Neural Adaptive Backstepping Control of a Robotic Manipulator With Prescribed Performance Constraint

Abstract

This paper presents an adaptive neural network (NN) control of a two-degree-of-freedom manipulator driven by an electrohydraulic actuator. To restrict the system output in a prescribed performance constraint, a weighted performance function is designed to guarantee the dynamic and steady tracking errors of joint angle in a required accuracy. Then, a radial-basis-function NN is constructed to train the unknown model dynamics of a manipulator by traditional backstepping control (TBC) and obtain the preliminary estimated model, which can replace the preknown dynamics in the backstepping iteration. Furthermore, an adaptive estimation law is adopted to self-tune every trained-node weight, and the estimated model is online optimized to enhance the robustness of the NN controller. The effectiveness of the proposed control is verified by comparative simulation and experimental results with Proportional-integral-derivative and TBC methods.

MLP-Based Neural Guaranteed Performance Control for MEMS Gyroscope With Logarithmic Quantizer

“…with i _,¯ i being positive constants, ρ i (t) for i = 1, 2 being the preselected performance function and it is typically chosen as the following exponential decaying one [13]- [15].…”

“…with ζ i (t) = e pi (t)/ρ i (t) being the normalized function. Lemma 2 [13]: As long as the transformed error z i is bounded, the MEMS gyroscope tracking error e pi is within the preselected boundaries, i.e., − i ρ i (t) < e pi (t) <¯ i ρ i (t) always holds.…”

“…To further improve the tracking performance in both transient and steady-state phases and remove the design conservatism universally existing in previous control methods for MEMS gyroscope, which can only ensure the closed-loop stability with UUB, prescribed performance control (PPC) proposed in [13]- [15] is an efficient strategy. Nowadays, PPC has been commonly used in uncertain nonlinear systems such as air-breathing hypersonic vehicle (AHV) [16], [17] and [35], robotic manipulator [13] and underwater vehicles [18]. With the aid of a proper equivalent error transformation function, PPC can assure the tracking error within predetermined boundaries, such that the prescribed performance can be realized without the tedious tuning of design parameters.…”

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

“…Therefore the aforementioned methods may be invalid in some control practices with high transient-state and steady-state performance requirements. To meet such application requirements, fortunately, a novel control method named predefined performance control is proposed, [9][10][11] which includes the prescribed performance control (PPC) paradigm [12][13][14] and the barrier Lyapunov function (BLF) approach. [15][16][17][18]…”

Improved Beetle Antennae Search Algorithm-Based Lévy Flight for Tuning of PID Controller in Force Control System

“…e adaptive control system can effectively find signal changes in real time by continuously measuring and monitoring controlled systems [19]. When the controlled system is a complex system, the adaptive control method has to identify numerous parameters by the long-running job, which can cause damage in transmission system parts and excessive bearing wear [20]. e PID control system, which is also called the proportional-integral-derivative controller, is the favored control method [21].…”