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In this paper, we aim to improve the tracking performance of the manipulator joint system by establishing accurate friction model based on the Stribeck model and the cubic polynomial method. Meanwhile, in view of the established system model, an adaptive Radial Basis Function Neural Network (RBFNN) compensation computed-torque controller is designed for the manipulator joint system. Firstly, we consider the friction modeling process at low- and high- velocity regions to advance the model accuracy, and identify the parameters in the friction model equation offline via the particle swarm optimization (PSO) algorithm. Secondly, an adaptive RBFNN algorithm is developed to analyze the unmodeled dynamics online and introduce it to the computed-torque controller design. After that, we further conduct the stability analysis for the proposed controller based on the Lyapunov stability criterion. Finally, the self-developed manipulator joint platform introduction, the simulation experiment and the contradistinctive experiments are given to illustrate the effectiveness of designed controller. [ABSTRACT FROM AUTHOR]

Space robots are widely used for on-orbit capturing. After capturing a rotational non-cooperative target, its momentums produce unpredictable movements for the resultant space robot-target compound system. In order to address this problem, a collision-free detumbling strategy (CFDS) is proposed for space robots with a single 7-degree-of-freedom (DOF) manipulator. First, an acceleration potential field (AccPF) is presented for planning detumbling velocity trajectories, which guide the compound system to terminal rest while guaranteeing collision avoidance in dynamic environments. Energy conservation and acceleration limitations are considered in the trajectory generation. Afterwards, with a radial basis function neural network (RBFNN) approximating the target's uncertain dynamics, an adaptive control scheme is established to precisely track the planned detumbling velocity trajectory. Furthermore, system stability is certified by a Lyapunov function. The effectiveness of the CFDS is eventually validated by simulations. [ABSTRACT FROM AUTHOR]

To overcome the uncertainties of the nonlinear model of a morphing aircraft, this paper presents a high-precision adaptive back-stepping control method based on the radial basis function neural network (RBFNN). Firstly, based on the analysis of static and dynamic aerodynamic parameters of the morphing aircraft, its nonlinear control law is designed by using the conventional back-stepping method. The RBFNN is introduced to approximate online the uncertain terms of the nonlinear control law so as to improve its robustness. The robust term is designed to eliminate the approximation error caused by the RBFNN. Secondly, the tracking differentiator is designed through solving the virtual control variables, thus solving the "differential expansion" problem existing in the traditional back-stepping method. The Lyapunov stability analysis proves that our method can ensure that the tracking error of a closed-loop system converges finally and that its signals are uniformly bounded. Finally, the digital simulation model of the morphing aircraft is established with the MATLAB/Simulink; our method is compared with the conventional back-stepping control method. The simulation results show that our method has a higher control precision and stronger robustness.

This paper presents a robust adaptive backstepping control (RABC) for high-speed permanent-magnet synchronous motor (HSPMSM) drive system. The proposed RABC achieves high performance operation by incorporating an ideal backstepping controller (IBC), a recurrent radial basis function neural network (RRBFNN) uncertainty observer, and a robust controller. The Lyapunov stability theorem is utilized to design the IBC as a position controller of the HSPMSM servo drive system. To enhance the disturbance rejection capability during parameter changes, certain information is needed within the backstepping control law so that the system performance would not sorely be affected. To mitigate the need for the lumped parameter uncertainties within the backstepping controller, an online adaptive observer based on RRBFNN is designed to estimate the nonlinear parameter uncertainties. Moreover, the robust controller is intended to retrieve the remaining of the RRBFNN approximation errors. To assure the stability of the proposed RABC, the Lyapunov stability analysis is used to derive the online adaptive control laws. The performance of the proposed RABC is verified by simulation and experimental analysis under different operating conditions and parameter uncertainties. The test results validate the effectiveness of the proposed RABC scheme to achieve preferable tracking performance regardless of external disturbances and parameter uncertainties.

