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For a class of nontriangular nonlinear systems in presence of unknown disturbances, we propose a predictor-based neural dynamic surface control (PNDSC) strategy in this paper. This nontriangular system is transformed via the mean value theorem, and a predictor is then constructed. To avoid an algebraic loop problem, partial state vectors are employed as input signals of neural networks (NNs) for approximating unknown dynamics, and compensation items are designed to compensate for approximation errors from NNs. Different from the traditional NDSC, the PNDSC in this paper utilizes prediction errors to update learning parameters for improving NNs’ learning behaviors with overlarge adaptive gains. On the basis of improved NNs’ approximation behaviors, a predictor-based NNs disturbance observer (PNNDO) is constructed for compensation for external disturbances and approximation errors from NNs. Furthermore, with predictors, a normalization method of weights is developed to reduce the number of online learning parameters. On the basis of the aforementioned result, measurement noises are taken into account in our predictor-based neural control strategy. We employ predictor states, rather than measurement information paralyzed by noises, in design of our control strategy. This reduces high-frequency oscillations in control input. A Lyapunov-based stability analysis shows that all signals are ultimately bounded in the closed-loop system. Finally, the effectiveness of the proposed control strategy is verified by a numerical example and a permanent magnet brushless DC motor system. [ABSTRACT FROM AUTHOR]

Transcendental functions are commonly used in many fields such as nonlinear functions of artificial neural networks (ANNs). Due to nonlinearity of these functions, hardware implementation of the functions faces many challenges. Some methods cannot control the approximation accuracy beforehand, or they cannot approximate target functions accurately and efficiently. Other methods can only approximate a particular function without generality. A universal piecewise linear (PWL) approximation method is proposed to solve the problems existing in the above methods. In this paper, a general piecewise linear (PWL) approximation method with controllable maximum absolute error for transcend-ental functions is proposed. The method has the self-adaptive capability to choose the smallest number of segments under the constraint of a controllable maximum absolute error. Therefore, it requires fewer segments and incurs less hardware overhead. Moreover, it can approximate any transcendental functions and does not rely on any properties of the target function. Comparing our work with other methods used in three transcendental functions, including the sigmoid, hyperbolic tangent, and logarithmic function, our method improves the approximation accuracy by 4.5 times, 2.1 times, and 1.3 times, respectively; in addition, our method is more efficient and it reduces the circuit area by 49.9%, 22.4%, and 28.5%, respectively. [ABSTRACT FROM AUTHOR]