小型液壓挖掘機工作裝置的設計【說明書+CAD】

小型液壓挖掘機工作裝置的設計【說明書+CAD】,說明書+CAD,小型液壓挖掘機工作裝置的設計【說明書+CAD】,小型,液壓,挖掘機,工作,裝置,設計,說明書,仿單,cad LINEAR, NONLINEAR AND CLASSICAL CONTROL OF A 1/5TH SCALE AUTOMATED EXCAVATOR E. Sidiropoulou, E. M. Shaban, C. J. Taylor, W. Tych, A. Chotai Engineering Department, Lancaster University, Lancaster, UK, c.taylorlancaster.ac.uk Environmental Science Department, Lancaster University, Lancaster, UK Keywords: Identification; model-based control; proportional- integral-plus control; state dependent parameter model. Abstract This paper investigates various control systems for a laboratory robot arm, representing a scale model of an autonomous exca- vator. The robot arm has been developed at Lancaster Univer- sity for research and teaching in mechatronics. The paper con- siders the application of both classical and modern approaches, including: Proportional-Integral (PI) control tuned by conven- tional Ziegler-Nichols rules; linear Proportional-Integral-Plus (PIP) control, which can be interpreted as one logical exten- sion of the conventional PI approach; and a novel nonlinear PIP design based on a quasi-linear model structure, in which the parameters vary as a function of the state variables. The paper considers the pragmatic balance required in this context, between design and implementational complexity and the po- tential for improved closed-loop performance. 1 Introduction Construction is of prime economic significance to many indus- trial sectors. Intense competition, shortfall of skilled labour and technological advances are the forces behind rapid change in the construction industry and one motivation for automa- tion 1. Examples of excavation based operations include gen- eral earthmoving, digging and sheet-piling. On a smaller scale, trenching and footing formation require precisely controlled excavation. Full or partial automation can provide benefits such as reduced dependence on operator skill and a lower operator work load, both of which are likely to contribute to improve- ments in consistency and quality. However, a persistent stumbling block for developers is the achievement of adequate fast movement under automatic con- trol. Here, a key research problem is to obtain a computer controlled response time that improves on that of a skilled hu- man operator. This presents the designer with a difficult chal- lenge, which researchers are addressing using a wide range of approaches; see e.g. 2, 3, 4. This paper considers a laboratory robot arm, a 1/5th scale rep- resentation of the more widely known Lancaster University Computerised Intelligent Excavator (LUCIE), which has been developed to dig trenches on a construction site 4, 5. De- spite its smaller size and light weight, the 1/5th model has sim- ilar kinematic and dynamic properties to LUCIE and so pro- vides a valuable test bed for the development of new control strategies. In this regard, the paper considers both classical and modern approaches, including: Proportional-Integral (PI) control tuned by conventional Ziegler-Nichols rules; linear Proportional-Integral-Plus (PIP) control, which can be inter- preted as one logical extension of the conventional PI ap- proach 6, 7; and a novel nonlinear PIP design based on a quasi-linear model structure in which the parameters vary as a function of the state variables 8. Further to this research, the paper briefly considers utilisation of the robot arm as a tool for learning and teaching in mecha- tronics at Lancaster University. In fact, the laboratory demon- strator provides numerous learning opportunities and individ- ual research projects, for both undergraduate and postgraduate students. Development of the complete trench digging system requires a thorough knowledge of a wide range of technologies, including sensors, actuators, computing hardware, electronics, hydraulics, mechanics and intelligent control. Here, control system design requires a hierarchical approach with high-level rules for determining the appropriate end- effector trajectory, so as to dig a trench of specified dimen- sions. In practice, the controller should also include modules for safety and for handling obstructions in the soil 4. Finally, the high-level algorithm is coupled with appropriate low-level control of each joint, which is the focus of the present paper. 