Reducing a class of polygonal path tracking to straight line tracking via nonlinear strip-wise affine transformation

In this paper a piecewise linear homeomorphism is presented that maps a strictly monotone polygonal chain to a straight line. This mapping enables one to reduce the path tracking task for mobile robots to straight line tracking. Due to the simplicity of the transformation, closed form solutions for the direct and inverse mapping are presented. Furthermore, the transformation also defines a feedback equivalence relation between the original and the transformed system equations of the mobile robot. It is shown that the form of the system equations is preserved and that the transformation essentially maps a car-like robot in the original domain, to a car-like robot in the transformed domain. This enables one to use straight line trackers developed solely for this system, for the tracking of arbitrary strictly monotone polygonal curves. Finally, it is shown that the use of this mapping can also simplify the application of existing path tracking controllers since they only need to track straight line paths. In general, one can eliminate from the existing path controllers all parameters that are needed for non-straight paths, thus obtaining respective simplified controllers. For example, it is shown that a fuzzy path controller with 135 rules can be reduced to an equivalent fuzzy straight line tracking controller with 45 rules.     

一种基于小脑模型关节控制器评论-策略家的机器人跟踪控制算法

本文提出了一种基于小脑模型关节控制器(CMAC)的评论–策略家算法,设计不依赖模型的跟踪控制器,来解决机器人的跟踪问题.该跟踪控制器包含位置控制器和角度控制器,其输出分别为线速度和角速度.位置控制器由评价单元和策略单元组成,每个单元都采用CMAC算法,按改进δ学习规则在线调整权值.策略单元产生控制量;评判单元在线调整策略单元学习速率.以双轮驱动自主移动机器人为例,与固定学习速率CMAC做比较,仿真数据表明,基于CMAC的评论–策略家算法的跟踪控制器具有跟踪速度快,自适应能力强,配置参数范围宽,不依赖数学模型等特点.     

基于线性二次型算法的轮式移动机器人轨迹跟踪控制

文章在控制输入饱和约束条件下,以非完整移动机器人的运动学模型为对象,研究了移动机器人的轨迹跟踪问题.首先在参考轨迹处对运动学模型进行线性化得到移动机器人线性时变系统,证明了其能观性和能控性,在此基础上设计了饱和约束条件的分段线性二次型控制器(Piecewise Linear QuadraticRegulator,PLQR),并基于Lyapunov方法证明了其稳定性.在MATLAB软件平台下的仿真和实验结果表明,基于PLQR的轮式移动机器人对不同初始位姿及不同的参考轨迹都有较好的跟踪效果,且能够避免控制律跳变现象,满足饱和约束条件.     

Fuzzy-neuro position/force control for robotic manipulators with uncertainties

The performance of a controller for robot force tracking is affected by the uncertainties in both the robot dynamic model and the environmental stiffness. This paper aims to improve the controller’s robustness by applying the neural network to compensate for the uncertainties of the robot model at the input trajectory level rather than at the joint torque level. A self-adaptive fuzzy controller is introduced for robotic manipulator position/force control. Simulation results based on a two-degrees of freedom robot show that highly robust position/force tracking can be achieved, despite the existence of large uncertainties in the robot model.     

Trajectory tracking optimization of mobile robot using artificial immune system

In this paper, an optimization method that provides quick response using artificial immune system, is proposed and applied to a mobile robot for trajectory tracking. The study focuses on the immune theory to derive a quick optimization method that puts emphasis on immunity feedback using memory cells by the expansion and suppression of the test group rather than to derive a specific mathematical model of the artificial immune system. Various trajectories were selected in mobile environment to evaluate the performance of the proposed artificial immune system. The global inputs to the mobile robot are reference position and reference velocity, which are time variables. The global output of mobile robot is a current position. The tracking controller makes position error to be converged to zero. In order to reduce position error, compensation velocities on the track of trajectory are necessary. Input variables of fuzzy are position errors in every sampling time. The output values of fuzzy are compensation velocities. Immune algorithm is implemented to adjust the scaling factor of fuzzy automatically. The results of the computer simulation proved the system to be efficient and effective for tracing the trajectory to the final destination by the mobile robot.

