MOHAMMAD SYAZWAN BIN TALIB B020910019MUHAMAD HAFFIZ BIN MOHD RADZI B020910190MUHAMMAD SHAKIR BIN SULAIMAN B020810095MUHAMMAD FARHAN BIN MOHD ROSLAN B020910115 EN. RIDZA AZRI BIN RAMLEEFAKULTI KEJURUTERAAN ELEKTRONIK & KEJURUTERAAN KOMPUTER
To learn open-loop and close loop system To learn linear and nonlinear control To design a simple manipulator controller based on several techniques
Limited Sequence Control – pick-and-place operations using mechanical stops to set positions. Playback with point to point control – records work cycle as a sequence of points, then plays back the sequence during program execution. Playback with continuous path control – greater memory capacity and/or interpolation capability to execute paths (in addition to points). Intelligent control – exhibits behavior that makes it seem intelligent, e.g., responds to sensor inputs, makes decisions, communicates with humans.
Electric – Uses electric motors to actuate individual joints – Preferred drive system in todays robots Hydraulic – Uses hydraulic pistons and rotary vane actuators – Noted for their high power and lift capacity Pneumatic – Typically limited to smaller robots and simple material transfer applications
Closed-loop (feedback) control system Open-loop control system
A system in which the output variable is compared with an input parameter, and any difference between the two is used to drive the output into agreement with the input. Figure 2 : Control-loop System
operates without the feedback loop Simpler and less expensive Risk that the actuator will not have the intended effect Figure 3 : Open-loop System
Linear Control of Manipulators Feedback and Closed-loop Control Figure 4 : Block Diagram Manipulator Control System
The initial and the final location of end-effector in Cartesian space, specified by the transformation matrices Tinitial and Tfinal, serve as the input. The inverse kinematics model computes the desired end-effector location in joint space. Then, a trajectory generator computes the joint-position time histories, based on the joint-space algorithms. Depending on the servo error computed from the base reference values and the sensor measurements, the control system commands the individual actuators to achieve the desired motion. Figure 5 : Operation for point-to point motion control of a manipulator
The use of linear-control techniques is valid only when the system being studied can be modeled mathematically by linear differential equations. We have to calculate joint position time histories that correspond to desired end-effector motions through space. For the case of manipulator control, such linear methods must be viewed as approximate methods, for the dynamics of a manipulator are more properly represented by a nonlinear differential equation. It is often reasonable to make such approximations methods are most often used in current industrial practice. The justification for using linear controllers is not only empirical. There is a certain linear controller leads to a reasonable control system even without resorting to a linear approximation of manipulator dynamics.
A control system that makes use of feedback is called a closed-loop control system. The "loop" closed by such a control system around the manipulator is apparent in the figure. The only way to build a high-performance control system is to make use of feedback from joint sensors, as indicated in the figure. Typically, this feedback is used to compute the servo error by finding the difference between the desired and the actual position and that between the desired and the actual velocity. The control system can then compute how much torque is required as a function to reduce servo errors.
The central problem is designing a closed-loop system that meets certain performance specifications. First of all, the system has to remain stable. We will define a system to be stable, if the errors remain "small" when executing various desired trajectories even in the presence of some disturbances. An improperly designed control system can sometimes result in unstable performance, in which servo errors are enlarged instead of reduced. The second requirement is that the closed-loop performance of the system is satisfactory. In practice, such "proofs" range from mathematical proofs based on certain assumptions and models to more empirical results, such as those obtained through simulation or experimentation.
Figure 6 : Robot Control Architecture for n-DOF manipulator
The manipulator control problem is a multi- input, multi-output (MIMO)problem, involving joint and the end-effector locations, velocities, accelerations, and force vectors. To simplify the problem, each joint is considered to be independent and separately controlled. This single-joint model is assumed to have a single input(set point) and single output(location, velocity, etc..). Hence, the n-DOF manipulator is modelled as n- independent linear second-order system and is controlled by n-independent single-input, single- output (SISO)control systems. Before developing and analyzing the linear second- order SISO model of the joint, the general second-order linear system characteristics are briefly brushed up first.
To overcome unmodeled dynamics, variablepayloads, fiction and disturbance torque, variationand noise, we use nonlinear control to : Tracking, regulate state, state set point Ensure the desired stability properties Ensure the appropriate transients Reduce the sensitivity to plant parameters
A lot of techniques that are used for nonlinearsystems come from linear systems, because: Nonlinear systems can (sometime) be approximated by linear systems. Nonlinear systems can (sometime) be “transformed "into linear systems. The tools are generalized and extended.
Proportional and Derivative Control (PD) Proportional Integral and Derivatives (PID) State Space Controller (Poles Placement Technique) Artificial Intelligence Controller (Fuzzy Logic & Neural Network) Adaptive Cruise Controller (ACC) Cooperative Adaptive Cruise Controller (CACC) Model Predictive Controller (MPC). Robust Control and etc..
John J. Craig - Introduction To Robotics - Mechanics And Control. R K Mittal & I J Nagrath, “Robotics and Control”, Tata McGraw-Hill Publishing Company. Ltd., New Delhi,2003