High parking accuracy, comfort and stability, and fast response speed are important indicators to measure the control performance of a fully automatic operation system. In this paper, aiming at the problem of low accuracy of the fully automatic operation control of urban rail trains, a radial basis function neural network position output-constrained robust adaptive control algorithm based on train operation curve tracking is proposed. Firstly, on the basis of the mechanism of motion mechanics, the nonlinear dynamic model of train motion is established. Then, RBFNN is used to adaptively approximate and compensate for the additional resistance and unknown interference of the train model, and the basic resistance parameter adaptive mechanism is introduced to enhance the anti-interference ability and adaptability of the control system. Lastly, on the basis of the RBFNN position output-constrained robust adaptive control technology, the train can track the desired operation curve, thereby achieving the smooth operation between stations and accurate stopping. The simulation results show that the position output-constrained robust adaptive control algorithm based on RBFNN has good robustness and adaptability. In the case of system parameter uncertainty and external disturbance, the control system can ensure high-precision control and improve the ride comfort.

Summary: In this paper, a novel adaptive control scheme is proposed based on radial basis function neural network (RBFNN). The considered system is deduced by the structure of RBFNN with nonzero time‐varying parameter that installed in the fore‐end and terminal of RBFNN. With this structure and the Taylor expansion of any smooth continuous nonlinear function, a universal approximation of RBFNN is addressed according to the analysis of the character of continuous homogenous function and the Euler's theorem. The approximation accuracies can be adjusted online by the nonzero time‐varying parameter in the device with the degree of continuous homogenous function, which expand the semiglobally stability to global stability over conventional neural controller design approaches. Based on the theory analysis of barrier Lyapunov function, the violation of time‐varying constraints can be subjugated without wrecked. Finally, simulation results are carried out to verify the effectiveness by the design methods. [ABSTRACT FROM AUTHOR]

Piezoelectric-driven mechanisms have several advantages like high stiffness, rapid response, and good resolution. Therefore, they are widely used for many micro/nano trajectory-tracking applications. However, the existence of the hysteretic nonlinearity behavior makes it challenging to use in practice. In addition, the hysteresis changes with frequency and is dependent on environmental parameters like temperature and load. Finding a method that can track both continuous periodic and nonperiodic motion under wide frequency range with high precision is nontrivial. In this study, a feedforward-feedback control strategy is proposed to bridge this gap, where a direct inverse rate-dependent Prandtl–Ishlinskii model based on radial basis function neural network to compensate rate-dependent hysteresis and a proportional-integral controller with an inner-loop disturbance observer to further attenuate tracking error (caused by the imperfect modeling, unknown lumped disturbance). The proposed method can perform a wide-bandwidth tracking control of periodic and nonperiodic motion of a piezoelectric-driven mechanism. Experiments are then conducted to demonstrate the capability of the proposed controller. [ABSTRACT FROM AUTHOR]

High parking accuracy, comfort and stability, and fast response speed are important indicators to measure the control performance of a fully automatic operation system. In this paper, aiming at the problem of low accuracy of the fully automatic operation control of urban rail trains, a radial basis function neural network position output-constrained robust adaptive control algorithm based on train operation curve tracking is proposed. Firstly, on the basis of the mechanism of motion mechanics, the nonlinear dynamic model of train motion is established. Then, RBFNN is used to adaptively approximate and compensate for the additional resistance and unknown interference of the train model, and the basic resistance parameter adaptive mechanism is introduced to enhance the anti-interference ability and adaptability of the control system. Lastly, on the basis of the RBFNN position output-constrained robust adaptive control technology, the train can track the desired operation curve, thereby achieving the smooth operation between stations and accurate stopping. The simulation results show that the position output-constrained robust adaptive control algorithm based on RBFNN has good robustness and adaptability. In the case of system parameter uncertainty and external disturbance, the control system can ensure high-precision control and improve the ride comfort. [ABSTRACT FROM AUTHOR]