This article addresses the dynamic path planning for mobile robots in a 3-D space with obstacle avoidance and moving target tracking. A new mathematical approach to calculating the dynamic path in a 3-D space is proposed. An adaptive network-based fuzzy inference system (ANFIS) with the use of the mathematical results is then implemented to generate automatically the dynamic path of the mobile robot. This approach allows implementing faster decision-making and can be seen as an efficient algorithm to avoid obstacles and to track moving targets. Simulation results validate the performance of the proposed scheme.

This paper studies the use of neural networks in robotic position/orientation control. The process is divided into two tasks, i.e., the inverse kinematics solution and the adaptive motor control. Simulation results of a three-link robotic arm in a two-dimensional workspace demonstrate the validity of the design. The hierarchal nature of the design allows it to be applied to more complicated systems that operate in a three-dimensional workspace.

This paper addresses the calibration-free robotic eye-hand coordination in a way other than the conventional image Jacobian matrix approach that has been studied extensively in literature. A nonlinear mapping rather than the linear mapping used in the image Jacobian matrix between the image space and the robotic control space is proposed. This mapping is regarded as the system's unmodeled dynamics expressed in system state equations. An extended state observer is designed first to estimate the unmodeled dynamics as well as the external disturbance of the system. With the estimation results as the compensation, a system controller is designed based on the nonlinear state-error feedback control strategy. Convergence of the extended state observer as well as the overall controller for a typical eye-hand coordination system is proved. Compared with the conventional calibration-free robotic eye-hand coordination with a Jacobian matrix, the proposed controller is independent of specific tasks and system configurations. Thus, a general design procedure is proposed for the calibration-free robotic eye-hand coordination. Simulation and experiment results demonstrate the satisfactory performance and effectiveness of the proposed approach.

This paper proposes an adaptive fuzzy sliding mode controller for robotic manipulators. An adaptive single-input single-output (SISO) fuzzy system is applied to calculate each element of the control gain vector in a sliding mode controller. The adaptive law is designed based on the Lyapunov method. Mathematical proof for the stability and the convergence of the system is presented. Various operation situations such as the set point control and the trajectory control are simulated. The simulation results demonstrate that the chattering and the steady state errors, which usually occur in the classical sliding mode control, are eliminated and satisfactory trajectory tracking is achieved.

A conservativeness-reduced design of a gain scheduled output feedback H/sub /spl infin// controller for an n-joint rigid robotic manipulator, which integrates the varying-parameter rate without their feedback, is proposed. The robotic system is reduced to be a linear parameter varying (LPV) form, which depends affinely on the varying-parameter. The design of a controller, which satisfies the closed-loop H/sub /spl infin// performance, is reduced to be a solution of the parameterized linear matrix inequalities (LMIs) of parameter matrices. With a use of the concept of "multi-convexity", the solution of the infinite LMIs in the varying-parameter and its rate space is reduced to be a solution of the finite LMIs for the vertex set. The proposed controller eliminates the feedback of the varying-parameter rate and prescribes its upper boundary so that the conservativeness of the controller design is reduced. The experiment results verify the effectiveness of the proposed design.

A new approach to the design of a fuzzy continuous gain scheduled H/sub /spl infin// controller based on Taylor series fitting for n-joint rigid robotic manipulators, which guarantees satisfactory dynamic characteristics in the whole movement range, is presented. This scheme combines the gain scheduled H/sub /spl infin// theory with LMI approach to design a continuous gain scheduled H/sub /spl infin// controller, which is applicable to systems with fast state variations so that the deficiency of the conventional gain scheduled controllers is overcome, with the use of Taylor series fitting. Fuzzy control is then incorporated so chat the designed controller possesses the characteristics of a fast response for large errors and a well-damped response for small errors. The system thus always has a good dynamic performance along with the variations of the system states. Simulations and experiments demonstrate the effectiveness of the designed controller.

A neural network (NN)-based adaptive controller with an observer is proposed for the trajectory tracking of robotic manipulators with unknown dynamics nonlinearities. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity, while NNs are employed to further improve the control performance of the controlled system through approximating the modified robot dynamics function. The adaptive controller for robots with an observer can guarantee the uniform ultimate bounds of the tracking errors and the observer errors as well as the bounds of the NN weights. For performance comparisons, the conventional adaptive algorithm with an observer using linearity in parameters of the robot dynamics is also developed in the same control framework as the NN approach for online approximating unknown nonlinearities of the robot dynamics. Main theoretical results for designing such an observer-based adaptive controller with the NN approach using multilayer NNs with sigmoidal activation functions, as well as with the conventional adaptive approach using linearity in parameters of the robot dynamics are given. The performance comparisons between the NN approach and the conventional adaptation approach with an observer is carried out to show the advantages of the proposed control approaches through simulation studies.

Speech recognition is one of the important areas in digital speech processing. The study of speech recognition is a part of a quest for artificially intelligent machines that can hear and understand spoken information. The conventional methods for speech recognition are very complicated and time consuming. To apply fuzzy logic to speech recognition is a new attempt in digital speech processing. The approach proposed in the paper simplifies the algorithm in speech recognition and makes the real-time processing time shorter. The situation considered in this paper is the simplest, i.e., the situation of speaker dependence, small vocabulary and isolated words.

Speech recognition is a major topic in speech signal processing. Many algorithms based on results of speech analysis, among which dynamic time warping) and hidden Markov models are the most important, have been advanced. However, these algorithms generally turn out to be too complicated to be implemented in real time systems. The proposed algorithm in this paper, which uses a fuzzy logic recognition approach based on the power distribution pattern of a segment of a speech, allows the implementation of real-time speech recognition.

The extraction of a desired signal from extraneous noise is a typical research topic in the field of signal processing. The filter used to realize this task to enhance a narrow-band signal in the presence of broadband noise is called a line enhancer. In this paper, line enhancement (noise cancellation) by using fuzzy theory is introduced. By using a fuzzy logic approach, the locations of the lower bound and the upper bound of the main power band, in the spectrum of an arbitrary narrow-band signal that is corrupted by a broad-band noise, can be detected. The membership functions are described mathematically by sets of line equations based on analytic geometry rather than by conventional graphics and fuzzy inference is therefore mathematically implemented in software. A proper band-pass linear FIR filter is then automatically generated, based on the detected information to filter and finally enhance the signal of interest. The computer simulation, which is analyzed in this paper, demonstrates satisfactory results. The implementation considerations of the proposed fuzzy line enhancer are compared with those of the existing adaptive line enhancers; it is satisfactory, and the algorithm is simpler.

For a class of multiinput-multioutput (MIMO) sampled-data nonlinear systems with unknown dynamic nonlinearities, a stable neural-network (NN)-based adaptive control approach which is an integration of an NN approach and the adaptive implementation of the variable structure control with a sector, is developed. The sampled-data nonlinear system is assumed to be controllable and its state vector is available for measurement. The variable structure control with a sector serves two purposes. One is to force the system state to be within the state region in which the NN's are used when the system goes out of neural control; and the other is to provide an additional control until the system tracking error metric is controlled inside the sector within the network approximation region. The proof of a complete stability and a tracking error convergence is given and the setting of the sector and the NN parameters is discussed. It is demonstrated that the asymptotic error of the system can be made dependent only on inherent network approximation errors and the frequency range of unmodeled dynamics. Simulation studies of a two-link manipulator show the effectiveness of the proposed control approach.