) within an intelligent space via mixed H2/ Hinfindecentralized control was developed. For implementing (dynamic) obstacle avoidance and trajectory tracking, two distributed CCD (charge-coupled device) cameras were established to realize the pose of the CLMR and the position of the obstacle. Based on the authority of these two CCD cameras, a suitable reference command for the proposed controller of the CLMR is planned by the information of the CCD camera with higher authority and then transmitted to the CLMR by a wireless device. The features of the proposed control included smaller energy consumption with bounded tracking error, attenuation of output disturbance, and improvement of control performance. The suggested control system contained two processors with multiple sampling rates. One personal computer (PC) was employed to capture the image of CLMR and obstacle, to plan a reference command for the CLMR, and then to transmit the reference command to the CLMR. The other was a DSP (digital signal processor) implementing in the CLMR to control two DC motors. A sequence of experiments was carried out to confirm the effectiveness of the proposed control system.

In this paper, a partially known nonlinear dynamic system with input and state time-varying delay was approximated by N fuzzy-based linear subsystems described by state-space model with average-delay. For tracking the trajectory with a primary frequency, the fuzzy reference models with desired amplitude and phase features were established. Similarly, the same fuzzy sets of the system rule were employed to design a fuzzy-neural variable structure control (FNVSC). The proposed control contained a radial basis neural network to learn the uncertainties caused by the fuzzy-model error and the interactions resulting from the other subsystems. As the norm of the switching surface was inside of a defined set (e.g., ||sigma(t)||sigma2) the learning law started; the proposed method was an adaptive control possessing a compensation of uncertainties. As it was outside of the other set (e.g., ||sigma(t)|| > nsigma1, where nsigma1> nsigma2) the learning law stopped; the proposed method became a robust control. A transition between robust control and adaptive control was also assigned to smooth the possible discontinuity of control input. In addition, no assumption about the upper bound of the time-varying delay for the state and the input is required; however, a time-average delay is needed for the controller design. The stability of the overall system was verified by Lyapunov stability theory