Clamping weighted least-norm method for the manipulator kinematic control with constraints

One of the main challenges for kinematic control of manipulators is how to effectively address various constraints, such as obstacle avoidances. Most constraints can be converted into a virtual joint-limit problem by the general weighted least-norm method reported recently. However, a chattering of weighted factors might occur in the presence of disturbances. In this paper, in order to improve the robustness to disturbances, a clamping term forcing joints away from their limits is added to the solution of inverse kinematics, termed as the clamping weighted least-norm method. It is proved that the joint-limit constraint is always guaranteed. The proposed method is applicable to both non-redundant and redundant manipulators; the accuracy of main task is sacrificed only when there is confliction between main task and joint-limit constraint. Experiments on the seven-degree-of-freedom redundant manipulator illustrate the good performance for avoiding joint limits while tracking a given trajectory.     

A singularity-free motion control algorithm for robot manipulators—a hybrid system approach

This paper presents the design and implementation of a singularity-free tracking algorithm for robot manipulators using a hybrid system approach. A hybrid robot motion controller is designed to ensure feasible robot motion in the neighborhood of kinematic singularities. The hybrid control system has a two-layered hierarchical structure, a discrete layer and a continuous layer. The robot workspace is partitioned into subspaces based on the singular configurations of the robot. Switching between continuous controllers is involved when the robot travels across the subspaces. With the hybrid controller, the robot can work at the vicinity of singular configurations, but also can go through and stay at the singular configurations. The stability of the hybrid system is investigated using multiple Lyapunov function theory. Experimental results have demonstrated the advantages of the hybrid robot motion control method.     

Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach

We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid fuzzy-neural approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. We describe an intelligent system for controlling robot manipulators to illustrate our fuzzy-neural hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the fractal dimension of a time series of measured values of the variables is used as a selection parameter. We use neural networks for identification and control of robotic dynamic systems. We also compare our hybrid fuzzy-neural approach with conventional fuzzy control to show the advantages of the proposed method for control.     

Numerical method for the simultaneous determination of unknown parameters in a parabolic equation

Numerical procedures for the solution of an inverse problem of simultaneously determining unknown parameters in a linear parabolic equation are considered. The approach proposed is to approximate unknown functions using Chebyshev polynomials, which are determined consecutively from the solutions of the minimization problems based on overspecified data. Finally, the results of a numerical experiment are displayed.     

Satisfaction of gain and phase margin constraints using proportional controllers

Gain and phase margins (GM and PM) are frequently used as robustness indicators for linear time invariant systems. In many cases, the design problem is reduced to determination of a loop gain to satisfy several design criteria including gain and PM specifications. Therefore finding the set of fixed order compensators that satisfy given gain and PM specifications attracted the attention of a considerable part of the scientific community recently. It is possible to show that it may not be possible to satisfy the required gain and/or PM specifications using only a proportional controller. As a result, finding the limits of the proportional controllers for a given plant (i.e. finding the maximum achievable gain and PMs, MAGM and MAPM) is also important. After providing alternative methods (to those that already exist in the literature) for determining all stabilising proportional controllers that satisfy gain and PM constraints, a new method for calculating MAGM and MAPM using proportional controllers is given in this article. A formulation to calculate maximum gain that results in MAGM is also provided.     

Survey on model-based manipulation planning of deformable objects

A systematic overview on the subject of model-based manipulation planning of deformable objects is presented. Existing modeling techniques of volumetric, planar and linear deformable objects are described, emphasizing the different types of deformation. Planning strategies are categorized according to the type of manipulation goal: path planning, folding/unfolding, topology modifications and assembly. Most current contributions fit naturally into these categories, and thus the presented algorithms constitute an adequate basis for future developments.     

Design of PID controllers satisfying gain margin and sensitivity constraints on a set of plants

This paper presents a method for the design of PID-type controllers, including those augmented by a filter on the D element, satisfying a required gain margin and an upper bound on the (complementary) sensitivity for a finite set of plants. Important properties of the method are: (i) it can be applied to plants of any order including non-minimum phase plants, plants with delay, plants characterized by quasi-polynomials, unstable plants and plants described by measured data, (ii) the sensors associated with the PI terms and the D term can be different (i.e., they can have different transfer function models), (iii) the algorithm relies on explicit equations that can be solved efficiently, (iv) the algorithm can be used in near real-time to determine a controller for on-line modification of a plant accounting for its uncertainty and closed-loop specifications, (v) a single plot can be generated that graphically highlights tradeoffs among the gain margin, (complementary) sensitivity bound, low-frequency sensitivity and high-frequency sensor noise amplification, and (vi) the optimal controller for a practical definition of optimality can readily be identified.     

