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18-04-2010, 10:19 PM



This project and implimentation deals with a speed sensorless separately excited dc motor drive which uses the adaptive observer to estimate the rotor speed. The stability analysis of speed estimation is carried out. The modified feed forward control is integrated with the adaptive observer to simplify the implementation. The design guideline for feedback gain and the speed controller are also given to assure system stability for the entire operating region and Simulation results are also provided .
Separately excited dc motor drive systems are operated with torque control. Closed loop drives may be employed for speed control which requires the feedback speed signal from tacho generators or pulse encoders. The use of transducer may adversely affect the stable performance of the motor, increases the hardware complexity and costs[1]. Consequently there has been intense research on the development of speed estimation[2] by using the knowledge of motor parameters, input voltage and current to the motor, the speed estimation can be computed. However the closed loop estimator is more accurate than open loop estimator[3].
This project and implimentation consists of a speed sensorless separately excited dc motor drive with an adaptive observer to estimate the rotor speed (closed loop estimator).The stability of speed estimation was analyzed by using Routh Hurwitz criterion. The modified feed forward control is integrated with the adaptive observer to simplify the implementation.

2.1 Introduction: The electric motor is a machine which converts electric energy into mechanical energy. It depends for its operation on the force which is known to exist on a conductor carrying current while situated in a magnetic field.
Construction: A dc motor is similar in construction to a dc generator .A dc
machine mainly consists of two parts.
1. Stationary part: It is designed mainly for producing a magnetic flux.
2. Rotating part: It is called the armature, where the electrical energy is converted into mechanical energy.
The stationary part and the rotating parts are separated from each other by an air gap. The stationary part of a dc motor consists of main poles, designed to create the magnetic flux, commutating poles interposed between the main poles and designed to ensure sparkles operation of the3 brushes at the commutator and a frame/yoke.
The armature is a cylindrical body rotating in the space between the poles and comprising a slotted armature core, a winding inserted in the armature core slots , a commutator and brush gear. As a matter of fact any dc generator will run as a motor when its field and armature are connected to a source of direct current. The field winding produces the necessary magnetic field .The flow of current through the armature conductors produces a force which the armature.
1. Paper mills
2. Textile mills
3. Cranes
4. Printing press
1. High starting torque.
2. Accurate stepless speed control with constant torque.
3. Quick starting, stopping, reversing and accelerating.
1. High initial cost.
2. Increased operating and maintenance costs.
2.2 Principle of operation of dc motors:
Energy conversion:
As stated above, mechanical energy is changed into electrical energy by movement of conductor through a magnetic field. The converse of this is also true. If electrical energy is supplied to a conductor lying normal to a magnetic field, resulting in current flow in the conductor, a mechanical force and thus mechanical energy will be produced.
Producing mechanical force:
As in the generator, the motor has a definite relationship between the direction of the magnetic flux, the direction of motion of the conductor or force, and the direction of the applied voltage or current. Since the motor is the reverse of the generator, Fleming's left hand rule can be used. If the thumb and first two fingers of the left hand are extended at right angles to one another, the thumb will indicate the direction of motion, the forefinger will indicate the direction of the magnetic field, and the middle finger will indicate the direction of current. In either the motor or generator, if the directions of any two factors are known, the third can be easily determined.
The principle of operation of dc motor can be stated as follows: Whenever a current carrying conductor is placed in a magnetic field it experiences a force which is given by Flemingâ„¢s left hand rule.
Flemingâ„¢s left hand rule:
Stretch the thumb, the fore finger and the middle finger in such a
direction such that they are mutually perpendicular to each other, if the fore
finger represents magnetic field, the middle finger represents the direction of
the conventional current, the thumb represents the direction of the force.
Value of mechanical force:
The force exerted upon a current carrying conductor is dependent
upon the density of the magnetic field, the length of conductor, and the value
of current flowing in the conductor. Assuming that the conductor is located
at right angles to the magnetic field, the force developed can be expressed as follows:
F = BIL sin
F = force in newtons
B = flux density in lines per square meter
= length of the conductor in meters I = current in amperes.
