An addendum on “Robust control of robots by the computed torque method”

We reinterprete and improve recent results on robust control of robots by the computed method. The methods and ideas used are inspired by ‘passivity based’ control methods for robot manipulators and lead to a significant increase in freedom of controller implementation, thereby providing more flexibility to the designer of robot control systems.     

Global asymptotic stability of a tracking sectorial fuzzy controller for robot manipulators.

This paper shows that fuzzy control systems satisfying sectorial properties are effective for motion tracking control of robot manipulators. We propose a controller whose structure is composed by a sectorial fuzzy controller plus a full nonlinear robot dynamics compensation, in such a way that this structure leads to a very simple closed-loop system represented by an autonomous nonlinear differential equation. We demonstrate via Lyapunov theory, that the closed-loop system is globally asymptotically stable. Experimental results show the feasibility of the proposed controller.     

A Flexible Control Architecture for Mobile Robots: An Application for a Walking Robot

To get the best features of both deliberative and reactive controllers, present mobile robot control architectures are designed to accommodate both types of controller. However, these architectures are still very rigidly structured thus deliberative modules are always assigned to the same role as a high-level planner or sequencer while low-level reactive modules are still the ones directly interacting with the robot environment. Furthermore, within these architectures communication and interface between modules are if not strongly established, they are very complex thus making them unsuitable for simple robotic systems. Our idea in this paper is to present a control architecture that is flexible in the sense that it can easily integrate both reactive and deliberative modules but not necessarily restricting the role of each type of controller. Communication between modules is through simple arbitration schemes while interface is by connecting a common communication line between modules and simple read and/or write access of data objects. On top of these features, the proposed control architecture is scalable and exhibits graceful degradation when some of the modules fail, similar to the present mobile robot architectures. Our idea has enabled our four-legged robot to walk autonomously in a structured uneven terrain.     

A “static” optimal strategy for a service robot: A one dimensional case

In this paper we present a method for creating a strategy to control the movements of a service robot, servicing thread rewinding machines. The robot moves from one machine to another detecting a dropped or broken thread. When this is detected the robot will restart the machine.

A previous paper considered a number of naive strategies and compared them to the simple strategy of just moving back and forth from one end of the line to the other. In this paper we will propose a new method for determining the strategy and we will examine the effects of this new strategy with the aid of simulation.     

不确定性机器人的自适应跟踪控制

提出了一种自适应控制策略 ,用于不确定性机器人的轨迹跟踪 ,该控制器结构简单 ,且无需计算回归矩阵 ,通过对二自由度的机器人的仿真 ,证明该方法能使跟踪误差快速趋近于零     

Experimental study on direct adaptive control of a PUMA 560 industrial robot

The article describes the implementation and experimental validation of a new direct adaptive control scheme on a PUMA 560 industrial robot. The testbed facility consists of a Unimation PUMA 560 six-jointed robot and controller, and a DEC Micro VAX II computer which hosts the RCCL (Robot Control “C” Library) software. The control algorithm is implemented on the Micro VAX which acts as a digital controller for the PUMA robot, and the Unimation controller is effectively bypassed and used merely as an I/O device to interface the Micro VAX to the joint motors. The control algorithm for each robot joint consists of an auxiliary signal generated by a constant-gain PID controller, and an adaptive position-velocity (PD) feedback controller with adjustable gains. The adaptive independent joint controllers compensate for the interjoint couplings and achieve accurate trajectory tracking without the need for the complex dynamic model and parameter values of the robot. Extensive experimental results on PUMA joint control are presented to confirm the feasibility of the proposed scheme, in spite of strong interactions between joint motions. The scheme is also implemented for control of the end-effector motion in Cartesian space. Experimental results validate the capabilities of the proposed control scheme. The control scheme is extremely simple and computationally very fast for concurrent processing with high sampling rates.     

