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1 108 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 47, NO. 1, FEBRUARY 1998 Instrumentation Architecture and Sensor Fusion for Systems Control Michael E. Stieber, Member IEEE, Emil Petriu, Senior Member IEEE, and George Vukovich Abstract This paper presents a systematic approach for the design of an instrumentation architecture and a sensor data fusion concept which together enable the robust control of complex electromechanical systems. The development is based on the theory of hyperstability. Earlier results are generalized and previous restrictions on sensors are relaxed to broaden the applicability of the proposed method. Experimental results validate the methodology and confirm its efficacy in practical applications. Index Terms Control, instrumentation architecture, robotics, sensor fusion, space, stability/hyperstability. I. INTRODUCTION DETERMINING suitable types, number and locations of sensors is an important step in the design of complex controlled systems. Reference [1] proposes a systematic approach for the design of instrumentation architectures for systems such as the Space Station Remote Manipulator System (SSRMS) [2] whose role in the assembly of the International Space Station has motivated this line of research. Fig. 1 illustrates, in four snapshots, how the SSRMS is employed to install an airlock delivered to the Space Station by the Space Shuttle. Reference [1] develops a set of conditions for the selection of sensors which ensure that the input-output behavior of complex systems is suitable for feedback control with relatively simple ( model-free ) conventional or intelligent control systems, on the basis of the theory of hyperstability [3]. In order to apply the concept to large space manipulators with vision-based control, this methodology is extended in [4] to include, via dynamic data fusion, other sensors which do not meet some of the restrictive conditions. In this paper, we further generalize the underlying concepts and considerably relax remaining restrictions on sensors. We also discuss the implementation of the proposed approach for an experimental robot emulating certain key characteristics of the SSRMS. The experimental results validate the proposed approach. This paper is organized as follows. Section II provides a definition of complex systems in our context. The concept for the selection of sensors and sensor data fusion is outlined in Section III. Section IV develops new conditions for the so- Manuscript received June 1, 1997; revised February 1, M. E. Stieber is with the Canadian Space Agency, Saint Hubert, P.Q., Canada ( mike.stieber@space.gc.ca). E. Petriu is with the School of Information Technology and Engineering, University of Ottawa, Ottawa, Ont., Canada. G. Vukovich is with the Canadian Space Agency, Saint Hubert, P.Q., Canada. Publisher Item Identifier S (98) called hyperstability sensors. The experimental setup and results are discussed in Section V. II. DEFINITION OF COMPLEX SYSTEMS This development is motivated by sensing and control problems associated with large space manipulators like the SSRMS. In terms of their dynamic properties, these systems can be classified as mechanical systems with multiple rigid and/or flexible bodies and holonomous kinematic constraints, as conceptually depicted in Fig. 2. It is well known from classical mechanics [5], [6] that these systems can be described by nonlinear and time-variant second-order differential equations of the form Here, we use the following definitions. is the vector of generalized coordinates describing the motion of the system; is a time variant mass matrix; describes forces and torques due to second-order dynamics, damping and friction effects; describes conservative forces and torques due to the potential energy (e.g., strain energy) in the system; describes the excitation of the system by forces and torques generated by actuators; projects these excitations into the space defined by the generalized coordinates The power transferred to the system by the actuators is given by [5], [6] Based on the principle of conservation of energy and the second law of thermodynamics the following relationship for the energy stored in the system (1) at can be established [5], [6] III. SENSOR SELECTION AND SENSOR FUSION A. Classification of Sensors A set of sensors providing measurements of the motion of the system (1) can be described by a measurement vector defined as a vector function of the generalized coordinates and their derivatives (1) (2) (3) (4) /98$ IEEE

2 STIEBER et al.: INSTRUMENTATION ARCHITECTURE AND SENSOR FUSION 109 Fig. 1. Installation of airlock by the Space Station Remote Manipulator System. Fig. 3. Extended hyperstability concept. Fig. 2. General complex mechanical system with multiple flexible bodies. providing measurements and performance sensors with outputs With this classification the measurement function (4) can be expressed by The function is determined by the selection of the sensors for the system (1), and by the characteristics of the sensors chosen. The judicious choice of sensors and associated parameters such as their location is critical to enable stable and efficient control of the system. Our objective is to establish a methodology for this process. To this end we propose the concept illustrated in Fig. 3, which is an extension of the concepts discussed in [1] and [4]. Here the sensors are classified into hyperstability sensors The hyperstability sensors must be chosen such as to satisfy the hyperstability condition where is an arbitrary but finite constant [1], [3]. This condition implies a certain duality of the hyperstability (5) (6)

