Bin Li

Bio: Bin Li is an academic researcher from Qilu University of Technology. The author has contributed to research in topics: Artificial neural network & Nonlinear system. The author has an hindex of 10, co-authored 44 publications receiving 425 citations. Previous affiliations of Bin Li include Shandong University.

Design and simulation for a hydraulic actuated quadruped robot

Abstract: This paper describes the mechanical configuration of a quadruped robot firstly. Each of the four legs consists of three rotary joints. All joints of the robot are actuated by linear hydraulic servo cylinders. Then it deduces the forward and inverse kinematic equations for four legs with D-H transformation matrices. Furthermore, it gives a composite foot trajectory composed of cubic curve and straight line, which greatly reduces the velocity and acceleration fluctuations of the torso along forward and vertical directions. Finally, dynamics cosimulation is given with MSC.ADAMS and MATLAB. The results of co-simulation provide important guidance to mechanism design and parameters preference for the linear hydraulic servo cylinders.

Research of mammal bionic quadruped robots: A review

Abstract: This paper focuses on the mammal bionic quadruped robots. The main challenge in this field is how to design the highly dynamical and high payload quadruped robots. This paper firstly introduces the history of bionic quadruped robots, particularly the landmark quadruped robots. Then the state-of-the art of drive mode for quadruped robots is reviewed. Subsequently, the development trend of quadruped robots is described. Based on the state-of-the art of quadruped robots, the technical difficulties of bionic quadruped robots are briefly reviewed. And the hydraulic quadruped robot developed in Shandong University is introduced. Finally, the summary and future work of the quadruped robots is given.

Optimized Formation Control Using Simplified Reinforcement Learning for a Class of Multiagent Systems With Unknown Dynamics

Abstract: The article proposes an optimized leader-follower formation control using a simplified reinforcement learning (RL) of identifier–critic–actor architecture for a class of nonlinear multiagent systems. In general, optimal control is expected to be obtained by solving Hamilton–Jacobi–Bellman (HJB) equation, but the equation associated with a nonlinear system is difficult to solve by analytical method. Although the difficulty can be effectively overcome by the RL strategy, the existing RL algorithms are very complex because their updating laws are obtained by carrying out gradient descent algorithm to square of the approximated HJB equation (Bellman residual error). For a multiagent system, due to the state coupling problem, these methods will become difficult implementation. In the proposed optimized scheme, the RL updating laws are derived from negative gradient of a simple positive function, which is the equivalence to HJB equation; therefore, the control algorithm is significantly simple. Furthermore, in order to solve the problem of unknown system dynamics, an adaptive identifier is integrated into the control. Finally, the theory and simulation demonstrate that the optimized formation scheme can guarantee the desired control performance.

The extreme learning machine learning algorithm with tunable activation function

Abstract: In this paper, we propose an extreme learning machine (ELM) with tunable activation function (TAF-ELM) learning algorithm, which determines its activation functions dynamically by means of the differential evolution algorithm based on the input data. The main objective is to overcome the problem dependence of fixed slop of the activation function in ELM. We mainly considered the issue of processing of benchmark problems on function approximation and pattern classification. Compared with ELM and E-ELM learning algorithms with the same network size or compact network configuration, the proposed algorithm has improved generalization performance with good accuracy. In addition, the proposed algorithm also has very good performance in the TAF neural networks learning algorithms.

Consensus Control for Networked Manipulators With Switched Parameters and Topologies

Abstract: To solve abruptly occurrence of parameters jumping and directed communication topologies changing in the control process of networked manipulators, in this paper, distributed switched consensus control algorithms are formulated for a group of robot manipulators in realizing cooperative consensus performance. In fact, networked Lagrange systems are modeled as switched systems regarding the different parameters and topologies. Namely, the dynamic models switch when the system parameters or the topology structures change. The consensus control strategy is constructed by resorting to (improved) average dwell time (ADT) method and sliding-mode control technique, and a unified analysis methodology is developed to perform the convergence analysis for the closed-loop system by Lyapunov stable theory. The main contribution of this paper is the development of a systematically adaptive consensus algorithm by simultaneously considering shifting parameters and switching communication network (as two unavoidable key factors) in the process of communication interaction among robots. A distinctive feature of the developed consensus protocol is to introduce the directed network topology characterizing the local communication interaction among robots, which is especially suitable for representing the the structures and features of the realistic cooperative multi-robotic systems. Accordingly, the developed consensus tracking strategy for manipulators possess prominent advantages including robustness,stability and effectiveness over the existing concentrated on single robot counterparts. Finally, numerical simulations of two-link manipulators are performed to illustrate the effectiveness of the obtained control algorithm.

Hydraulic Control Systems

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Adaptive Locomition of a Multilegged Robot over Rough Terrain

Abstract: Although the off-road mobility characteristics of wheeled or tracked vehicles are generally recognized as being inferior to those of man and cursorial animals, the complexity of the joint-coordination control problem has thus far frustrated attempts to achieve improved vehicular terrain adaptability through the application of legged locomotion concepts. Nevertheless, the evident superiority of biological systems in this regard has motivated a number of theoretical studies over the past decade which have now reached a state of maturity sufficient to permit the construction of experimental computer-controlled adaptive walking machines. At least two such vehicles are known to have recently demonstrated legged locomotion over smooth hard-surfaced terrain. This paper is concerned with an extension of the present theory of limb coordination for such machines to the case in which the terrain includes regions not suitable for weight-bearing and which must consequently be avoided by the control computer in deciding when and where to successively place the feet of the vehicle. The paper includes a complete problem formalization, a heuristic algorithm for solution of the problem thus posed, and a preliminary evaluation of the proposed algorithm in terms of a computer simulation study.