At the same time torque is being produced, the conductors are
moving in a magnetic field and generating a voltage. This voltage is in opposition to
the voltage that causes current flow through the conductor and is referred to counter
voltage or back EMF. The value of current flowing through the armature is
dependent upon the difference between the applied voltage and the counter
In a dc motor when the armature rotates, the conductors on it cut
the lines of force of magnetic field in which they revolve, so that an e.m.f is induced in the armature as in a generator. The induced e.m.f acts in opposition to the current in the machine and therefore to the applied voltage, so that it is customary to refer which states that the direction of an induced e.m.f is such as to oppose the change causing it , which is of course , the applied voltage.
Eb = k Ø ZN/60*p/A
Where k is a number depending on nature of armature winding.
The value of the back e.m.f is always less than the applied voltage, although the differences is small when the machine is running under normal conditions. It is the difference between these two quantities which actually drives the current through the resistance of the armature circuit. If this resistance of the armature is represented by Ra, the back e.m.f by Eb and the applied voltage by V then we have
V = Eb + Ia Ra
Where Ia is the current in the armature circuit.
When the field of the machine is excited and a potential difference is impressed upon the machine terminals, the current in the armature circuit reacts with the air gap flux to produce a turning moment or torque which tends to cause the armature to revolve.
1. Separately excited dc motor
2 .Self excited dc motor
Self excited motors are of three types.
1. Shunt motor
2. Series motor
3. Compound motor
In separately excited dc motor, the field winding is excited separately. It may be observed that the field current is constant, since the field winding is connected directly to the supply which is assumed to be at constant voltage. Hence the flux is approximately constant and since also the back e.m.f is almost constant under these conditions the speed is approximately constant .It is therefore employed in practice for drives, the speeds of which are required to be the independent of the loads.
2.3 Characteristics of separately excited dc motor:
The characteristic curves of the motor are those curves which show the relation between the following quantities.
1. Torque and armature current i.e., Ta/Ia characteristic. This is also known as electrical characteristic.
2. Speed and torque i.e. T/Ia characterstic .
3. Speed and torque i.e., N/Ta characteristic. This is also known as mechanical characteristic.
When running on no-load, a small armature current flows to drive the machine against the friction and other losses in it. As the load is applied to the motor, and is increased the torque rises almost proportionally to the increase in the current. This is not quite true, because the flux has been assumed to be constant, whereas it decreases slightly owing to armature reaction .The effect of this is to cause the top of the curve connecting the torque and the line current to bend over as shown in fig.
The speed current characteristics of the motor is deduced from the following relation
N = K Eb [As flux is constant]
In the separately excited dc motor, the field circuit is connected to the supply terminals so that exciting current remains constant as long the temperature of the machine is constant, and field regulator is not adjusted .Actually as the machine warms up, the field resistance increases and the exciting current decreases by about 4% for every 10 C rise in temperature .Neglecting this effect and the armature reaction, it is seen that the speed of the motor falls slightly as the load increases. The fall in the speed is proportional to the volt drop IR in the armature circuit. If , however , we consider the effect of armature reaction, an increase of load causes a slight decrease in flux , unless the machine is fitted with the compensating windings. This weakening of the field tends to raise the speed, so that the actual fall in speed than that calculated by a consideration of the volt drop in the armature. On the whole, the separately excited dc motor may be regarded as one in which the speed is approximately constant, falling slightly as the load increases.
The speed of the separately excited motor can be increased by inserting the resistance in the field by means of a field regulator. This weakens the field and causes the motor to run faster in order to generate the necessary back e.m.f. Of course it is impossible to reduce the speed by this method below that at which it runs with no field resistance in the circuit
Fig 2.2
In the separately excited dc motor, as the load torque increases the speed falls below somewhat, but the machine is regarded as approximately constant speed motor.
1. For constant speed applications requiring medium starting torque.
2. For latches centrifugal pumps, blowers and fans, machine tools, wood
Working machines, reciprocating pumps.
3.1 Equations governing dynamic model of dc motor:
Assuming field excitation is held constant.