Locomotion Control of a Hydraulically Actuated Hexapod Robot by Robust Adaptive Fuzzy Control with Self-Tuned Adaptation Gain and Dead Zone Fuzzy Pre-compensation

Hydraulically actuated robotic mechanisms are becoming popular for field robotic applications for their compact design and large output power. However, they exhibit nonlinearity, parameter variation and flattery delay in the response. This flattery delay, which often causes poor trajectory tracking performance of the robot, is possibly caused by the dead zone of the proportional electromagnetic control valves and the delay associated with oil flow. In this investigation, we have proposed a trajectory tracking control system for hydraulically actuated robotic mechanism that diminishes the flattery delay in the output response. The proposed controller consists of a robust adaptive fuzzy controller with self-tuned adaptation gain in the feedback loop to cope with the parameter variation and disturbances and a one-step-ahead fuzzy controller in the feed-forward loop for hydraulic dead zone pre-compensation. The adaptation law of the feedback controller has been designed by Lyapunov synthesis method and its adaptation rate is varied by fuzzy self-tuning. The variable adaptation rate helps to improve the tracking performance without sacrificing the stability. The proposed control technique has been applied for locomotion control of a hydraulically actuated hexapod robot under independent joint control framework. For tracking performance of the proposed controller has also been compared with classical PID controller, LQG state feedback controller and static fuzzy controller. The experimental results exhibit a very accurate foot trajectory tracking with very small tracking error with the proposed controller.     

指数稳定的机器人鲁棒跟踪控制

本文提出了一种用于控制具有参数不确性机器人轨迹踊跃的鲁棒控制策略，该控制器的设计根据Ｌｙａｐｕｎｏｖ理论，由一个基于Ｓｌｏｔｉｎｅ方法的标称控制器和一个非线性连续反馈补偿器组成。     

Speed control of differentially driven wheeled mobile robots—model‐based adaptive approach

In this paper, the issue concerning model‐based adaptive control for a differentially driven wheeled mobile robot is addressed. By choosing state variables directly related to its speeds (linear and angular), the robot's dynamic model becomes simple with good properties. The controller takes account of the robot dynamics and the coupling between the motions of two wheels, and thus achieves better speed tracking than commonly used model‐free PID controller does. The effectiveness of the model free controllers (mostly PID) in some situations is also discussed. Simulation studies are done to verify the effectiveness of the proposed approaches. © 2005 Wiley Periodicals, Inc.     

Fractional Control of a Humanoid Robot Reduced Model with Model Disturbances

There is an open discussion between those who defend mass-distributed models for humanoid robots and those in favor of simple concentrated models. Even though each of them has its advantages and disadvantages, little research has been conducted analyzing the control performance due to the mismatch between the model and the real robot, and how the simplifications affect the controller’s output. In this article we address this problem by combining a reduced model of the humanoid robot, which has an easier mathematical formulation and implementation, with a fractional order controller, which is robust to changes in the model parameters. This controller is a generalization of the well-known proportional–integral–derivative (PID) structure obtained from the application of Fractional Calculus for control, as will be discussed in this article. This control strategy guarantees the robustness of the system, minimizing the effects from the assumption that the robot has a simple mass distribution. The humanoid robot is modeled and identified as a triple inverted pendulum and, using a gain scheduling strategy, the performances of a classical PID controller and a fractional order PID controller are compared, tuning the controller parameters with a genetic algorithm.     

A multivariable control scheme for robot manipulators

The article puts forward a simple scheme for multivariable control of robot manipulators to achieve trajectory tracking. The scheme is composed of an inner loop stabilizing controller and an outer loop tracking controller. The inner loop utilizes a multivariable PD controller to stabilize the robot by placing the poles of the linearized robot model at some desired locations. The outer loop employs a multivariable PID controller to achieve input-output decoupling and trajectory tracking. The gains of the PD and PID controllers are related directly to the linearized robot model by simple closed-form expressions. The controller gains are updated on-line to cope with variations in the robot model during gross motion and for payload change. Alternatively, the use of high gain controllers for gross motion and payload change is discussed. Computer simulation results are given for illustration.     