3 110 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 47, NO. 1, FEBRUARY 1998 sensors with the actuators and requires exactly one hyperstability sensor for each actuator, i.e., Other implications of this condition will be discussed in Section IV-A. A major limitation of the concept proposed in [1] is that it requires that all sensors satisfy (6), which is often not realistic. For example, vision-based end-point control of flexible space robots falls outside the scope of [1] for that reason. The concept of [1] is therefore extended, as shown in Fig. 3, to include an arbitrary number of other sensors which are combined to a set of performance sensors with a measurement vector The total number of sensors associated with the system (1) then is There are no conditions on the choice of performance sensors. As the name implies they are usually needed to achieve the performance requirements for the closed-loop system. B. Dynamic Sensor Fusion Concept The measurements of the performance sensors are integrated with those of the hyperstability sensors and system input commands by a dynamic data fusion and control algorithm with the property (7) (8) Fig. 4. Permissible characteristics of hyperstability sensors. where is an arbitrary finite constant. It is shown analytically in [7] that any operator with property (8) will stabilize, in the BIBO sense, any system of the form (1) with properties (2) and (3) and sensors as defined in (4) (6). The vector operator may be composed of algebraic, integral, differential or fuzzy logic operations on A class of suitable operators be defined on an element-by-element basis follows:,if,if not with the associated fusion logic (true, false) can as (9) (10) It is shown in [7] that (9) and (10) together satisfy (8). This basic concept for the fusion logic is extended in Section IV-B to account for measurement errors of the hyperstability sensors. Section V describes the implementation and experimental evaluation of an instrumentation and control system based on (9) and (10) for vision-based control of a flexible robot. IV. PROPERTIES OF HYPERSTABILITY SENSORS A. General Characteristics We will now take a closer look at the implications of condition (6). This condition can be linked to the principle expressed in (3) if sensors are selected such as to provide a measure of the power transferred to the system by the actuators. It follows immediately from (2) that the particular choice of sensors defined by (11) results in the relation which, when substituted in (3), satisfies condition (6) for hyperstability sensors with Since this holds for all mechanical systems of the rather general form (1) regardless of system parameters such as mass distribution, stiffness, number of degrees-offreedom, etc., the data fusion and control concept per (7) and (8) will guarantee BIBO stable closed-loop behavior under all parameter variations. Therefore this selection of sensors enables the robust control of complex mechanical systems such as flexible space manipulators [8]. Equation (11) implies the following characteristics for hyperstability sensors: the number of hyperstability sensors must be equal to the number of actuators; hyperstability sensors must measure quantities derived from the generalized velocities, i.e., [1]; hyperstability sensors must match the types of actuators, e.g., a torque actuator requires a matching angular rate sensor [1]; collocation of sensors and actuators is required on flexible structures [1], [9], [10]. In space manipulator systems like the SSRMS, a sufficient number of sensors with the above characteristics is usually

4 STIEBER et al.: INSTRUMENTATION ARCHITECTURE AND SENSOR FUSION 111 Fig. 5. Experimental apparatus. available as part of the instrumentation associated with the motors in the manipulator joints [2]. However, the strict equality in (11) is usually not achievable in practice due to the various nonideal characteristics of the sensors and the measurement process, e.g., sensor errors, measurement noise and quantization. B. Permissible Sensing Errors The condition on the hyperstability sensors and the measurement process can be relaxed because (11) is a sufficient condition for satisfying (6), but not a necessary condition, as discussed in [1]. We assume that the measurement process generates measurements which can be described by a mapping of the ideal hyperstability sensor outputs per (11). The individual measurement processes are characterized by mapping functions with an error parameter (12) If the absolute measurement errors are bounded by constants per Fig. 4, the mapping functions can be defined as follows: (13) It is apparent from Fig. 4 that only some approximation of the ideal characteristic is required. This broadens the choice of hyperstability sensors to realistic devices with error margins and limited resolution. (Additional and possibly tighter requirements on the sensors may arise from performance considerations.) Based on the definition (13) we may substitute the logic (10) by the following logic based on the nonideal measurement processes: (14) The logic (14) is not the same as (10) but can be shown to be sufficient to satisfy property (8) due to the fact that implies (but not vice-versa). V. EXPERIMENTAL VALIDATION A. Design of Experiment The instrumentation and sensor fusion concept has been experimentally validated in the laboratory experiment shown in Fig. 5. The laboratory robot features a very flexible link to emulate the flexibility characteristics of space robots such as the SSRMS. The experimental work aims at investigating problems associated with robotic assembly tasks on the Space Station, such as the airlock installation shown in Fig. 1. A related laboratory benchmark task is the precise positioning of payloads relative to objects in the workspace. As planned also for actual assembly operations on the Space Station, a Space Vision System (SVS) is used as a primary sensor to measure the payload position relative to a visual reference