2 Hardware The robot arm has a similar arrangement to LUCIE 4, 5, ex- cept that this laboratory 1/5th scale model is attached to a work- bench and the bucket digs in a sandpit. As illustrated in Fig. 1, the arm consists of four joints, including the boom, dipper, slew and bucket angles. Three of these are actuated by hydraulic cylinders, with just the slew joint based on a hydraulic rotary actuator with a reduction gearbox: see 9 for details. The velocity of the joints is controlled by means of the applied voltage signal. Therefore, the whole rig has been supported by multiple I/O asynchronous real-time control systems, which allow for multitasking processes via modularisation of code written in Turbo C+ R. The computer hardware is an AMD- K6/PR2-166 MHz personal computer with 96 MB RAM. The joint angles are measured directly by mounting rotary po- tentiometers concentric with each joint pivot. The output sig- nal from each potentiometer is transmitted with an earth line to minimise signal distortion due to ambient electrical noise. Figure 1: Schematic diagram of the laboratory excavator showing the four controlled joints. These signals are routed to high linearity instrumentation am- plifiers within the card rack for conditioning before forwarding to the A/D converter. Here, the range of the input signal just af- ter conditioning does not exceed 5 volts. This A/D converter is a high performance 16 channel multiplexed successive ap- proximation converter capable of 12 bit conversion in less than 25 micro seconds. At present only eight available channels are being used. In the future, therefore, there would be no prob- lem for incorporating additional sensors into the system; e.g. a camera for detecting obstacles, to be used as part of the higher level control system, or force sensors. Valve calibration is essential to provide the arm joints with meaningful input values. This calibration is based on normal- izing the input voltage of each joint into input demands, which range from -1000 for the highest possible downward velocity to +1000 for the highest possible upward velocity of each joint. Here, an input demand of zero corresponds to no movement. Note that, without such valve calibration, the arm will gradu- ally slack down because of the payload carried by each joint. In open-loop mode, the arm is manually driven to dig the trench, with the operator using two analogue joysticks, each with two-degrees of freedom. The first joystick is used to drive the boom and slew joints while the other is used to move the dipper and bucket joints. In this manner, a skillful operator moves the four joints simultaneously to perform the task. By contrast, the objective here is to design a computer controlled system to automatically dig without human intervention. 3 Kinematics The objective of the kinematic equations is to allow for con- trol of both the position and orientation of the bucket in 3- dimensional space. In this case, the tool-tip can be pro- grammed to follow the planned trajectory, whilst the bucket angle is separately adjusted to collect or release sand. In this re- gard, Fig. 1 shows the laboratory excavator and its dimensions, i.e. i (joint angles) and li (link lengths), where i = 1,2,3,4 for the boom, dipper, bucket and slew respectively. Kinematic analysis of any manipulator usually requires devel- opment of the homogeneous transformation matrix mapping the tool configuration of the arm. This is used to find the po- sition, orientation, velocity and acceleration of the bucket with respect to the reference coordinate system, given the joint vari- able vectors 10. Such analysis is typically based on the well- known Denavit-Hartenberg convention, which is mainly used for robot manipulators consisting of an open chain, in which each joint has one-degree of freedom, as is the case here 9. 3.1 Inverse kinematics Given X,Y,Z from the trajectory planning routine, i.e. the position of the end effector using a coordinate system origi- nating at the workbench, together with the orientation of the bucket = 1+2+3, the following inverse kinematic algo- rithm is derived by Shaban 9. Here Ci and Si denotes cos(i) and sin(i) respectively, whilst C123 = cos(1 + 2 + 3). X = X l4C4 C4 l3C123 (1) Y = Y l3S123 (2) 1 = arctan bracketleftbigg(l 1 + l2C2) Y l2S2 X (l1 + l2C2) X l2S2 Y bracketrightbigg (3) 2 = arccos bracketleftbigg X2 + Y 2 l2 1 l22 2l1l2 bracketrightbigg (4) 3 = 1 2 (5) 4 = arctan bracketleftbiggZ X bracketrightbigg (6) 3.2 Trajectory planning Excavation of a trench requires both continuous path (CP) motion during the digging operation and a more primitive point-to-point (PTP) motion when the bucket is moved out of the trench for discharging. In particular, each digging cycle can be divided into four distinct stages, as follows: positioning the bucket to penetrate the soil (PTP); the digging process in a horizontal straight line along the specified void length (CP); Figure 2: Trajectory planning for the laboratory excavator. picking up the collected sand from the void to the discharge side (PTP); discharging the sand (CP). For the present example, the CP trajectory can be traversed at a constant speed. Suppose v0 and vf denote, respectively, the initial and final position vector for the end-effector and that the movement is required to be carried out in T seconds. In this case, the uniform straight-line trajectory for the tool-tip is, v = (1St)v0 + Stvf 0 t T (7) Here, St is a differentiable speed distribution function, where S0 = 0 and ST = 1. Typically, the speed profile St first ramps up at a constant acceleration, before proceeding at a constant speed and finally ramping down to zero at a constant deceler- ation. In the