This article presents advances in optimal experiment design, which are intended to improve the parameter identification of nonlinear state space models. Instead of using a sequence of samples from one or just a few coherent sequences, the idea of identifying nonlinear dynamic models at distinct points in the state space is considered. In this way, the placement of the experiment points is fully flexible with respect to the set of reachable points. Also, a method for model-based generation of prediction errors is proposed, which is used to compute an a-priori estimate of the sample covariance of the prediction error. This covariance matrix may be used to approximate the Fisher information matrix a-priori. The availability of the Fisher matrix a-priori is a prerequisite for experiment optimization with respect to covariance in the parameter estimates. This work is driven by the problem of parameter identification of hydraulic models. There are methods for hydraulic systems regarding the estimation of parameters from experimental data, but the choice of experiments has not been treated adequately yet. A hydraulic servo system actuating a stewart platform serves as an illustrative example to which the methods above are applied.

This article aderesses aspects of the a-priori computation of the Fisher information matrix. The aim of the type of Fisher matrix discussed is the experiment design for parameter estimation of nonlinear state space models. A definition for the sampling of experiments is stated, which is convenient for software implementation. Both, measurement samples that belong to a coherent sequence and samples from independent experiments, may be stored together in one single data structure. Since the covariance matrix of the prediction error is required in the Fisher matrix, a method for its a-priori prediction is proposed. A servo-hydraulic positioning system serves as a validation example. The comparison of the a-priori and a-posteriori Fisher matrices of this example show that the method proposed is suitable to provide an approximation of an optimality criterion for experiment design.

Despite of a promising potential robots based on parallel kinematic structures have not yet found their way into industry on a large scale. One of the main reasons for that is a workspace to installation-space ratio, which usually is inferior to that of their serial counterparts. In this paper development and design issues of a triglide-robot based on the linear delta are presented. Using a workspace enlargement approach based on the use of several workspaces going along with different assembly modes of the structure's the workspace to installation-space ratio of this robot can be enhanced significantly. Additionally this paper is part of a joint effort of several authors to benchmark different kinematic structures using based-fixed linear drives against each other. Thus, the presented structure is characterized using standardized benchmark criteria.

Besides a quantity of well-known positive characteristics, parallel robots also obtain a couple of negative properties which limit their efficient use in certain application areas nowadays. Direct kinematic singularities that constrain the internal workspace are one of these inconvenient properties. This paper establishes a unique index, with physical meaning, that indicates the entrance of the Hexa parallel robot, considering its joint limits, to all its direct kinematic singularities. For this, we complement two methods to detect singularities: the power inspired measure method and the Grassmann geometry method. The applied procedure uses screw theory and line geometry tools to represent movements, actions and lines, respectively, in the robot. This procedure is practically verified in Hexa robot and shows that some singularities are overlapped forming large singularities.

In this paper, we present work about robot control architecture, assembly planning and task planning for manufacturing robots. The interface between an offline planning unit and control systems is handled with skill primitives. Thus, skill primitives and skill primitive nets are explained in detail. Our long term aim is to combine robot control with task and assembly planning, so that with less human interaction manufacturing costs can be reduced. Even parallel kinematic machines provide enormous opportunities to reduce cycle times and thus the benefit should not be wasted by expensive specialized robot programming. Thus, we give an overview of our system and focus on some aspects to implement such a sophisticated system.

Based on closed kinematic chains, parallel robots obtain favorable dynamic properties as well as high stiffness. Hence, their application can significantly enlarge the productivity of automated production processes. A control concept for tapping the high potential concerning low cycle times and high path-tracking accuracy is presented. The proposed approach adapts autonomously to changing dynamic parameters as varying payload. The autonomous behavior is achieved by combining an adaptive control approach with an adaptive, time-optimal trajectory planning concept and an online-trajectory adaption mechanism. Extensive experimental results prove the performance of the proposed approach. Note to Practitioners -Many applications in the field of production automation (material handling, assembly, etc.) require high operating speeds and accelerations. During the past years, parallel robots proved to be an efficient and suitable supplement to serial robots. Unfortunately, the promising possibilities of parallel robots often cannot yield profit because their dynamic potential is still not fully exploited. The payload/robot mass ratio of parallel structures is even higher compared to serial robots, where the influence of the payload on the impedance of the robot is negligible. By use of direct drives the influence of a variable payload cannot be ignored. A modified adaptive control concept, which adapts autonomously to changing dynamic parameters-as varying payload due to diversity of assembly processes-guarantees high tracking accuracy and therefore better process quality as well as accurate estimates of changing dynamic parameters and therefore better process quality. In addition the productivity of the process can be enlarged, if the full drive power can be used at each point on the path. Thus, a new adaptive time-optimal trajectory planning algorithm is used to exploit the dynamic potential of the direct drives and consequently to shorten the cycle times. The aim of time-optimal trajectory planning, as it is commonly understood, is the determination of the maximum velocity profile along a given path that complies with all given dynamic and kinematic robot constraints like limited drive forces/torques, limited path and/or drive velocities and limited path jerk. Combining the adaptive control scheme and the adaptive, time-optimal trajectory planning algorithm with an online trajectory adaption mechanism, a control concept is realized, which autonomously adapts to changing dynamic robot behavior. Using this new approach, the advantages of parallel robots-as well as serial robots with direct drives-can better be utilized. This is a necessary prerequisite for a larger extension of PKMs for industrial applications.

Beside a quantity of well-known positive characteristics parallel robots also obtain a couple of negative properties limiting their efficient use in certain application areas nowadays. Additional sensors integrated in the passive joints provide the possibility to overcome several of these limitations simultaneously thus leading towards an increasing overall system performance. In detail, different control functions, necessary for fast, precise and safe robot operation like calibration, workspace monitoring and position control can be improved. To tap the potential of the concept of passive joint-sensors, algorithms to evaluate the additional sensor information for certain control functions and an approach to determine the necessary sensor resolution for a class of parallel robots are developed. Different sensor configurations are investigated and compared due to computational efficiency and resolution demands. Based on these requirements, we report on the development of new inductive micro sensors for angle measurement capable for integration, e.g. into rotary joints of high-performance parallel robots. Basic design and process technologies of the single sensor components as well as first measuring results are described.

This paper presents a model-based robust controller for hydraulically driven robot manipulators. The approach guarantees that the output error of the plant remains within a prescribed bound despite of model uncertainties. Manipulator dynamics and actuator dynamics including the dynamics of the valves are taken into account. The stability proof is based on the Lyapunov method and passivity arguments. It is shown that the stability proof is not restricted to the proposed control law of the hydraulic actuators but also holds for arbitrary hydraulic force control laws as long as certain requirements are fulfilled.