Evolutionary design of discrete controllers for hybrid mechatronic systems

This paper investigates the issue of evolutionary design of controllers for hybrid mechatronic systems. Finite State Automaton (FSA) is selected as the representation for a discrete controller due to its interpretability, fast execution speed and natural extension to a statechart, which is very popular in industrial applications. A case study of a two-tank system is used to demonstrate that the proposed evolutionary approach can lead to a successful design of an FSA controller for the hybrid mechatronic system, represented by a hybrid bond graph. Generalisation of the evolved FSA controller to unknown control targets is also tested. Further, a comparison with another type of controller, a lookahead controller, is conducted, with advantages and disadvantages of each discussed. The comparison sheds light on which type of controller representation is a better choice to use in various stages of the evolutionary design of controllers for hybrid mechatronic systems. Finally, some important future research directions are pointed out, leading to the major work of the succeeding part of the research.     

Design of nonlinear controllers for active vehicle suspension with state constraints

Design of active vehicle suspension has tradeoffs between three main performance metrics (passenger comfort, suspension deflection and road holding ability). It has been known that each performance can be achieved by H _∞ controller and they can be gathered by LPV (Linear Parameter Varying) method. However, because the suspension deflection limit was not explicitly considered, this limit may be exceeded. In this paper, the authors propose a “reference shaping“ based method in order to improve the control performance. In this approach, a “virtual reference” signal is imposed to the system such that the suspension deflection is kept small. The effectiveness of the approach is examined by numerical simulations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Adaptive feed-forward compensation for hybrid control with acceleration time waveform replication on electro-hydraulic shaking table

This article presents a high fidelity acceleration time waveform replication (TWR) on an electro-hydraulic shaking table (EHST) using the hybrid control combined with an offline feed-forward compensator and an online adaptive inverse control (AIC). This study applies the acceleration and velocity feedback to improve the steady performance of the EHST, and employs the system inverse transfer function (ITF) of the acceleration closed-loop system to extend the frequency bandwidth and a modeling error compensator to improve the dynamic characteristics. Moreover, the investigation utilizes a Zero Phase Error Tracking Controller to improve the accuracy of the designed ITF. The proposed hybrid controller also utilizes an online AIC for a high fidelity TWR after that the dynamic characteristics has been improved by the feedback and feed-forward controllers. Thus, the proposed hybrid control strategy combines the merits of the offline feed-forward compensation and online AIC. Performance analysis and comparison of the experimental results demonstrate that better replication accuracy with the proposed hybrid control can be achieved in experiments on an actual EHST.     

A two-stage design of adaptive fuzzy controllers for time-delay systems with unknown models

In this article, a systematic two-stage design method for adaptive fuzzy controllers is presented. The proposed control scheme has low computational complexity. Moreover, the exact mathematical model of the plant to be controlled is not required. The fuzzy controller under consideration is based on the proportional-derivative fuzzy control scheme and triangular membership functions. In the design procedure, the domain intervals of the input and output variables are selected with a heuristic approach to minimize a cost function under the constraint of tolerable overshoots in the response curve. A learning scheme is then proposed to automatically adjust the parameters in the fuzzy controller to reduce the error of the system. It can also be used adaptively to improve the system performance of a time-varying system. Simulations and comparisons are included to demonstrate the effectiveness of the proposed method.     

混合约束过程的多变量协调预测控制

研究了工业生产过程存在着输入、输出软硬混合约束的优化控制技术。给出了基于约束的预测控制算法,提出了用动态优先级对有硬约束的操作变量进行在线协调,当协调过程找不到满足所有约束条件的可行解时,对被控变量进行约束软化,本文采取的线性和二次型相结合的惩罚函数对预测时域上每个时刻的激活值进行惩罚,不仅可以保证可行解的存在,而且能使系统处于动态和稳态的优化性能。     

Experience-based optimization of universal manipulation strategies for industrial assembly tasks

The trend towards smaller lot sizes and shorter product life cycles requires automation solutions with higher flexibility. Today’s robotic systems often are uneconomical for frequently changing boundary conditions and varying tasks due to high engineering costs needed for a well-defined supply of parts and pallets. At the same time, even small inaccuracies due to shape deviations in parts or pallets often cause high downtime. This work contributes to the robustness of industrial assembly processes with high inaccuracy concurrent to narrow tolerances. Therefore, contact-based manipulation strategies are defined, which are model-free and object-independent and solve common industrial tasks as palletizing, packaging and machine feeding. While the strategies are robust to inaccuracy up to 5 mm/5° due to localization uncertainty or object displacement, they handle usual industrial assembly tolerance of far below 1mm. The necessary flexibility and reusability for new tasks is guaranteed by hierarchical decomposition into atomic sub-strategies. In order to accelerate the execution, the manipulation strategies are customized to each specific task by unsupervised experience-based learning. The flexibility of the manipulation strategies and the progress in cycle time during the execution are shown on common industrial tasks with varying objects, tolerances and inaccuracies.     

