Keping Liu

Modeling and variable structure control of a vehicle flexible manipulator

In this paper, the mathematical modeling and the application of a new trajectory tracking control technique for hydraulic-driven rigid-flexible manipulator are concerned. To get a closer dynamic behavior of the real system, both the flexible manipulator linkage and the actuator dynamics are considered. The exact dynamic model of flexible manipulator is derived using Lagrange principle and assumed modes method. The partial decoupled dynamic equation is derived using nonlinear decoupling feedback control method. The whole dynamic model is established by a driven Jacobin matrix, which represents the coupling between hydraulic servo system and mechanical system. A variable structure controller with inverse dynamics is designed for trajectory tracking. To weaken the chattering of control signal, saturation function is used to instead of sign function. The experimental results investigate the effectiveness of the proposed approaches.

A Combined Backstepping Terminal Sliding Mode Algorithm Based Decentralized Control Scheme for Reconfigurable Manipulators

A decentralized control scheme based on a combined backstepping terminal sliding mode algorithm for reconfigurable manipulators is proposed. Based on Lyapunov stability theory, backstepping technique and terminal sliding mode are utilized in the first and second order of the subsystem respectively, the unknown terms and interconnection term are approximated or compensated by neural networks whose weights are updated with adaptive laws. For the serious chatter of the controller with the linear sliding mode, the terminal sliding mode replaced the linear sliding mode. In contrast, the method improves the convergence rate and the tracking accuracy, the control signal is smoother. Finally, the simulation results show the effectiveness of proposed scheme for different configurations with no need to modify any parameters.

Decentralized Control of Harmonic Drive Based Modular Robot Manipulator using only Position Measurements: Theory and Experimental Verification

This paper presents a decentralized control approach of harmonic drive (HD) based modular robot manipulator (MRM) using only position measurements. Unlike known methods that rely on joint torque and velocity sensing, this paper addresses the problem of controlling HD based MRM using only position measurements on the motor and link sides of each module. The dynamic model of HD based MRM is formulated by employing a high-fidelity HD model and the velocity of each robot joint is estimated based on a novel nonlinear velocity estimator. With only local information on each module, a decentralized integral sliding mode controller (ISMC) is designed based on variable gain super-twisting algorithm (VGSTA) to compensate the model uncertainty and to reduce the chattering effect of the controller. The asymptotic stability of the closed-loop system is proved using the Lyapunov theory. Finally, experiments are performed for a 3-DOF MRM to verify the advantage of the proposed method.

Decentralized control for harmonic drive–based modular and reconfigurable robots with uncertain environment contact

In this article, a decentralized control strategy is presented for harmonic drive–based modular and reconfigurable robots with uncertain environment contact. Unlike conventional methods that rely on robot–environment contact model or force/torque sensing, this article addresses the problem of controlling modular and reconfigurable robots in contact with uncertain environment using only encoder data of each joint module. By employing a control-oriented harmonic drive model, the dynamic model of modular and reconfigurable robot is formulated as a synthesis of interconnected subsystems, in which the interconnected joint couplings are with small magnitudes. Based on the integral sliding mode control technique and the adaptive super-twisting algorithm, the decentralized controller is designed to compensate model uncertainty in which the up-bound is unknown. The stability of the modular and reconfigurable robot system is proved using Lyapunov theory. Finally, simulations are conducted for 2-degree-of-freedom modular and reconfigurable robots with different configurations under the situations of dynamic contact and collision to investigate the advantage of the proposed approach.

Robust control of a two-link flexible manipulator with neural networks based quasi-static deflection compensation

A robust control method of a two-link flexible manipulator with neural networks based quasi-static distortion compensation is proposed and experimentally investigated. The dynamics equation of the flexible manipulator is divided into a slow subsystem and a fast subsystem based on the assumed mode method and singular perturbation theory. A decomposition based robust controller is proposed with respect to the slow subsystem, and H/sup /spl infin// control is applied to the fast subsystem represented by the elastic mode . The overall closed loop control is determined by the composite algorithm that combines the two control laws. Furthermore, a neural network compensation scheme is also integrated into the control system to compensate for quasi-static deflection. The proposed control method has been implemented on a two-link flexible manipulator for precise end-tip tracking control. Experimental results are presented in this paper along with discussions.