The differential equation governing the motor are given by
Ua (t) = Ra Ia (t) + La ( dIa (t)/dt ) + Eb (t)
Eb (t) = k w(t)
Tm(t) = k Ia(t)
dw(t)/dt = (Tm (t) “ TL (t) ) /J
Where in
Ua (t) : armature voltage Ia (t) : armature current
Eb (t) : induced voltage w (t) : angular velocity
Tm (t): electrical torque TL (t) : load torque
Ra (t): armature resistance La : armature inductance
J : moment of inertia k : constant
3.2 Block diagram:
4.1 Introduction
A familiar example of a control loop is the action taken to keep one's shower water at the ideal temperature. The person feels the water to estimate its temperature. Based on this measurement they perform a control action: use the hot water tap to adjust the process. The person would repeat this input-output control loop, adjusting the hot water flow until the process temperature stabilized at the desired value.
Feeling the water temperature is taking a measurement of the process value or process variable (PV). The desired temperature is called the set point (SP). The output from the controller and input to the process (the tap position) is called the manipulated variable (MV). The difference between the measurement and the set point is the error (e), too hot or too cold and by how much.
As a controller, one decides roughly how much to change the tap position (MV) after one determines the temperature (PV), and therefore the error. This first estimate is the equivalent of the proportional action of a PID controller. The integral action of a PID controller can be thought of as gradually adjusting the temperature when it is almost right. Derivative action can be thought of as noticing the water temperature is getting hotter or colder, and how fast, and taking that into account when deciding how to adjust the tap.
Making a change that is too large when the error is small is equivalent to a high gain controller and will lead to overshoot. If the controller were to repeatedly make changes that were too large and repeatedly overshoot the target, this control loop would be termed unstable and the output would oscillate around the set point in either a constant, growing, or decaying sinusoid. A human would not do this because we are adaptive controllers, learning from the process history, but PID controllers do not have the ability to learn and must be set up correctly. Selecting the correct gains for effective control is known as tuning the controller.
If a controller starts from a stable state at zero error (PV = SP), then further changes by the controller will be in response to changes in other measured or unmeasured inputs to the process that impact on the process, and hence on the PV. Variables that impact on the process other than the MV are known as disturbances and generally controllers are used to reject disturbances and/or implement set point changes. Changes in feed water temperature constitute a disturbance to the shower process.
In theory, a controller can be used to control any process which has a measurable output (PV), a known ideal value for that output (SP) and an input to the process (MV) that will affect the relevant PV. Controllers are used in industry to regulate temperature, pressure, flow rate, chemical composition, level in a tank containing fluid, speed and practically every other variable for which a measurement exists Automobile cruise control is an example of a process which utilizes automated control. Due to their long history, simplicity well grounded theory and simple setup and maintenance requirements, PID controllers are the controllers of choice for many of these applications.
Controller: A controller is a device introduced in the system to modify the error signal and to produce a control signal
4.2 Proportional controller:
Where the proportional term makes a change to the output that is proportional to the current error value. The proportional response can be adjusted by multiplying the error by a constant Kp, called the proportional gain.
The proportional term is given by: Pout = Kp e(t)
¢ Pout: Proportional output
¢ Kp: Proportional Gain, a tuning parameter
¢ e: Error = SP - PV
¢ t: Time or instantaneous time (the present)
Change of response for varying Kp:
A high proportional gain results in a large change in the output for a given change in the error. If the proportional gain is too high, the system can become unstable (See the section on Loop Tuning). In contrast, a small gain results in a small output response to a large input error, and a less responsive (or sensitive) controller. If the proportional gain is too low, the control action may be too small when responding to system disturbances.
In the absence of disturbances, pure proportional control will not settle at its target value, but will retain a steady state error that is a function of the proportional gain and the process gain. Despite the steady-state offset, both tuning theory and industrial practice indicate that it is the proportional term that should contribute the bulk of the output.
