Whenever we need to start and stop the motor from more than one point, then we may expand it through push buttons in the motor control circuit ( for example, You may use this alternative power control wiring diagram of controlling a three phase motor from more than two places) .
We may connect;
Start push buttons in Parallel and,
Stop push buttons in Series,
to control ON / OFF operation of the motor from more than one place.
Whenever we need to provide emergency stop to the 3-Phase Motor, apart the motor Start and Stop push buttons, we may use many more (as needed) Stop push buttons (Use Start Push buttons in parallel and Stop push buttons in series).
The main advantage of using the Contactor, we can control i.e. Start and Stop the motor from any location
Suppose, you have to Control the Motor ON/OFF operation from more than two or three places, for this propose, you may use the following simple control circuit.
Abbreviations:
L1 , L2, L3 = Red, Yellow, Blue ( 3 Phase Lines)
N = Neutral
O/L = Over Load Relay
NO = Normally Open
K1 = Contactor (Contactor coil)
K1/NO = Contactor Holding Coil (Normally Open)
you might Also read:
Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. In this paper, we propose a modular approach to such motor learning and control. We review the behavioral evidence and benefits of modularity, and propose a new architecture based on multiple pairs of inverse (controller) and forward (predictor) models. Within each pair, the inverse and forward models are tightly coupled both during their acquisition, through motor learning, and use, during which the forward models determine the contribution of each inverse model's output to the final motor command. This architecture can simultaneously learn the multiple inverse models necessary for control as well as how to select the inverse models appropriate for a given environment. Finally, we describe specific predictions of the model, which can be tested experimentally.
Humans exhibit an enormous repertoire of motor behavior which enables us to interact with many different objects under a variety of different environments. The ability to perform in such a varying and often uncertain environment is a feature which is conspicuously absent from most robotic control, as robots tend to be designed to operate within rather limited environmental situations. In general, the problem of control can be considered as the computational process of determining the input to some system we wish to control which will achieve some desired output. In human motor control, the problem might be to select the input, i.e. motor command, to achieve some required output, i.e. desired sensory feedback. If we consider an example of lifting a can to ones lips, it may be that the desired output at a specific time is a particular acceleration of the hand as judged by sensory feedback. However, the motor command needed to achieve this acceleration will depend on many variables, both internal and external to the body. Clearly, the motor command depends on the state of the arm, i.e. its joint angles and angular velocities. The dynamic equations governing the system also depend on some relatively unvarying parameters, e.g. masses, moments of inertia, and center of masses of the upper arm and forearm. However, these parameters specific to the arm are insufficient to determine the motor command necessary to produce the desired hand acceleration; knowledge of the interactions with the outside world must also be known. For example, the geometry and inertial properties of the can will alter the arm's dynamics. More global environmental conditions also contribute to the dynamics, e.g. the orientation of the body relative to gravity and the angular acceleration of the torso about the body. As these parameters are not directly linked to the quantities we can measure about the arm, we will consider them as representing the context of the movement. As the context of the movement alters the input–output relationship of the system under control, the motor command must be tailored to take account of the current context.Considering the number of objects and environments, and their possible combinations, which can influence the dynamics of the arm (let alone the rest of the body), the motor control system must be capable of providing appropriate motor commands for the multitude of distinct contexts that are likely to be experienced. Given the abundance of contexts within which we must act, there are two qualitatively distinct strategies to motor control and learning. The first is to use a single controller which uses all the contextual information in an attempt to produce an appropriate control signal. However, such a controller would demand enormous complexity to allow for all possible scenarios. If this controller were unable to encapsulate all the possible contexts, it would need to adapt every time the context of the movement changed before it could produce appropriate motor commands—this would produce transient and possibly large performance errors. Alternatively, a modular approach can be used in which multiple controllers co-exist, with each controller suitable for one or a small set of contexts. Based on an estimate of the current context, some of the controllers could be activated to generate the appropriate motor command. Under such a modular strategy, there would need to be a context identification process which could choose the appropriate controllers from the set of all possible controllers.