5 112 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 47, NO. 1, FEBRUARY 1998 target in the workspace. This real-time photogrammetry-based measurement system, its characteristics and applications in space operations are discussed in [4] and [11]. Fig. 5 show the SVS targets on the tip of the robot arm and the reference target in the workspace. Measurement of the robot tip motion relative to the reference target enables the precise positioning of payloads with respect to objects in the workspace whose absolute position is not accurately known with respect to the base of the robot. However, using this measurement for control introduces severe dynamic stability problems due to the flexibility of the robot arm, and the noncollocation of sensors and actuators on the flexible arm, as discussed in [9]. This problem is addressed and solved by the instrumentation and sensor fusion concept proposed in Section III. The simplest case in the laboratory is a single joint motion of the robot. The joint is equipped with a tachometer which meets conditions (11) and (13) and is chosen as a hyperstability sensor providing a measurement The vision system is the performance sensor providing a measurement of the robot tip/payload position. As the control operator (9) can be chosen freely it may be based on conventional control techniques or intelligent control by fuzzy logic etc. The experimental results presented below were obtained with a simple standard control operator based on a lead filter as a feed-forward control term, and proportional feedback. This control operator can be expressed as a transfer function in the frequency domain ( complex frequency, set point, and are control gain constants),if,if not (15) The dynamic data fusion logic, which assures stability of the system under all conditions (e.g., unknown stiffness, variable payload) and for all but pathological choices for the control operator, is in this case given by: (true, false) (16) B. Experimental Results Fig. 6 shows a typical experimental result obtained while repositioning the tip of the flexible robot arm after a step change in the target position of roughly 20 in. One case without payload is compared to another with a 1.2 kg payload. The top diagram in Fig. 6 shows the response of the tip position as measured by the SVS, indicating a smooth transient converging toward the new set point. For the unloaded arm the convergence is more rapid than with the payload, as expected. The center diagram shows the motion of the 3rd joint of the robot. The joint angle completes a significant part of its overall travel before the tip even begins to move around s, indicating significant bending in the flexible link. However, the strain energy in the flexible link is reduced during the motion as the joint pauses at various times, leaving only relatively small residual vibrations at the completion of the joint motion. Fig. 6. Experimental results. Step responses with different payloads. The characteristic pausing is also evident from the bottom diagram showing the angular velocity of the joint, which is the hyperstability signal used in this design. The experimental results confirm the stability and robustness properties expected from the theoretical development. The results also indicate good performance in conjunction with a fairly simple control strategy and implementation.

6 STIEBER et al.: INSTRUMENTATION ARCHITECTURE AND SENSOR FUSION 113 VI. SUMMARY AND CONCLUSIONS We outlined a systematic procedure for the design of the instrumentation architecture for complex mechanical systems, such as space manipulators with significant flexible-body and nonlinear effects. In this approach, the sensors are classified as hyperstability sensors and performance sensors. While the performance sensors can be chosen freely to meet the performance requirements of the system, certain conditions apply to the hyperstability sensors. Sensor errors are taken into account to ensure that hyperstability sensor can be implemented with realistic, nonideal devices. A dynamic data fusion approach for integrating the measurements from the different types of sensors guarantees stability robustness of model-free control. An experimental evaluation of the proposed concept validates the methodology. REFERENCES [1] M. E. Stieber, E. Petriu, and G. Vukovich, Systematic design of instrumentation architecture for complex systems, IEEE Trans. Instrum. Meas., vol. 45, no. 2, pp , [2] M. E. Stieber, C. Trudel, and D. Hunter, Robotic systems for the international space station, Proc. IEEE Int. Conf. Robotics & Automation, 1997, pp [3] V. M. Popov, Hyperstability of Control Systems, Springer Verlag, [4] M. E. Stieber, M. McKay, G. Vukovich, and E. Petriu, Vision-based sensing and control for space robotics applications, Proc. IEEE Inst. Meas. Technology, Brussels, [5] H. Goldstein, Classical Mechanics. Reading, MA: Addison-Wesley, [6] L. Meirovitch, Analytical Methods in Vibration, Macmillan Co., [7] M. E. Stieber, E. Petriu, and G. Vukovich, Instrumentation and data fusion architecture for systems control, Proc. IEEE Inst. Meas. Technology Conf., Ottawa, [8] A. G. Kelkar, S. M. Joshi, and T. E. Alberts, Passivity-based control of nonlinear flexible multibody systems, IEEE Trans Automat. Contr., vol. 40, no. 5, pp , [9] M. E. Stieber, G. Vukovich, and E. Petriu, Stability aspects of visionbased sensing and control for space robots, Proc. IEEE Int Conf Robotics and Automation, 1997, pp [10] V. A. Spector and H. Flashner, Modeling and design implications of noncollocated control in flexible systems, Trans. ASME, J. Dyn. Syst., Meas. Contr., vol. 112, pp , June [11] S. G. MacLean and H. F. L. Pinkney, Machine vision in space, CASI J., vol. 39, no. 2, Michael E. Stieber (M 88) received the Dipl.-Ing. degree from Technische Universität München, Germany, in 1979 and the Ph.D. degree in electrical engineering from the University of Ottawa, Ottawa, Ont., Canada, in Currently he is with the Canadian Space Agency, working on the International Space Station project as Robotics Engineering Manager. He holds several patents on vehicle control systems. His research interests include control systems, instrumentation, robotics and the application of AI techniques to securities trading. Emil Petriu (M 86 SM 88), photograph and biography not available at the time of publication. George Vukovich, photograph and biography not available at the time of publication.

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