A prescribed‐performance‐like control for improving tracking performance of networked robots

Abstract

To balance the performance and implementability of classical predefined performance control algorithm when its hard constraint is violated, this article investigates a switching-based prescribed-performance-like control (PPLC) approach for nonlinear teleoperation systems. To achieve the objective, a switching mechanism is introduced into the classical controller design and based on this, a nonlinear proportional plus auxiliary compensation controller is designed, where a switched filter control technique is employed.By modeling the resulting closed-loop system as a special switched delayed system and using the piecewise Lyapunov-Krasovskii functional method, the state-independent input-to-output stabilty of closed-loop system is established, where a feasible range of the "initial" values of piecewise continuous performance function is given to achieve a tradeoff between the closed-loop stability and overshoot. It is shown that the developed switching-based PPLC approach obtains better performance than the classical predefined performance control algorithms. Simulations on a pair of 3-DOF planar arms illustrate the approach.

Filter‐based robust model‐free adaptive funnel control for discrete‐time nonlinear systems with jumped reference signal

“…where y 0 = y −1 = −5, and the measurement noise 𝜖 k is given as a white noise with zero mean and 0.0033 variance. Parameters in the proposed control strategy, that is, the update laws ( 33)-(34) and the robust controller (44), are collected in Table 1, and the desired reference signal is given as follows FOF (32) h f = 2h s = 0.2s > h s > 0 with sampling period h s = 0.1 s Update laws (33) and ( 34) In order to verify the effectiveness of the proposed control strategy, the robust MFAC-DFF in Section 4, the MFAC-DFF without using FOF and the typical MFAC 3 with constant weighting factor 𝜆 j k = 𝜆 ∞ = 2 are tested, respectively. The results in Figure 6 demonstrate that the tracking performances of the proposed robust MFAC-DFF are the best, and the chattering of its control signal is also the smallest, which verifies the effectiveness of FOF (32) and time-varying weighting factor 𝜆 j k (42).…”

“…In addition, in the existing MFACs with prescribed performance 27‐30 and permitted control parameters, the quantitative analysis of tolerable uncertainties and performance margin defined as a minimum distance from the tracking error to the preset performance boundaries has not been fully analyzed and discussed. On the other hand, the jumped reference signal in robotics, vehicles and industrial electronics 31 will result in the tracking error exceeding the performance constraints, which has been solved by re‐setting the performance boundaries for continuous‐time systems 31,32 . But, this problem has not been considered in recent MFACs with prescribed performance for discrete‐time nonlinear systems, 27‐29 especially without re‐setting the performance boundaries.…”

“…On the other hand, the jumped reference signal in robotics, vehicles and industrial electronics 31 will result in the tracking error exceeding the performance constraints, which has been solved by re-setting the performance boundaries for continuous-time systems. 31,32 But, this problem has not been considered in recent MFACs with prescribed performance for discrete-time nonlinear systems, [27][28][29] especially without re-setting the performance boundaries.…”