case of uniform straight-line motion, the speed profile will take the form St = 1/T. By integrating, the speed distribution function will be St = t/T. For this particular application, the kinematic constraints of the laboratory excavator allow for digging a trench with length and depth not exceeding 600 mm and 150 mm, respectively. Fig. 2 shows one complete digging cycle, illustrating the proposed path for the bucket. Note that each digging path is followed by picking up the soil to the point (270,150,0) with an orien- tation of 180 degrees using PTP motion. This step is followed by another PTP motion to position the bucket inside the dis- charging area at coordinate (100,100,400). The last step in the digging cycle is the discharging process which finishes at (600,100,700) with an orientation of -30 degrees. 4 Teaching and learning One of the most important features of engineering education is the combination of theoretical knowledge and practical expe- rience. Laboratory experiments, therefore, play an important role in supporting student learning. However, there are several factors that often prevent students from having access to such learning-by-doing interaction with robotic systems. These in- clude their high cost, fragility and the necessary provision of skilled technical support. Nonetheless, the utilization of robots potentially offers an excellent basis for teaching in a number of different engineering disciplines, including mechanical, elec- trical, control and computer engineering; e.g. 11, 12, 13, 14. Robots provide a fascinating tool for the demonstration of basic engineering problems and they also facilitate the development of skills in creativity, teamwork, engineering design, systems integration and problem solving. In this regard, the 1/5th scale representation of LUCIE pro- vides for the support of research and teaching in mechatronics at Lancaster University. It is a test bed for various approaches to signal processing and real-time control; and provides numer- ous learning opportunities and individual projects for both un- dergraduate and postgraduate research students. For example, since only a few minutes are needed to collect experimental data in open-loop mode, the robot arm provides a good labo- ratory example for demonstrating contrasting mechanistic and data-based approaches to system identification. With regards to control system design, various classical and modern approaches are feasible. However, the present au- thors believe that PIP control offers an insightful introduction to modern control theory for students. Here, non-minimal state space (NMSS) models are formulated so that full state vari- able feedback control can be implemented directly from the measured input and output signals of the controlled process, without resort to the design and implementation of a determin- istic state reconstructor or a stochastic Kalman Filter 6, 7. Indeed, a MEng / MSc module in Intelligent Control taught in the Department covers all these areas, utilising the robot arm as a design example. 5 Control methodology The benchmark PID controller for each joint is based on the well known Ziegler-Nichols methodology. The system is placed under proportional control and taken to the limit of sta- bility by increasing the gain until permanent oscillations are achieved. The ultimate gain obtained in this manner is subse- quently used to determine the control gains. An alternative ap- proach using a Nichols chart to obtain specified gain and phase margins is described by 15. Linear PIP control is a model-based approach with a similar structure to PID control, with additional dynamic feedback and input compensators introduced when the process has second order or higher dynamics, or pure time delays greater than one sample interval. In contrast to classical methods, however, PIP design exploits the power of State Variable Feedback (SVF) methods, where the vagaries of manual tuning are replaced by pole assignment or Linear Quadratic (LQ) design 6, 7. Finally, a number of recent publications describe an approach for nonlinear PIP control based on the identification of the fol- lowing state dependent parameter (SDP) model 8, yk = wTk pk (8) where, wTk = bracketleftbig yk1 ykn uk1 ukm bracketrightbig pk = bracketleftbig p1,k p2,k bracketrightbigT p1,k = bracketleftbig a1k ank bracketrightbig p2,k = bracketleftbig b1k bm k bracketrightbig Here yk and uk are the output and input variables respectively, while ai k(i = 1,2,.,n) and bj k(j = 1,.,m) are state dependent parameters. The latter are assumed to be functions of a non-minimal state vector Tk . For SDP-PIP con- trol system design, it is usually sufficient to limit the model (8) to the case that Tk = wTk . The NMSS representation of (8) is, xk+1 = Fkxk + gkuk + dyd,k (9) yk = hxk where the non-minimal state vector is defined, xk = bracketleftbigyk ykn+1 uk1 ukm+1 zkbracketrightbigT and zk = zk1 + yd,k yk is the integral-of-error between the command input yd,k and the output yk. Inherent