Adaptive non-singular fast terminal sliding mode based on prescribed performance for robot manipulators

A fast convergent non-singular terminal sliding mode adaptive control law based on prescribed performance is formulated to solve the uncertainties and external disturbances of robot manipulators. First, the tracking error of robot manipulators is transformed by using the prescribed performance function, which improves the transient behaviors and steady-state accuracy of robot manipulators. Then, a novel fast convergent non-singular terminal sliding mode surface is brought up according to the transformed error, and the control law is derived to meet the stability requirements of robot manipulators. In practice, the upper boundary of the lumped disturbances cannot be accurately obtained. Therefore, an adaptive prescribed performance control (PPC) controller to lumped disturbances is brought up to ensure the stability and finite-time convergence of robot manipulators. Finally, the system stability of robot manipulators is proved by the Lyapunov theorem. Simulation results and comparative analysis demonstrate the superiority and robustness of the raised strategy.     

Model-based robust control design and experimental validation of collaborative industrial robot system with uncertainty

Based on the traditional PID control and robust control algorithm, a novel practical robust control method is designed for the 6-DOF collaborative industrial robot with uncertainty. The proposed algorithm consists of a robust term and a model-based PD control term, which we call MPDP controller. It is demonstrated by Lyapunov theoretical analysis that the algorithm is able to guarantee uniform boundedness and uniform ultimate boundedness of the system. Simulations and experiments show the good performance of MPDP control in a robot with smaller steady-state tracking errors and better robustness compared to PID controllers.     

Quasi-min-max MPC for visual servoing stabilization of omnidirectional wheeled mobile robots

This paper proposes a new visual servoing quasi-min-max MPC algorithm for stabilization control of an omnidirectional wheeled mobile robot subject to physical and visual constraints. The visual servoing dynamics of the robot are modeled as the state-dependent linear error system with nonlinear control inputs of rotation and deflection velocities of wheels. The state-dependent linear error system is covered as linear parameters-varying models which is used to design the visual servoing quasi-min-max MPC controller. The actual control inputs of the robot are then computed by the solution of an inverse algebraic equation of the MPC actions. The recursive feasibility and stability of the new visual servoing MPC are ensured by some LMIs conditions. The performance and practicability of the visual servoing MPC are verified by some simulation and experiment results.     

Wavelet CMAC for MIMO uncertain nonlinear systems using a modified social ski-driver algorithm

This study uses a Mexican hat wavelet membership function for a cerebellar model articulation controller (CMAC) to develop a more efficient adaptive controller for multiple input multiple output (MIMO) uncertain nonlinear systems. The main controller is called the adaptive Mexican hat wavelet CMAC (MWCMAC), and an auxiliary controller is used to remove the residual error. For the MWCMAC, the online learning laws are derived from the gradient descent method. In addition, the learning rate values are very important and have a great impact on the performance of the control system; however, they are difficult to choose accurately. Therefore, a modified social ski driver (SSD) algorithm is proposed to find optimal learning rates for the control parameters. Finally, a magnetic ball levitation system and a nine-link biped robot are used to illustrate the effectiveness of the proposed SSD-based MWCMAC control system. The comparisons with other existing control algorithms have shown the superiority of the proposed control system.     

Design and analysis of turnover platform for welding robot北大核心CSCD

A turnover platform for welding robot was designed for the application of welding robot with lower accuracy requirement, which was of low cost and higher position accuracy. In this turnover platform, the pneumatic motor was used as the power output, and the indexing mechanism with high accuracy was the transmission system with high transmitting ratio based on worm and wheel. The position information was acquired by using the photoelectric encoder, and the turnover motion with high accuracy was realized through the closed-loop controller. Simulation results showed that the maximum speed of the welding platform approached 14 r/min, and the platform could meet the requirements of most welding products. Such a turnover platform can offer the application program of the welding robot with low cost for the middle and low level products, and reduce the cost of welding robot and improve welding productivity.     

压电谐振驱动三足机器人的平面运动

设计并实现了一类利用压电陶瓷片作动,由三条曲梁足支撑的振动驱动机器人.建立了在一条足共振驱动下机器人水平运动的动力学方程,数值计算解释了摩擦作用下的运动机理,寻找到异性摩擦对运动方向、速度的影响和压电激励频率与运动速度间的关系.通过建立圆弧曲梁控制方程求解圆弧型足面内振动的固有频率及振型,设计了三组不同频率的圆弧曲梁足参数,实验制作了机器人模型,利用压电控制三足间振动的共振切换,实现了预想的三个方向的运动以达到平面运动的效果,实验测量了机器人的运动速度与理论计算吻合得较好.     

PAC Learning of One-Dimensional Patterns

Developing the ability to recognize a landmark from a visual image of a robot's current location is a fundamental problem in robotics. We consider the problem of PAC-learning the concept class of geometric patterns where the target geometric pattern is a configuration ofk points on the real line. Each instance is a configuration ofn points on the real line, where it is labeled according to whether or not it visually resembles the target pattern. To capture the notion of visual resemblance we use the Hausdorff metric. Informally, two geometric patternsP andQ resemble each other under the Hausdorff metric if every point on one pattern is close to some point on the other pattern. We relate the concept class of geometric patterns to the landmark matching problem and then present a polynomial-time algorithm that PAC-learns the class of one-dimensional geometric patterns. We also present some experimental results on how our algorithm performs.     

微小气动机器人移动FSM建模与控制

根据仿生尺蠖运动机理研制了一种用于人体腔道微创诊查的微小机器人系统,该机器人系统由前支撑单元、后支撑单元和具有3个气室的橡胶驱动器三部分组成.根据微型机器人的本体结构分析了机器人的移动控制原理,给出了在一个运动循环周期内机器人移动一个步距的运动状态和控制时序.阐述了有限状态机原理,并基于有限状态机原理建立了该机器人移动...     

灵长类仿生机器人悬臂运动仿生控制综述

灵长类仿生机器人是通过智能机械手段模仿灵长类运动的一类机器人,针对其悬臂运动仿生的控制研究是该领域的热点.综述了目前灵长类仿生机器人悬臂运动仿生控制的研究方法,给出了悬臂运动仿生控制的一般方法与基于"动态伺服"理论的悬臂运动仿生控制策略,提出了悬臂运动仿生控制中亟待解决的若干问题,并对今后灵长类仿生机器人悬臂运动仿生控...     

Bionic autonomous learning control of a two-wheeled self-balancing flexible robot

This paper presents an OCPA (operant conditioning probabilistic automaton) bionic autonomous learning system based on Skinner’s operant conditioning theory for solving the balance control problem of a two-wheeled flexible robot. The OCPA learning system consists of two stages: in the first stage, an operant action is selected stochastically from a set of operant actions and then used as the input of the control system; in the second stage, the learning system gathers the orientation information of the system and uses it for optimization until achieves control target. At the same time, the size of the operant action set can be automatically reduced during the learning process for avoiding little probability event. Theory analysis is made for the designed OCPA learning system in the paper, which theoretically proves the convergence of operant conditioning learning mechanism in OCPA learning system, namely the operant action entropy will converge to minimum with the learning process. And then OCPA learning system is applied to posture balanced control of two-wheeled flexible self-balanced robots. Robot does not have posutre balanced skill in initial state and the selecting probability of each operant in operant sets is equal. With the learning proceeding, the selected probabilities of optimal operant gradually tend to one and the operant action entropy gradually tends to minimum, and so robot gradually learned the posture balanced skill.