     

A system decomposition method for region tracking control of a non-holonomic mobile robot with dynamic parameter uncertainties

The tracking control problem of non-holonomic mobile robot systems has been extensively investigated in the past decades, however, most of the existing control strategies were developed specifically for the fixed-point tracking. This technical note focuses on the region tracking control for a non-holonomic mobile robot system with parameter uncertainties in the robot dynamics. With the system decomposition and adaptive control method, some restrictions imposed on the angular and linear velocities of the non-holonomic mobile robot in recent literature are removed, enabling to track dynamic trajectories with any values of the angular and line velocities. The proposed adaptive control scheme can simultaneously solve both the regulation and region tracking problems of a non-holonomic mobile robot with one passive wheel and two actuated wheels. By utilizing the designed control laws, the mobile robot system is able to globally reach inside a moving region specified by potential functions whose path can be a circular curve, a straight line, or sinusoidal curve, by using a single adaptive controller. Since the dynamic region can be specified arbitrarily small, the fixed-point tracking can be regarded as a special case of region tracking studied in this paper. Compared with the traditional fixed-point tracking, region tracking has more flexibility and better robustness. Numerical results are presented to show the effectiveness of the designed strategy.     

信息融合在移动机器人轨迹模糊跟踪中的应用

在机器人轨迹跟踪过程中,机器人自动跟踪的精度直接影响跟踪效果;以3自由度移动机器人为研究对象研究了机器人轨迹模糊跟踪系统,且在该系统中,采用多个传感器同时对移动机器人进行跟踪检测,并利用融合算法对其进行融合,将融合后的结果作为模糊控制器的输入;计算机仿真结果表明,在3自由度移动机器人轨迹跟踪中,采用多传感器信息融合是合理的、可行的;且可以减少跟踪过程中由传感器引起的误差对跟踪精度的影响,提高控制精度.     

带模糊防滑控制的深海机器人速度预测跟踪

面向深海“稀软底”作业机器人提出一种基于模糊控制的防滑控制方法，并采用带参数自校正模块的变维灰色预测控制器进行速度跟踪控制。防滑控制器能增强深海机器人的行驶速度与沉积物特性匹配，有效抑制作业机器人两侧履带的打滑，保证机器人在极限海底沉积物上安全行走作业。带自校正模块的变维预测速度跟踪算法，与单纯的PID速度跟踪、不带自校正模块的预测速度跟踪、不变维带自校正模块的预测速度跟踪相比，具有更好的速度跟踪性能和自适应性。仿真结果表明了算法的有效性。     

PHANTOM Omni机器人的自适应模糊滑模控制

针对PHANTOM Omni机器人的位置轨迹跟踪问题,采用了一种基于模糊逻辑的自适应模糊滑模控制方案。利用滑模控制中的切换函数作为输入,根据模糊系统的逼近能力设计控制器,并基于李雅谱诺夫方法设计自适应律对控制器所需参数进行实时调节。仿真中将其与传统的滑模控制进行了比较,仿真结果表明：自适应模糊滑模控制能使PHANTOM Omni机器人更好地实现期望的位置轨迹跟踪并有效地减轻抖振现象,从而证明了该方法在PHANTOM Omni机器人上实施的可行性。     

Application of a hybrid controller to a mobile robot

This paper presents the application of a hybrid controller to the optimization of the movement of a mobile robot. Through hybrid controller processes, the optimal angle and velocity of a robot moving in a work space was determined. More effective movement resulted from these hybrid controller processes. The experimental scenarios involved a five-versus-five soccer game and a MATLAB simulation, where the proposed system dynamically assigned the robot to the target position. The hybrid controller was able to choose a better position according to the circumstances encountered. The hybrid controller that is proposed includes a support vector machine and a fuzzy logic controller. We used the method of generalized predictive control to predict the target position, and the support vector machine to determine the optimal angle and velocity required for the mobile robot to reach the goal. First, we used the generalized predictive control to predict the target position. Then, the support vector machine is used to classify the angle that must be followed by the mobile robot to reach the goal. Next, a fuzzy logic controller is designed to determine the velocity of the left and right wheels of the mobile robot. Thus generated, the velocity was optimized according to the measures obtained by the support vector machine. Finally, based on the optimal velocity of robot, the output membership function was modified. Consequently, the proposed hybrid controller allowed the robot to reach the goal quickly and effectively.     

