Adaptive Predefined Performance Neural Control for Robotic Manipulators with Unknown Dead Zone

Abstract

This paper proposes an adaptive predefined performance neural control scheme for robotic manipulators in the presence of nonlinear dead zone. A neural network (NN) is utilized to estimate the model uncertainties and unknown dynamics. An improved funnel function is designed to guarantee the transient behavior of the tracking error. The proposed funnel function can release the assumption on the conventional funnel control. Then, an adaptive predefined performance neural controller is proposed for robotic manipulators, while the tracking errors fall within a prescribed funnel boundary. The closed-loop system stability is proved via Lyapunov function. Finally, the numerical simulation results based on a 2-DOF robotic manipulator illustrate the control effect of the presented approach.

An Improved Multisensor Self-Adaptive Weighted Fusion Algorithm Based on Discrete Kalman Filtering

“…Drawbacks of the Current Algorithms. It is not difficult to conclude from ( 25), (26), and ( 30) that both of the conditional extremes of the estimation error and the sensor weights are closely related to the measurement variance of the sensor. e smaller the sensor measurement variance is, the smaller the conditional extreme of the error is estimated and the larger the corresponding sensor weight is, which reflect the characteristics that the sensor weight is autoadaptive to the sensor measurement variance.…”

“…As described in Section 4.1, there were 3 humidity sensors (type: RS485) to measure soil moisture in a certain area at the same time. e true value of soil moisture at this moment was set to 21RH% (can be considered constant in a certain period of time), and based on this, true value Gaussian white noise (white noise launcher type: Noisecom NC6000A/ NC8000A) with a mean of zero and a variance of 0.2, 0.5, and 0.7 was added, respectively, to simulate the noise pollution of Take N filter output as the data source of the fusion algorithm Calculate R jj (k), R ij (k) according to (33) and (34) Calculate the mean value of R ij (k) according to (35) Calculate the variance of each sensor according to (36) Calculate the average of historical measurement data of each sensor according to (26) Calculate the weight value of each sensor according to (25) Calculate the estimated value according to (27)…”

“…It is a recursive linear minimum variance estimation method, and it can process the measured values which are only related to some states, so as to obtain the state estimation with the least estimation error [15][16][17]. As shown in Figure 1, the improved algorithm [17] runs on the host computer system and fuses the raw measurement data (including noise) [18][19][20][21][22][23][24][25] which are sent to the host computer through the ZigBee wireless sensor [26][27][28][29][30][31][32][33] network (ZigBee WSN). e fused data are used as the decision of the irrigation control.…”

Adaptive sliding mode disturbance observer–based funnel trajectory tracking control of quadrotor with external disturbances

“…In another line, funnel control can also be utilized to improve the transition performance of control systems, and as comparison made in [31], funnel control is simpler and more effective than PPC. Several applications based on this method have been developed in [32][33][34][35][36]. To address the problem of unknown dead zone in robotic manipulator systems while guaranteeing the transient behaviour of tracking error, a backstepping funnel control is proposed in [32], where the dead zone is represented as a linear time-varying system and the bound of input dead zone is not required.…”

“…To address the problem of unknown dead zone in robotic manipulator systems while guaranteeing the transient behaviour of tracking error, a backstepping funnel control is proposed in [32], where the dead zone is represented as a linear time-varying system and the bound of input dead zone is not required. Different from [32], the neural network-based funnel controller is proposed in [33], in which neural network is utilized to estimate the non-linear dead zone. In [34], funnel control is applied to ensure the transient and steady-state performance of velocity and altitude tracking for air-breathing hypersonic vehicles.…”