Fig 4.1
4.3 Integral controller:
The contribution from the integral term is proportional to both the magnitude of the error and the duration of the error. Summing the instantaneous error over time (integrating the error) gives the accumulated offset that should have been corrected previously. The accumulated error is then multiplied by the integral gain and added to the controller output. The magnitude of the contribution of the integral term to the overall control action is determined by the integral gain, Ki.
The integral term is given by: Iout = Ki e(t)
Change of response for varying Ki
¢ Iout: Integral output
¢ Ki: Integral Gain, a tuning parameter
¢ e: Error = SP - PV
¢ t: Time in the past contributing to the integral response
The integral term (when added to the proportional term) accelerates the movement of the process towards set point and eliminates the residual steady-state error that occurs with a proportional only controller. However, since the integral term is responding to accumulated errors from the past, it can cause the present value to overshoot the set point value
Fig 4.2
Derivative controller:
The rate of change of the process error is calculated by determining the slope of the error over time (i.e. its first derivative with respect to time) and multiplying this rate of change by the derivative gain Kd. The magnitude of the contribution of the derivative term to the overall control action is termed the derivative gain, Kd.
The derivative term is given by: Dout = Kd de(t)
Change of response for varying Kd
¢ Dout: Derivative output
¢ Kd: Derivative Gain, a tuning parameter
¢ e: Error = SP - PV
¢ t: Time or instantaneous time (the present)
The derivative term slows the rate of change of the controller output and this effect is most noticeable close to the controller set point. Hence, derivative control is used to reduce the magnitude of the overshoot produced by the integral component and improve the combined controller-process stability. However, differentiation of a signal amplifies noise and thus this term in the controller is highly sensitive to noise in the error term, and can cause a process to become unstable if the noise and the derivative gain are sufficient.
Fig 4.3
The output from the three terms, the proportional, the integral and the derivative terms are summed to calculate the output of the PID controller. Defining u(t) as the controller output, the final form of the PID algorithm is:
and the tuning parameters are
1. Kp: Proportional Gain - Larger Kp typically means faster response since the larger the error, the larger the Proportional term compensation. An excessively large proportional gain will lead to process instability and oscillation.
2. Ki: Integral Gain - Larger Ki implies steady state errors are eliminated quicker. The trade-off is larger overshoot: any negative error integrated during transient response must be integrated away by positive error before we reach steady state.
3. Kd: Derivative Gain - Larger Kd decreases overshoot, but slows down transient response and may lead to instability due to signal noise amplification in the differentiation of the error.
4.4 PID controller:
A proportional“integral“derivative controller (PID controller) is a generic control loop feedback mechanism widely used in industrial control systems. A PID controller attempts to correct the error between a measured process variable and a desired set point by calculating and then outputting a corrective action that can adjust the process accordingly.
The PID controller calculation (algorithm) involves three separate parameters; the Proportional, the Integral and Derivative values. The Proportional value determines the reaction to the current error, the Integral determines the reaction based on the sum of recent errors and the Derivative determines the reaction to the rate at which the error has been changing. The weighted sum of these three actions is used to adjust the process via a control element such as the position of a control valve or the power supply of a heating element.
By "tuning" the three constants in the PID controller algorithm the PID can provide control action designed for specific process requirements. The response of the controller can be described in terms of the responsiveness of the controller to an error, the degree to which the controller overshoots the set point and the degree of system oscillation. Note that the use of the PID algorithm for control does not guarantee optimal control of the system or system stability.
Some applications may require using only one or two modes to provide the appropriate system control. This is achieved by setting the gain of undesired control outputs to zero. A PID controller will be called a PI, PD, P or I controller in the absence of the respective control actions. PI controllers are particularly common, since derivative action is very sensitive to measurement noise, and the absence of an integral value may prevent the system from reaching its target value due to the control action.
Fig 4.4
Note: Due to the diversity of the field of control theory and application, many naming conventions for the relevant variables are in common use.