An online reference governor for mobile robot to reduce the occurrence of control input saturation

This study proposed an online reference governor for a mobile robot to reduce the occurrence of control input saturation. For following the trajectory by a mobile robot, it is one of the practical subjects to provide appropriate control reference even if any disturbances occur. We proposed a methodology to regulate the control reference iteratively based on time-scaling approach. The time-scaling approach is a method to realize to regulate time development characteristic on the given trajectory. It is difficult to model the effect of the interaction with the road surface and the trajectory tracking error is appeared as the amount of accumulated such factors. Therefore, it is a practical approach to reduce the occurrence of control input saturation based on the evaluation of the trajectory tracking error. Proposed reference governor realizes online time scaling based on the trajectory tracking error index and a smooth transition dynamics. By introducing the proposed method, the occurrence of control input saturation can be reduced in case of that the disturbances occur. For verification of our proposed method, computer simulations utilizing a stable velocity controller were conducted and the results were discussed.     

Quasi sliding mode‐based single input fuzzy self‐tuning decoupled fuzzy PI control for robot manipulators with uncertainty

This paper presents a robust adaptive control strategy for robot manipulators, based on the coupling of the fuzzy logic control with the so‐called sliding mode control (SMC) approach. The motivation for using SMC in robotics mainly relies on its appreciable features. However, the drawbacks of the conventional SMC, such as chattering effect and required a priori knowledge of the bounds of uncertainties can be destructive. In this paper, these problems are suitably circumvented by adopting a reduced rule base single input fuzzy self tuning decoupled fuzzy proportional integral sliding mode control approach. In this new approach a decoupled fuzzy proportional integral control is used and a reduced rule base single input fuzzy self‐tuning controller as a supervisory fuzzy system is added to adaptively tune the output control gain of the decoupled fuzzy proportional integral control. Moreover, it is proved that the fuzzy control surface of the single‐input fuzzy rule base is very close to the input/output relation of a straight line. Therefore, a varying output gain decoupled fuzzy proportional integral sliding mode control approach using an approximate line equation is then proposed. The stability of the system is guaranteed in the sense of the Lyapunov theorem. Simulations using the dynamic model of a 3DOF planar manipulator with uncertainties show the effectiveness of the approach in high speed trajectory tracking problems. The simulation results that are compared with the results of conventional SMC indicate that the control performance of the robot system is satisfactory and the proposed approach can achieve favorable tracking performance, and it is robust with regard to uncertainties and disturbances. Copyright © 2011 John Wiley & Sons, Ltd.     

Output feedback tracking control of robot manipulators with model uncertainty via adaptive fuzzy logic

Many robot controllers require not only joint position measurements but also joint velocity measurements; however, most robotic systems are only equipped with joint position measurement devices. In this paper, a new output feedback tracking control approach is developed for the robot manipulators with model uncertainty. The approach suggested herein does not require velocity measurements and employs the adaptive fuzzy logic. The adaptive fuzzy logic allows us to approximate uncertain and nonlinear robot dynamics. Only one fuzzy system is used to implement the observer-controller structure of the output feedback robot system. It is shown in a rigorous manner that all the signals in a closed loop composed of a robot, an observer, and a controller are uniformly ultimately bounded. Finally, computer simulation results on three-link robot manipulators are presented to show the results which indicate good position tracking performance and robustness against payload uncertainty and external disturbances.     

改进的Backstepping在移动机器人轨迹跟踪中的应用

以四轮移动机器人的运动学模型为研究对象,基于BackStepping的设计思想,通过构造一种简单的中间虚拟反馈变量,同时结合Lyapunov直接法设计了一种移动机器人轨迹跟踪控制律,并证明了系统在设计控制律下的全局稳定性;但控制律中含有未知参数,不同的参考轨迹都要重新调节才能达到良好的跟踪效果,因此利用极点配置的方法对这些参数进行了优化整定,从而保证了控制器的自适应性;文中以直线和圆为参考轨迹做了仿真实验;仿真结果表明该算法具有快速,精确,全局稳定的良好特性。     

A higher level path tracking controller for a four-wheel differentially steered mobile robot

A variety of approaches for path tracking control of wheeled mobile robots have been implemented. While most of these are based on controlling the robot dynamics, they are not applicable if the robot dynamics are inaccessible. In this paper, a fuzzy logic controller (FLC) for the path tracking of a wheeled mobile robot based on controlling the robot at a higher level is presented. The controller is highly robust and flexible and automatically follows a sequence of discrete waypoints, and no interpolation of the waypoints is needed to generate a continuous reference trajectory. The speeds are varied depending on the variations in the path and on the posture of the robot. The heuristic rules of the FLC are based on an analogy with a human driving a car and the optimization of the controller is based on experimentation. The implementation on a P3-AT mobile robot shows the effectiveness of the proposed approach.     