Enhanced direct adaptive fuzzy control of robot manipulators

In this article, an enhanced direct adaptive fuzzy robot controller is developed to overcome problems of high‐frequency oscillations across the boundary of the constraint set and large control signals. The direct adaptive fuzzy robot control algorithm employs tracking errors of the joint motion to drive the parameter adaptation. The predominant concern of the adaptation law is to reduce the tracking errors, and closed‐loop stability is ensured by appending a supervisory controller. This adaptive controller, appended with the supervisory controller, does not require the exact robot dynamics, but only the boundary of the dynamics. Theoretical results and simulation studies on a two‐link robot manipulator show that by modifying the activation function of the supervisory controller, the enhanced direct adaptive fuzzy robot controller is as robust as before and the problems of high‐frequency oscillations across the boundary of the constraint set and large control signals are alleviated. ©1999 John Wiley & Sons, Inc.     

Robustness of decentralized robot controller to payload variation

The decentralized controller for manipulation robot is tested for its robustness to payload variation. First the local controllers are synthesized to withstand variation of inertia round the joints and then the global stability of the robotic system is examined. Three various situations are discussed: a) when actuator inertia is large in comparison to mechanism inertia, b) if the variation of payload is small in comparison to mechanism inertia, and c) if the large variation of payload parameters are expected. An algorithm for testing the robustness of the robot control to parameter variation is established, too. The purpose of the algorithm is to determine the allowable variation of the payload parameters which can be withstood by the simple decentralized controller. On the other hand, by this algorithm the local servosystems can be synthesized which are capable to withstand assumed parameter variation. This synthesis is demonstrated on an example of particular robotic system.     

Optimal two-degree-of-freedom fuzzy control for locomotion control of a hydraulically actuated hexapod robot

Locomotion control of legged robots is a very challenging task because very accurate foot trajectory tracking control is necessary for stable walking. An electro-hydraulically actuated walking robot has sufficient power to walk on rough terrain and carry a heavier payload. However, electro-hydraulic servo systems suffer from various shortcomings such as a high degree of nonlinearity, uncertainty due to changing hydraulic properties, delay due to oil flow and dead-zone of the proportional electromagnetic control valves. These shortcomings lead to inaccurate analytical system model, therefore, application of classical control techniques result into large tracking error. Fuzzy logic is capable of modeling mathematically complex or ill-defined systems. Therefore, fuzzy logic is becoming popular for synthesis of control systems for complex and nonlinear plants. In this investigation, a two-degree-of-freedom fuzzy controller, consisting of a one-step-ahead fuzzy prefilter in the feed-forward loop and a PI-like fuzzy controller in the feedback loop, has been proposed for foot trajectory tracking control of a hydraulically actuated hexapod robot. The fuzzy prefilter has been designed by a genetic algorithm (GA) based optimization. The prefilter overcomes the flattery delay caused by the hydraulic dead-zone of the electromagnetic proportional control valve and thus helps to achieve better tracking. The feedback fuzzy controller ensures the stability of the overall system in the face of model uncertainty associated with hydraulically actuated robotic mechanisms. Experimental results exhibit that the proposed controller manifests better foot trajectory tracking performance compared to single-degree-of-freedom (SDF) fuzzy controller or optimal classical controller like state feedback LQR controller.     

Mobile robot path tracking using a robust PID controller

This paper presents a simple and effective solution for the path tracking problem of a mobile robot using a PID controller. The proposed method uses a simple linearized model of the mobile robot composed of an integrator and a delay. The synthesis procedure is simple and allows the PID controller to be tuned considering the nominal performance and the robustness as control specifications. Experimental results demonstrate the good performance and robustness of the proposed controller.     

Improved Image Based Visual Servoing with Parallel Robot

The position regulation problem of an eye-in-hand type of parallel robot based pointing systems (PRBPS) is considered in this paper. A fuzzy logic system is first designed to compensate for the uncertainties of the parallel robot and the uncertainty of the image Jacobian, then a hybrid controller (HC) including the image-based nonlinear controller and the adaptive supervisory fuzzy logic controller (ASFLC) is derived by using the Lyapunov direct method to realize the position regulation (PR). The stability of the closed-loop system in the Lyapunov sense is proven theoretically. The fuzzy scaling matrix is combined with the HC to improve the performance of the control system. The simulation results demonstrate that the PRBPS realizes PR with very good robustness to the parameter uncertainties, and the control input torques and settling time are reduce greatly in the case of large initial feature errors.     