Whenever we need to start and stop the motor from more than one point, then we may expand it through push buttons in the motor control circuit ( for example, You may use this alternative power control wiring diagram of controlling a three phase motor from more than two places) .
We may connect;
Start push buttons in Parallel and,
Stop push buttons in Series,
to control ON / OFF operation of the motor from more than one place.
Whenever we need to provide emergency stop to the 3-Phase Motor, apart the motor Start and Stop push buttons, we may use many more (as needed) Stop push buttons (Use Start Push buttons in parallel and Stop push buttons in series).
The main advantage of using the Contactor, we can control i.e. Start and Stop the motor from any location
Suppose, you have to Control the Motor ON/OFF operation from more than two or three places, for this propose, you may use the following simple control circuit.
Abbreviations:
L1 , L2, L3 = Red, Yellow, Blue ( 3 Phase Lines)
N = Neutral
O/L = Over Load Relay
NO = Normally Open
K1 = Contactor (Contactor coil)
K1/NO = Contactor Holding Coil (Normally Open)
you might Also read:
Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. In this paper, we propose a modular approach to such motor learning and control. We review the behavioral evidence and benefits of modularity, and propose a new architecture based on multiple pairs of inverse (controller) and forward (predictor) models. Within each pair, the inverse and forward models are tightly coupled both during their acquisition, through motor learning, and use, during which the forward models determine the contribution of each inverse model's output to the final motor command. This architecture can simultaneously learn the multiple inverse models necessary for control as well as how to select the inverse models appropriate for a given environment. Finally, we describe specific predictions of the model, which can be tested experimentally.
Humans exhibit an enormous repertoire of motor behavior which enables us to interact with many different objects under a variety of different environments. The ability to perform in such a varying and often uncertain environment is a feature which is conspicuously absent from most robotic control, as robots tend to be designed to operate within rather limited environmental situations. In general, the problem of control can be considered as the computational process of determining the input to some system we wish to control which will achieve some desired output. In human motor control, the problem might be to select the input, i.e. motor command, to achieve some required output, i.e. desired sensory feedback. If we consider an example of lifting a can to ones lips, it may be that the desired output at a specific time is a particular acceleration of the hand as judged by sensory feedback. However, the motor command needed to achieve this acceleration will depend on many variables, both internal and external to the body. Clearly, the motor command depends on the state of the arm, i.e. its joint angles and angular velocities. The dynamic equations governing the system also depend on some relatively unvarying parameters, e.g. masses, moments of inertia, and center of masses of the upper arm and forearm. However, these parameters specific to the arm are insufficient to determine the motor command necessary to produce the desired hand acceleration; knowledge of the interactions with the outside world must also be known. For example, the geometry and inertial properties of the can will alter the arm's dynamics. More global environmental conditions also contribute to the dynamics, e.g. the orientation of the body relative to gravity and the angular acceleration of the torso about the body. As these parameters are not directly linked to the quantities we can measure about the arm, we will consider them as representing the context of the movement. As the context of the movement alters the input–output relationship of the system under control, the motor command must be tailored to take account of the current context.Considering the number of objects and environments, and their possible combinations, which can influence the dynamics of the arm (let alone the rest of the body), the motor control system must be capable of providing appropriate motor commands for the multitude of distinct contexts that are likely to be experienced. Given the abundance of contexts within which we must act, there are two qualitatively distinct strategies to motor control and learning. The first is to use a single controller which uses all the contextual information in an attempt to produce an appropriate control signal. However, such a controller would demand enormous complexity to allow for all possible scenarios. If this controller were unable to encapsulate all the possible contexts, it would need to adapt every time the context of the movement changed before it could produce appropriate motor commands—this would produce transient and possibly large performance errors. Alternatively, a modular approach can be used in which multiple controllers co-exist, with each controller suitable for one or a small set of contexts. Based on an estimate of the current context, some of the controllers could be activated to generate the appropriate motor command. Under such a modular strategy, there would need to be a context identification process which could choose the appropriate controllers from the set of all possible controllers.
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