The direction-of-arrival (DOA) estimation model of this paper is based on deviation compensation model with input noise, and the performance of the traditional Least mean square (LMS) adaptive algorithm shows poor performance. Instead, total least squares (TLS) algorithm is widely used in models which contains input noise. Therefore, we present TLS algorithm for DOA estimation, which is used to update weight coefficient by searching the peak of the spatial spectrum to estimate the direction of the angle of arrival. Due to the unsatisfactory DOA estimation performance on fixed step algorithms, a variable stepsize gradient descent total least-squares (VSS-GDTLS) is proposed. The variable step size strategy is derived by applying the instantaneous augmented weight vector and the estimated signal power. Moreover, the convergence of the proposed algorithm is analyzed. Finally, simulation results show the superiority of the VSS-GDTLS algorithm than GDTLS and the other adaptive algorithms.

Robot dynamics model uncertainty and unpredictable external perturbations are important factors that influence control accuracy and stability. To accurately compensate for the dynamics model in sliding mode control (SMC), a new parallel network (PCR) is proposed in this paper. The network parallelizes the radial basis function and convolutional neural network, which gives it the advantage of making full use of one-dimensional data fitting results and two-dimensional data feature information, realizing the deep learning of multidimensional data and improving the model’s compensation accuracy and anti-interference ability. Meanwhile, based on the integration of adaptive control techniques and gradient descent, a new weight update algorithm is designed to realize the online learning of PCR networks under loss-free functions. Then, a new sliding mode controller (PCR-SMC) is established. The model-free intelligent control of the robot is accomplished without knowledge of the predetermined upper bounds. Additionally, the stability analysis of the control system is proved by the Lyapunov theorem. Lastly, robot tracking control simulations are performed on two trajectories. The results demonstrate the high-precision tracking performance of this controller in comparison with the RBF-SMC controller.

In the past, 1/f noise was regarded as a stochastic process that accords with Gaussian distribution. According to our studies, the output transistor 1/f noise can be characterized more accurately as non-Gaussian α-stable distribution rather than Gaussian distribution. We define and consistently estimate the samples normalized cross-correlations of linear SαS processes and propose a samples normalized cross-correlations–based α-spectrum method effective in noisy environments. Simulation results and diodes noise spectrum estimation results exhibit good performance.