Jia Ma

Robust backstepping control of active vibration isolation using a stewart platform

This paper focuses on deriving a robust backstepping control approach to solve the active vibration isolation problem using a Stewart platform. The dynamics of the Stewart platform driven by the linear voice coil motors is developed with the Newton-Euler method. By fully considering the characteristics of vibration isolation, the properties of the dynamics of the Stewart platform are applied to transform the coupled dynamics into six independent single-input single-output (SISO) channels. Furthermore, in the procedure of controller design, the influence factors of vibration isolation are taken into account, such as the parameter perturbation and the unmodeled dynamics, etc. Meanwhile, high-gain design method is employed to deal with the problem introduced by input unmodeled dynamics of the system. It is demonstrated that a sufficiently small L2 gain from disturbance to output can be obtained in Lyapunov synthesis. The simulation results show that the controller can effectively attenuate low frequency vibrations in six degrees of freedom (DOFs) and a satisfactory vibration isolation performance can be achieved.

Nonlinear robust control method for active vibration isolation using a Stewart platform

This paper focuses on developing a nonlinear robust controller to solve the active vibration isolation problem using a Stewart platform. The dynamics of the Stewart platform driven by voice coil actuators is derived by the Newton-Euler method. The influence factors of vibration isolation are taken into account, such as the nonlinear characteristics of the dynamic model, the parameter perturbation and the unmodeled dynamics, etc. The favorable feature of the proposed controller is that it is not necessary for the upper bound of the unmatched uncertainties to be known in advance. A tuning rule is designed to deal with the estimation problem of the uncertainties. The uniformly ultimately bounded (UUB) stability of the controller is demonstrated by applying the Lyapunov approach and a UUB lemma. The simulation results illustrate that the controller can effectively attenuate low frequency vibrations in all six degrees of freedom (DOFs) and the satisfactory vibration isolation performance can be achieved.

A Multi-agent Architecture Based Cooperation and Intelligent Decision Making Method for Multirobot Systems

The design of a hybrid multi-agent architecture is proposed for multirobot systems. Analysis of the architecture shows that it is suitable for multirobot systems dealing with changing environments. Meanwhile, it is capable of controlling a group of robots to accomplish multiple tasks simultaneously. Two associated issues about the architecture are cooperation between robots and intelligent decision making. Ability vector, cost function and reward function are used as criteria to describe and solve the role assignment problem in multirobot cooperation. A solution of information fusion based on RBF neural networks is applied to solve the reality problem in decision making of multirobot systems. And an experiment about robot soccer shooting is designed. The experimental results verify that the method can improve the whole decision system in accuracy.

Adaptive neural network controller of a stewart platform with unknown dynamics for active vibration isolation

To solve the control problem of a Stewart platform with unknown dynamics for multiple degree-of-freedom(DOF) active vibration isolation, an adaptive radial basis function neural network(RBFNN) controller is developed. The RBFNN is employed to approximate the unknown dynamics of the system. And an on-line tuning rule for the parameters of the RBFNN is given based on the e1-modification and gradient algorithms. Meanwhile, a sliding mode control term is incorporated to further improve the robustness of the whole controller against external vibrations. In the presence of bounded vibrations, the uniformly ultimately boundedness of the filter error and the estimation errors of the RBFNN parameters can be guaranteed by the Lyapunov theory. Finally, simulation results demonstrate the proposed controller can effectively attenuate external low frequency vibrations in all six DOF.

Neural network disturbance observer based controller of an electrically driven stewart platform using backstepping for active vibration isolation

In this paper, a radial basis function disturbance observer (RBFDO) based controller is developed to solve the control problem of an electrically driven Stewart platform for multiple degree-of-freedom (DOF) active vibration isolation. The RBFDOs are employed to monitor the modeling errors and external disturbances, etc. And on-line tuning rules for updating the weights of the RBFDOs are designed based on the e1-modification algorithm. Meanwhile, by considering the dynamics of the Stewart platform and its voice coil actuators, the developed RBFDOs are integrated with the backstepping method to design the active vibration isolation controller. In the presence of external vibrations and model uncertainties, the uniformly ultimately boundedness of the stabilization errors and the weight estimation errors can be guaranteed by the Lyapunov theory. Finally, simulation results demonstrate the proposed controller can effectively attenuate external low-frequency vibrations in six DOFs.

Neurodynamic programming: a case study of the traveling salesman problem

The paper focuses on the study of solving the large-scale traveling salesman problem (TSP) based on neurodynamic programming. From this perspective, two methods, temporal difference learning and approximate Sarsa, are presented in detail. In essence, both of them try to learn an appropriate evaluation function on the basis of a finite amount of experience. To evaluate their performances, some computational experiments on both the Euclidean and asymmetric TSP instances are conducted. In contrast with the large size of the state space, only a few training sets have been used to obtain the initial results. Hence, the results are acceptable and encouraging in comparisons with some classical algorithms, and further study of this kind of methods, as well as applications in combinatorial optimization problems, is worth investigating.

Heuristic dynamic programming for semi-active control of vibration isolation

Vibration isolation controllers are used to suppress undesirable disturbance in order to guarantee better performance in many industrial and scientific domains. To overcome the drawbacks of the conventional passive systems, a novel design based on the action dependent heuristic dynamic programming (ADHDP) is addressed in this paper for the semi-active vibration isolator. ADHDP, derived from dynamic programming, is adopted in the design of a nonlinear optimal vibration controller. This approach is the simplest category of adaptive critic designs (ACDs) which are very efficient to solve a class of nonlinear optimal control problems. Only two subnetworks are involved in ADHDP, namely the action network and the critic network. Least mean square (LMS) algorithm with variable learning rate is applied to the adaptation of these two networks. A single-stage training process is also demonstrated as a useful training strategy. Two types of vibrations are utilized to verify the effectiveness of this control design. Simulation results show that the semi-active vibration isolator is able to reduce the influence of the vibration excitation to the payload significantly in comparison with the passive system.

Sonar Feature Map Building for a Mobile Robot

This paper presents an approach for sonar feature map building. The approach is composed of extracting features at the data-level fusion stage and fusing the extracted features with the registered features in the map at the feature-level fusion stage. A data-level fusion model, termed three measurements association model (TMAM), has been developed for associating three measurements with a line or a point feature. By use of TMAM, different sets of measurements obtained from a single sonar sensor at consecutive steps are associated with the line and point features. Subsequently, the parameters of the identified features are estimated by use of the iterated least square estimation method. Finally, when a feature is extracted, a simple feature-level fusion strategy is used to update the map. The proposed approach has been tested both in simulation and on real data.