Limitations of PID control:
While PID controllers are applicable to many control problems, they can perform poorly in some applications. PID controllers, when used alone, can give poor performance when the PID loop gains must be reduced so that the control system does not overshoot, oscillate or "hunt" about the control set point value. The control system performance can be improved by combining the feedback (or closed-loop) control of a PID controller with feed forward (or open-loop) control. Knowledge about the system (such as the desired acceleration and inertia) can be "fed forward" and combined with the PID output to improve the overall system performance. The feed-forward value alone can often provide the major portion of the controller output. The PID controller can then be used primarily to respond to whatever difference or "error" remains between the set point (SP) and the actual value of the process variable (PV). Since the feed-forward output is not affected by the process feedback, it can never cause the control system to oscillate, thus improving the system response and stability.
For example, in most motion control systems, in order to accelerate a mechanical load under control, more force or torque is required from the prime mover, motor, or actuator. If a velocity loop PID controller is being used to control the speed of the load and command the force or torque being applied by the prime mover, then it is beneficial to take the instantaneous acceleration desired for the load, scale that value appropriately and add it to the output of the PID velocity loop controller. This means that whenever the load is being accelerated or decelerated, a proportional amount of force is commanded from the prime mover regardless of the feedback value. The PID loop in this situation uses the feedback information to effect any increase or decrease of the combined output in order to reduce the remaining difference between the process set point and the feedback value. Working together, the combined open-loop feed-forward controller and closed-loop PID controller can provide a more responsive, stable and reliable control system.
Another problem faced with PID controllers is that they are linear. Thus, performance of PID controllers in non-linear systems (such as HVAC systems) is variable. Often PID controllers are enhanced through methods such as PID gain scheduling or fuzzy logic. Further practical application issues can arise from instrumentation connected to the controller. A high enough sampling rate, measurement precision, and measurement accuracy are required to achieve adequate control performance.
A problem with the Derivative term is that small amounts of measurement or process noise can cause large amounts of change in the output. It is often helpful to filter the measurements with a low pass filter in order to remove higher-frequency noise components. However, low-pass filtering and derivative control can cancel each other out, so reducing noise by instrumentation means is a much better choice. Alternatively, the differential band can be turned off in many systems with little loss of control. This is equivalent to using the PID controller as a PI controller.
5.1 Introduction
An adaptive observer is defined as one which estimates the state variables and parameters of an unknown stable linear time-invariant plant from its input-output data. At the present time, there are two distinct approaches to the design of adaptive observers for a plant whose input output behavior can be represented by an n-th order differential equation. In the first approach, the observer is of the same order as the plant and is referred to as a minimal (order) observer. Using the second approach, a non-minimal observer of order (2n-1) is obtained. Minimal observers are considerably more difficult to synthesize than non-minimal observers and require the generation of additional signals for the stabilization of the adaptive loop. However, they have the advantage of yielding simultaneously both parameter and state estimates of the plant. Non-minimal observers are considerably simpler in structure both the n state variables of the plant have to be estimated from the available (2n-1) state variables of the observer. A new observer is proposed which appears to combine the advantages of the two types of observers described above, With this observer, the parameter estimates of the plant are directly obtained with a structure which is no more complex than that of a non-minimal observer which is widely used at the present time. The parameter estimates are simultaneously used to determine directly the state estimates of the plant. Under certain conditions, the new observer has a faster rate of convergence than the observers known at present, which makes it particularly attractive for use in the control problem.
5.2 Building of an adaptive observer:
Using armature voltage and current observer can be built for the dc motor which estimate state variable (armature current) is given by
From equation (5) adaptive observer can be built which estimate the speed instead of actual speed is given
Adaptive observer :
Speed estimation :
From the equation (5) and (6) the error equation Where H are the feedback gains of the adaptive observer and ˜^™ denotes the estimated value of the adaptive observer is given by
From the error equation the speed estimation error can be derived by
From equation (7) and (9) the block diagram shown below is obtained
6.1Conditions for a stable system:
A system is said to be stable if its output is bounded for any bounded input. The closed loop transfer function can be expressed as a ratio of two polynomials in s. The denominator polynomial of closed loop transfer function is called characteristic equation. The roots of the characteristic equation are poles of the closed loop transfer function .A linear relaxed systems is said to have BIBO stability if every bounded input results in a bounded output.