一种CMAC网络模型及其在机器人控制中的应用

小脑模型关节控制器（CMAC）具有学习算法简单、在线学习速度快的优点,非常适于机器人等复杂系统的自适应控制,本文阐述了CMAC的原理,证明了其收敛性,提出了一种适合于机器人轨迹跟踪控制的CMAC,并给出了仿真实验结果。     

A Markov Game-Adaptive Fuzzy Controller for Robot Manipulators

This paper develops an adaptive fuzzy controller for robot manipulators using a Markov game formulation. The Markov game framework offers a promising platform for robust control of robot manipulators in the presence of bounded external disturbances and unknown parameter variations. We propose fuzzy Markov games as an adaptation of fuzzy Q-learning (FQL) to a continuous-action variation of Markov games, wherein the reinforcement signal is used to tune online the conclusion part of a fuzzy Markov game controller. The proposed Markov game-adaptive fuzzy controller uses a simple fuzzy inference system (FIS), is computationally efficient, generates a swift control, and requires no exact dynamics of the robot system. To illustrate the superiority of Markov game-adaptive fuzzy control, we compare the performance of the controller against a) the Markov game-based robust neural controller, b) the reinforcement learning (RL)-adaptive fuzzy controller, c) the FQL controller, d) the H_infin theory-based robust neural game controller, and e) a standard RL-based robust neural controller, on two highly nonlinear robot arm control problems of i) a standard two-link rigid robot arm and ii) a 2-DOF SCARA robot manipulator. The proposed Markov game-adaptive fuzzy controller outperformed other controllers in terms of tracking errors and control torque requirements, over different desired trajectories. The results also demonstrate the viability of FISs for accelerating learning in Markov games and extending Markov game-based control to continuous state-action space problems.     

Method for improving the position precision of a hydraulic robot arm: dual virtual spring–damper controller

Recently, control approaches for a hydraulic robot in the field of robotics have attracted considerable attention owing to their high power-to-weight ratio. Many studies on behavior and control exploiting the advantages of hydraulic robots have been pursued. Application to hydraulically actuated systems, however, is not straightforward due to the nonlinear internal dynamics of the actuators. This paper presents a relatively simple method to improve the position precision of a hydraulic robot arm. We propose a simple control method concept based on a virtual spring–damper (VSD) controller, which enables the robot to realize a desired position. The main advantage of the VSD control is its simple calculation method, which eliminates the need to solve the Jacobian pseudo-inverse or ill-posed inverse kinematics. In this study, experiments were conducted to identify the problems in previous study results and evaluated the applicability of VSD control to the hydraulic robot arm. A relatively simple method was proposed to solve these problems and to verify improvements in the position precision. The proposed method is the dual VSD controller in which an additional VSD model is applied to the elbow, in addition to the conventional VSD model connected to the wrist. The effectiveness of the proposed control scheme is demonstrated in experimentation with the hydraulic robot arm.     

Adaptive Fourier Series Neural Network PID Controller

In this work, an Adaptive Neural Networks PID controller structure, called Adaptive Fourier Series Neural Networks PID controller (AFSNNPID), is developed. The main objective is to obtain a simple controller for nonlinear systems that can be tuned online to reject perturbations effect and compensate the system parameters variation. Due to its simple architecture and very attractive proprieties, the Fourier Series Neural Network (FSNN) is used to online adjust the parameters of the PID controller. Furthermore, using the delta-rule algorithm, the adaptation dynamics of the FSNN is globally stable. The design procedure of the proposed controller and the stability analysis of the closed loop system using the small gain theorem are given. To assess the effectiveness of the proposed control scheme, the control of a 3-DOF robot arm manipulator is considered and a comparative study, using the adaptive neural network PID controller and the particle swarm optimization based PID controller, is carried out. The obtained results, through the experimental study, indicate that the AFSNNPID controller presents better control performance than the other controllers.