Simulations of adaptive controllers for a paper machine headbox

The paper headbox is a crucial part of a paper machine, largely influencing the paper quality. Since this can be improved by better control, the evaluation of advanced control methods is certainly justified from an economical point of view. Not only do disturbances have to be rejected, but smooth startups and fast setpoint changes must also be possible. To design a good controller, some parameters need to be known, the determination of which can be cumbersome. However, this can be considerably alleviated by adding an on-line parameter estimator to the controller. This allows the controller to adapt itself to slowly changing process characteristics. In this paper different adaptive control methods are used on the simulated process and compared to the classical PI-controls. Particularly, a single-output self-tuning controller with feedforward action and a multivariable adaptive deadbeat controller are promising. The last one provides simultaneous offset-free control of both process outputs.     

Adaptive sliding inverse control of a class of non-linear systems preceded by unknown non-symmetrical dead-zone

This paper deals with the adaptive control of a class of continuous-time non-linear dynamic systems preceded by unknown non-symmetrical, non-equal slope dead-zones. By exploring the properties of the dead-zone model intuitively and mathematically, a dead-zone inverse is constructed. Based on this inverse construction, an adaptive sliding controller is designed. Lyapunov stability analysis shows that the proposed adaptive control law ensures global stability of the adaptive system and achieves desired tracking precision. Simulation results attained for an uncertain non-linear system are presented to illustrate and further validate the effectiveness of the proposed approach.     

Multi-loop design of multi-scale controllers for multivariable processes

Based on the recently proposed (SISO) multi-scale control scheme, a new approach is introduced to design multi-loop controllers for multivariable processes. The basic feature of the multi-scale control scheme is to decompose a given plant into a sum of basic modes. To achieve good nominal control performance and performance robustness, a set of sub-controllers are designed based on the plant modes in such a way that they are mutually enhanced with each other so as to optimize the overall control objective. It is shown that the designed multi-scale controller is equivalent to a conventional PID controller augmented with a filter. The multi-scale control scheme offers a systematic approach to designing multi-loop PID controllers augmented with filters. Numerical studies show that the proposed multi-loop multi-scale controllers provide improved nominal performance and performance robustness over some well-established multi-loop PID controller schemes.     

A hybrid genetic algorithm for mixed model assembly line balancing problem with parallel workstations and zoning constraints

In this paper, we propose a hybrid genetic algorithm to solve mixed model assembly line balancing problem of type I (MMALBP-I). There are three objectives to be achieved: to minimize the number of workstations, maximize the workload smoothness between workstations, and maximize the workload smoothness within workstations. The proposed approach is able to address some particular features of the problem such as parallel workstations and zoning constraints. The genetic algorithm may lack the capability of exploring the solution space effectively. We aim to improve its exploring capability by sequentially hybridizing the three well known heuristics, Kilbridge & Wester Heuristic, Phase-I of Moodie & Young Method, and Ranked Positional Weight Technique, with genetic algorithm. The proposed hybrid genetic algorithm is tested on 20 representatives MMALBP-I and the results are compared with those of other algorithms.     

软件测试过程质量控制管理研究

本文以软件测试为研究对象,本着为软件开发企业、为软件用户负责的态度,围绕软件测试质量控制管理展开探究。首先,结合作者多年的实践工作经验,对本文研究的背景进行了简要介绍;其次,概述了软件测试内涵、方法等相关内容;最后,结合作者工作实际对软件测试过程中经常出现的几点主要问题进行了全面的梳理与分析,并以此为基础从几个不同的方面分别对软件测试过程质量控制管理策略进行了详细的介绍。     

Continuous bounded controllers for active control of structures

This paper considers nonlinear controller under bounded capacity of the actuators. The controller is first derived based on bang-bang type controllers. In order to avoid high-speed switching effect, a continuous function is used to replace signum function in bang-bang controller. Although a variety of continuous and bounded functions are available, a hyperbolic tangent is chosen as the candidate of the controller. The proposed control law is then applied to three- and six-storey buildings utilising active bracing systems. Different earthquake excitations with various intensities are used in the simulation to show the potential benefit of the proposed control law. Numerical results show that in contrast to bang-bang type controllers that utilise maximum capacity of the actuator, the proposed controller is adaptive in the sense that the maximum capacity of the actuator is utilised when only needed. In addition, the proposed controller can avoid the high-speed switching effects presence in the bang-bang controllers if the weighting variables are chosen appropriately.