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Machining Dynamics
Published in David A. Stephenson, John S. Agapiou, Metal Cutting Theory and Practice, 2018
David A. Stephenson, John S. Agapiou
Third-generation active control schemes were multivariable control schemes designed by Mitchell and Harrison [194]. They used the best features of each type of control scheme, and, simultaneously, eliminated some of their drawbacks. The predictive controller was an open loop, feed forward system, whereas the chase controller was a closed loop, feedback system; the multivariable controller was basically a closed loop, feed forward system. A block diagram of the control system, which employs a hardware observer, is shown in Figure 12.29. Observer theory was applied so that the active control scheme could effectively reduce chatter as well as attenuate noise effects. The state estimator or observer can be effective because it is not easy to determine the machine tool dynamic characteristics, which depend on the positions of movable elements, and because it is difficult to measure the necessary states of the rotating workpiece, such as workpiece center line position, velocity, or acceleration. An observer is actually a closed loop system itself which uses the same input signal as the observed system and compares its output with the observed system output. The observer tends to force the tool to follow the workpiece motion.
Power Converters
Published in Vadim Utkin, Jürgen Guldner, Jingxin Shi, Sliding Mode Control in Electro-Mechanical Systems, 2017
Vadim Utkin, Jürgen Guldner, Jingxin Shi
As a very important aspect of this chapter, observer-based control approaches have been presented, either asymptotic observers or sliding mode observers. It was shown that an observer-based control system may achieve a higher control performance than a non-observer-based control system. To reduce the number of sensors, sliding mode observers play an important role in the control design. The information is extracted through the concept of equivalent control; thus, no high-order time derivatives of the internal state are necessary. Simulation and experimental results confirmed the effectiveness of the proposed control approaches.
State observer design for microchannel cooling system using extended Kalman filter
Published in Numerical Heat Transfer, Part A: Applications, 2023
An observer is an algorithm that provides estimates of the internal system states based on the measurements of system inputs and outputs. The observer could process the incomplete and imperfect information provided by the sensors and thereby construct a reliable estimate of the whole system states [12]. Since the number and quality of the sensors are often limited due to cost and physical constraints, the observer thus plays a decisive role in a lot of applications [13,14]. Studies on observers have been conducted in various scientific fields, including robotics [15], chemical systems [16], medical systems [17], and aerospace systems [18]. Different methods have been applied for observer development, including the Kalman filter, extended Kalman filter (EKF), unscented Kalman filter, and particle filter. In this study, we focus on the design and application of EKF.
Fractional order adaptive Kalman filter for sensorless speed control of DC motor
Published in International Journal of Electronics, 2023
Ravi Pratap Tripathi, Ashutosh Kumar Singh, Pavan Gangwar
In all industrial work, mechanical movement is carried out with the use of electric motors, hydraulics and pneumatic systems. To drive these systems, motors are required. For motion transmission systems generally, AC motors are used while DC motors are used in robotic manipulators and in industrial applications, where high load torque is needed. To implement the industrial task, various control approaches are used, in general emphasis is given to controlling the speed of the machines. To achieve effective speed control, a closed-loop control system is used, in which motor state variables like current, position and rotor speed are feedbacked. A suitable controller can be implemented when all the state variables of the system are known. To measure the entire state variable, it uses various sensors; thereby cost increases and makes the system more complex. To reduce the number of sensors, hardware complexity and system cost, state observers/estimators are used in the control loops of electrical motors (Jang et al., 2003; Mohan et al., 2020). The advantages of state observers over sensors are that they are not affected by any environmental causes and any machine distortions (Wang et al., 2019). Some sensorless speed control methods were performed (Lascu et al., 2006; Raca et al., 2008). An observer is a mathematical tool that estimates states with the help of system dynamical models and some measured state variables.
Sliding mode observer-based fault detection for helicopter system
Published in Journal of Control and Decision, 2022
M. Raghappriya, S. Kanthalakshmi
An observer, based on the measured inputs and outputs, estimates the unmeasurable states of the system. The observer's structure is a mathematical model of a system having two inputs: a system input and an error signal. The difference between the measured and observed output is referred to as error and the main aim of the observer is to make the estimation error as zero. The SMO is designed based on the error model. An SMO uses a non-linear switching term to feedback the output estimation error. In the presence of bounded non-linearities/uncertainties in the system, a Lyapunov-based strategy is utilised to design an observer that reduces state error to zero in finite time. Also, the observer's switching term ensures that the error is bounded. Since the error remains bounded, the observer gains also remain bounded.