Terminal sliding mode control with active disturbance reject for spacecraft trajectory tracking

For the purpose of probe soft landing on small bodies safely, this paper focuses on improving convergence speed, decrease the chattering and process uncertainties and perturbations from the trajectory tracking control system. This paper presents improved Terminal sliding mode control based on active disturbance reject, which changes terminal sliding mode surface to the sliding mode hyper-surface and apply Active Disturbance Reject Controller (ADRC) to estimation and compensation uncertainties and perturbations, respectively, in the case of the asteroid irregular shape and low gravity. The proposed algorithm can fast and accurately track the planned trajectory in the finite time and is robust to parameter uncertainty, feedback state error and external disturbances. Finally simulation experiments are carried out to demonstrate the effectiveness of the proposed control law.

Average dwell time approach to H∞ filter for continuous-time switched linear parameter varying systems with time-varying delay

Considering two types of delays including both time-varying delay and parameter varying delay in continuous switched linear parameter varying systems, the problem of H ∞ filtering under average dwell time switching is illustrated. The H ∞ filter depending on the linear time-varying parameter ρ ( t ) (mode-dependent parameterized filter) is designed at first. Then, based on multiple Lyapunov function and an improved reciprocally convex inequality, the corresponding existence sufficient conditions for the filter could ensure the obtained filter error system exponentially stable with a guaranteed H ∞ performance in the form of linear matrix inequalities. In addition, the designed filter gains under allowed switching signals are computed via the proposed convex optimal algorithm. In the end, two numerical examples show the effectiveness of the results in this work.

Decentralized robust optimal control for modular robot manipulators via critic-identifier structure-based adaptive dynamic programming

This paper presents a decentralized robust optimal control method for modular robot manipulators (MRMs) via a critic-identifier structure-based adaptive dynamic programming (ADP) scheme. The robust control problem of MRMs is transformed into an optimal compensation control issue, which consists of model-based compensation control, identifier-based learning control and ADP-based optimal control. The dynamic model of MRMs is deployed for each joint module where the local dynamic information is utilized to design the model compensation controller. A neural network (NN) identifier is established to approximate the interconnected dynamic coupling. Based on ADP and local online policy iteration algorithm, the Hamiltonian–Jacobi–Bellman equation is solved by constructing a critic NN, and then the approximate optimal control policy derivation is possible. The closed-loop robotic system is asymptotic stable by the implementation of a set of developed decentralized control policies. Simulations are presented to demonstrate the effectiveness of the proposed method.

Double-power reaching law sliding mode control for spacecraft decline based on radial basis function networks

In order to solve the problem of position and velocity control of asteroid detectors in the weak gravitational field, a double power sliding mode optimal control method based on radial basis network is proposed in this paper. The dynamic model of the detector in the asteroid fixed coordinate system and the detector landing point coordinate system is analyzed in detail. In the process of the detector descending, the double-power reaching law is adopted to reduce the time of convergence close in the slide surface and slow down the chattering of the system. Considering the influence of uncertainties and perturbations on the system, the radial basis network is used to compensate the influence of the uncertain and disturbances in the descent process of the detector. Completed the requirements of the position and speed control in the process of detector descending which prepared for the probe landing. The simulation results show the effectiveness and feasibility of the proposed method.

Torque sensorless decentralized neuro-optimal control for modular and reconfigurable robots with uncertain environments

Abstract A technical challenge of addressing the decentralized optimal control problem for modular and reconfigurable robots (MRRs) during environmental contacts is associated with optimal compensation of the uncertain contact force without using force/torque sensors. In this paper, a decentralized control approach is presented for torque sensorless MRRs in contact with uncertain environment via an adaptive dynamic programming (ADP)-based neuro-optimal compensation strategy. The dynamic model of the MRRs is formulated based on a novel joint torque estimation method, which is deployed for each joint model, and the joint dynamic information is utilized effectively to design the feedback controllers, thus making the decentralized optimal control problem of the environmental contacted MRR systems be formulated as an optimal compensation issue of model uncertainty. By using the ADP method, a local online policy iteration algorithm is employed to solve the Hamilton–Jacobi–Bellman (HJB) equation with a modified cost function, which is approximated by constructing a critic neural network, and then the approximate optimal control policy can be derived. The asymptotic stability of the closed-loop MRR system is proved by using the Lyapunov theory. At last, simulations and experiments are performed to verify the effectiveness of the proposed method.