type 1 servomechanism performance is introduced by means of this integral-of-error state. For brevity, Fk, gk, d, h are omitted here but are defined by e.g. 9, 16. The state variable feedback control algorithm uk = lkxk is subsequently defined by, lk = bracketleftbig f0,k . fn1,k g1,k . gm1,k kI,k bracketrightbig where lk is the control gain vector obtained at each sampling instant by either pole assignment or optimisation of a Linear Quadratic (LQ) cost function. With regard to the latter ap- proach, the present research uses a frozen-parameter system defined as a sample member of the family of NMSS models Fk, gk, d, h to define the P matrix 9, with the discrete-time algebraic Riccatti equation only used to update lk at each sam- pling instant. Finally, note that while the NMSS/PIP linear con- trollability conditions are developed by 6, derivation of the complete controllability and stability results for the nonlinear SDP system is the subject on-going research by the authors. 6 Control design For linear PIP design, open-loop experiments are first con- ducted for a range of applied voltages and initial conditions, all based on a sampling rate of 0.11 seconds. In this case, the Sim- plified Refined Instrumental Variable (SRIV) algorithm 17, suggests that a first order linear model with samples time delay, i.e. yk = a1yk1 + buk, provides an approximate representation of each joint. Here yk is the joint angle and uk is a scaled voltage in the range 1000, while a1,b are time invariant parameters. Note that the arm essentially acts as an integrator, since the normalised voltage has been calibrated so that there is no movement when uk = 0. In fact, a1 = 1 1000 800 600 400 200 0 200 400 600 800 10000.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 Parameter Scaled voltage Figure 3: Variation of b against input demand for the boom. is fixed a priori, so that only the numerator parameter b is estimated in practice for linear PIP design. With = 1, the dipper and bucket joints appear relatively straightforward to control using linear PIP methods. In this case, the algorithm reduces to a PI structure 6, hence the im- plementation results are similar to the PI algorithm tuned using classical frequency methods. As would be expected, the differ- ence between the classical and PIP methods for these joints is qualitative. Such differences relate only to the relative ease of tuning the algorithm to meet the stated control objectives. By contrast, with = 2, the slew and boom joints are better controlled using PIP methods since (as shown in numerous ear- lier publications) the latter automatically handles the increased time delay 9. Of course, an alternative solution to this prob- lem would be to introduce a Smith Predictor into the PI control structure. The authors are presently investigating the relative robustness of such an approach in comparison to PIP methods. However, further analysis of the open-loop data reveals limi- tations in the linear model above. In particular, the value of b changes by a factor of 10 or more, depending on the ap- plied voltage used, as illustrated in Fig. 3 for the case of the boom. Here, numerous experiments are conducted for a range of applied voltages and, in each case, SRIV methods used to estimate linear models. Fig. 3 illustrates these estimates of b plotted against the magnitude of the step input (the solid trace represents a straightforward polynomial fit). In fact, SDP analysis suggests that a more appropriate model for the boom takes the form of equation (8) with, wTk = bracketleftbig yk1 uk1 uk2 bracketrightbig pk = bracketleftbig a1k 0 b2k bracketrightbigT (10) where, a1k = 0.238106u2k2 1 b2k = 5.8459106uk2 + 0.01898 80 90 100 110 120 130 140 15020 0 20 40 60 80 90 100 110 120 130 140 1501000 500 0 500 1000 Figure 4: Top: linear PIP (thin trace), nonlinear SDP-PIP (thick) and command input (dashed) for the boom angle, plot- ted against sample number. Bottom: equivalent control inputs. The associated SDP-PIP control algorithm takes the form, uk = bracketleftbig f0,k g1,k kI,k bracketrightbigbracketleftbigyk uk1 zkbracketrightbigT (11) where the gains f0,k, g1,k and kI,k are updated at each sam- pling instant in the manner of a scheduled controller. Full de- tails of this approach and the equivalent SDP-PIP algorithms for the dipper, bucket and slew joints are given by Shaban 9. 7 Implementation Typical implementation results for the boom arm are illus- trated in Fig. 4, where it is clear that the SDP-PIP algorithm is more robust than the fixed gain, linear PIP algorithm (or equivalent classical PI controller) to large steps in the command level. Furthermore, the nonlinear approach yields a consider- ably smoother control input signal. Note that the linear and nonlinear controllers are designed to yield a similar speed of response in the theoretical case, i.e. the differences seen in Fig. 4 are due to the variation in b2 (Fig. 3) which is only taken account of in the SDP-PIP case. It should pointed out