PDC Control Design for Non-holonomic Wheeled Mobile Robots with Delayed Outputs

This paper presents a new technique for tracking-error model-based Parallel Distributed Compensation (PDC) control for non-holonomic vehicles where the outputs (measurements) of the system are delayed and the delay is constant. Briefly, this technique consists of rewriting the kinematic error model of the mobile robot tracking problem into a TS fuzzy representation and finding a stabilizing controller by solving LMI conditions for the tracking-error model. The state variables are estimated by nonlinear predictor observer where the outputs are delayed by a constant delay. To illustrate the efficiency of the proposed approach a comparison between the TS fuzzy observer and the nonlinear predictor observer is shown. For this study the reference trajectory is built by taking into account the acceleration limits of the mobile robot. All experiments are implemented on simulation and the real-time platform.     

考虑运动受限的履带式移动机器人轨迹跟踪控制

针对履带式移动机器人的轨迹跟踪控制问题进行研究,首先,建立了履带式移动机器人的运动学模型和跟踪误差模型;其次,设计了转速有限时间控制和线速度滑模控制的轨迹跟踪控制律,并给出了考虑运动受限作用下的控制律修正表达式;最后,基于MATLAB对所提控制律进行仿真,对比分析了不考虑运动受限情况下跟踪控制效果;结果表明,设计的跟踪控制律能够实现履带式移动机器人对圆轨迹的有效跟踪,且考虑运动受限作用的控制律更加符合实际;文章研究分析了运动受限作用对于移动机器人轨迹跟踪控制的影响,分析结果对其他移动机器人的运动控制研究具有参考价值。     

A 3-level autonomous mobile robot navigation system designed by using reasoning/search approaches

This paper describes how soft computing methodologies such as fuzzy logic, genetic algorithms and the Dempster–Shafer theory of evidence can be applied in a mobile robot navigation system. The navigation system that is considered has three navigation subsystems. The lower-level subsystem deals with the control of linear and angular volocities using a multivariable PI controller described with a full matrix. The position control of the mobile robot is at a medium level and is nonlinear. The nonlinear control design is implemented by a backstepping algorithm whose parameters are adjusted by a genetic algorithm. We propose a new extension of the controller mentioned, in order to rapidly decrease the control torques needed to achieve the desired position and orientation of the mobile robot. The high-level subsystem uses fuzzy logic and the Dempster–Shafer evidence theory to design a fusion of sensor data, map building, and path planning tasks. The fuzzy/evidence navigation based on the building of a local map, represented as an occupancy grid, with the time update is proven to be suitable for real-time applications. The path planning algorithm is based on a modified potential field method. In this algorithm, the fuzzy rules for selecting the relevant obstacles for robot motion are introduced. Also, suitable steps are taken to pull the robot out of the local minima. Particular attention is paid to detection of the robot’s trapped state and its avoidance. One of the main issues in this paper is to reduce the complexity of planning algorithms and minimize the cost of the search. The performance of the proposed system is investigated using a dynamic model of a mobile robot. Simulation results show a good quality of position tracking capabilities and obstacle avoidance behavior of the mobile robot.     

Kinematic path‐tracking of mobile robot using iterative learning control

This paper develops a kinematic path‐tracking algorithm for a nonholonomic mobile robot using an iterative learning control (ILC) technique. The proposed algorithm produces a robot velocity command, which is to be executed by the proper dynamic controller of the robot. The difference between the velocity command and the actual velocity acts as state disturbances in the kinematic model of the mobile robot. Given the kinematic model with state disturbances, we present an ILC‐based path‐tracking algorithm. An iterative learning rule with both predictive and current learning terms is used to overcome uncertainties and the disturbances in the system. It shows that the system states, outputs, and control inputs are guaranteed to converge to the desired trajectories with or without state disturbances, output disturbances, or initial state errors. Simulations and experiments using an actual mobile robot verify the feasibility and validity of the proposed learning algorithm. © 2005 Wiley Periodicals, Inc.