Trajectory Tracking Control Design for Dual-Arm Robots Using Dynamic Surface Controller

This paper presents a dynamic surface controller (DSC) for dual-arm robots (DAR) tracking desired trajectories. The DSC algorithm is based on backstepping technique and multiple sliding surface control principle, but with an important addition. In the design of DSC, low-pass filters are included which prevent the complexity in computing due to the “explosion of terms”, i.e. the number of terms in the control law rapidly gets out of hand. Therefore, a controller constructed from this algorithm is simulated on a four degrees of freedom (DOF) dual-arm robot with a complex kinetic dynamic model. Moreover, the stability of the control system is proved by using Lyapunov theory. The simulation results show the effectiveness of the controller which provide precise tracking performance of the manipulator

Nonlinear Dynamic Surface Control of Chaos in Permanent Magnet Synchronous Motor Based on the Minimum Weights of RBF Neural Network

This paper is concerned with the problem of the nonlinear dynamic surface control (DSC) of chaos based on the minimum weights of RBF neural network for the permanent magnet synchronous motor system (PMSM) wherein the unknown parameters, disturbances, and chaos are presented. RBF neural network is used to approximate the nonlinearities and an adaptive law is employed to estimate unknown parameters. Then, a simple and effective controller is designed by introducing dynamic surface control technique on the basis of first-order filters. Asymptotically tracking stability in the sense of uniformly ultimate boundedness is achieved in a short time. Finally, the performance of the proposed controller is testified through simulation results

Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs) be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP) technique, prescribed performance control, and a kind of smooth switching strategy into back-stepping design, a new composite switching adaptive neural prescribed performance control scheme is proposed and a new type of adaptive laws is constructed for the altitude subsystem. Compared with previous neural control scheme for flight vehicle, the remarkable feature is that the proposed controller not only achieves the prescribed performance including transient and steady property but also addresses the constraint on NN. Two comparative simulations are presented to verify the effectiveness of the proposed controller

Design of UDE-based dynamic surface control for dynamic positioning of vessels with complex disturbances and input constraints

In practice, dynamic positioning (DP) vessels are subjected to complex disturbances as well as the magnitude and changing rate constraints of the thrusts and moments. This study applied a dynamic surface controller based on an uncertainty and disturbance estimator (UDE) to a DP vessel with complex disturbances and input constraints. The UDE was designed to estimate and handle the complex disturbances. An auxiliary dynamic system (ADS) and smooth switching function were employed to compensate for the input constraints and avoid the singularity phenomenon caused by the ADS, respectively. The combination of the UDE method and dynamic surface control (DSC) technology significantly simplified the design process for the control law and increased the practicability for DP vessels. The stability of the proposed control law was proved using the Lyapunov theory. The effectiveness of the control law and possibility of actually applying it to a DP vessel were verified using simulation experiments

Modeling and Identification of Podded Propulsion Unmanned Surface Vehicle and Its Course Control Research

The response model of podded propulsion unmanned surface vehicle (USV) is established and identified; then considering the USV has characteristic of high speed, the course controller with fast convergence speed is proposed. The idea of MMG separate modeling is used to establish three-DOF planar motion model of the podded propulsion USV, and then the model is simplified as a response model. Then based on field experiments, the parameters of the response model are obtained by the method of system identification. Unlike ordinary ships, USV has the advantages of fast speed and small size, so the controller needs fast convergence speed and strong robustness. Based on the theory of multimode control, a fast nonsingular terminal sliding mode (FNTSM) course controller is proposed. In order to reduce the chattering of system, disturbance observer is used to compensate the disturbance to reduce the control gain and RBF neural network is applied to approximate the symbolic function. At the same time, fuzzy algorithm is employed to realize the mode soft switching, which avoids the unnecessary chattering when the mode is switched. Finally the rapidity and robustness of the proposed control approach are demonstrated by simulations and comparison studies

Adaptive Fuzzy Dynamic Surface Sliding Mode Position Control for a Robot Manipulator with Friction and Deadzone

Precise tracking positioning performance in the presence of both the deadzone and friction of a robot manipulator actuator is difficult to achieve by traditional control methodology without proper nonlinear compensation schemes. In this paper, we present a dynamic surface sliding mode control scheme combined with an adaptive fuzzy system, state observer, and parameter estimator to estimate the uncertainty, friction, and deadzone nonlinearities of a robot manipulator system. We design a dynamic surface sliding mode basic controller by systematic recursive design steps that yields several adaptive laws for the compensation of nonlinear friction, deadzone, and other unknown nonlinear dynamics. The boundedness and convergence of this closed-loop system are guaranteed by the Lyapunov stability theorem. Experiments on the Scorbot robot manipulator demonstrate the validity and effectiveness of the proposed control scheme