For BIBO stability the integral of impulse response should be finite, which implies that the impulse response should be finite asËœtâ„¢ tends to infinity. The impulse response is the inverse Laplace transform of the transfer function. This requirement for stability can be linked to the location of the roots of characteristic equation in the s-plane.
The following conclusions can be drawn based on the location of the roots of characteristic equation.
1. If all the roots of characteristic equation have negative real parts (i.e., lying on the left half of s-plane) then the impulse response is bounded. Hence the system is bounded input “bounded output stable.
2. If any root of the characteristic equation has a positive real part(i.e. ,lying on the right half of s-plane)then the impulse response is unbounded, the system is unstable.
3. If the characteristic equation has repeated roots on the imaginary axis, then the system is stable.
4. If one or more non repeated roots of the characteristic equation are lying on the imaginary axis, then the system is unstable.
5. If the characteristic equation has a single root at origin then the impulse response is unbounded and so the system is unstable.
6. If the characteristic equation has repeated roots at origin, then the system is unstable.
In system with one or more repeated roots on the imaginary axis or with the single root at origin, the output is bounded for bounded inputs except for the inputs having poles matching the system poles. These cases may be treated as acceptable or non acceptable. Hence when the system has non repeated poles on the imaginary axis or single pole at origin, it is referred as limitedly marginally stable system.
The following three points can be stated regarding the stability of the system depending on the location of the poles.
1. If all the roots of the characteristic equation has negative real parts , then the system is stable.
2. If any root of the characteristic equation has a positive real part or if there is a repeated root on the imaginary axis then the system is un stable.
3. If the condition (1) is satisfied except for the presence of one or more non repeated roots on the imaginary axis, then the system is limitedly or marginally stable.
The following are the conditions about the coefficients of the characteristic polynomial for a stable system.
1. If all the coefficients are positive and if no coefficient is zero then all the roots are in the left half of s-plane.
2. If any coefficient is equal to zero then some of the roots may be on the imaginary or on the right half of s-plane.
3. If any coefficient is negative then at least one root is in the right half of s-plane.
Thus the necessary condition for the stability of the system is that all the coefficients of its characteristic polynomial be positive. If any coefficient is zero/negative, then the system is unstable.
6.2 Routh Hurwitz criterion:
The roots of the characteristic equation of a stable system should lie on the left half of s-plane .Hence the roots should have negative real parts .The Routh-Hurwitz stability criterion is an analytical procedure for determining whether all the roots of a polynomial have negative real parts or not.
The necessary condition for stability is that all the coefficients of the polynomial be positive. If some of the coefficients are zero or negative it can be concluded that the system is not stable. When all the coefficients are positive , the system is not necessarily stable .Even though the coefficients are positive some of the roots may lie on the right half of s-plane or on the imaginary axis .In order for all the roots to have negative real parts, it is necessary but not sufficient that al of the coefficients of the characteristic equation be positive .If all the coefficients of the characteristic equation are [positive , then the system may be stable and one should proceed further to examine the sufficient conditions of stability.
A .Hurwitz and E.J. Routh independently published the method of investigating the sufficient conditions for the stability of the system.
The Routh stability criterion can be stated as follows.
The necessary and sufficient condition for stability is that all of the elements in the first column of the Routh array be positive. If this condition is not met, the system is unstable and the number of sign changes in the elements of the first column of the Routh array corresponds to the number of roots of the characteristic equation in the right half of s-plane.
6.3 Conditions for Stability of speed estimation:
The block diagram shown in figure (2) will be used for stability analysis which may be described by open loop transfer function
From the transfer function in equation (10) the Routh Hurwitz criteria can be applied for the analysis of stability of speed estimation
Consequently the speed estimation system will be stable and given to render the stability for the entire operating region .If the feedback gain H is designed ton
satisfy the relation (11) the result obtained is the estimate and actual speed become equal in to steady state.
Integration of feed forward control and adaptive observer:
From the rotor speed and armature current in equation (5) we can control the electromagnetic torque of the motor by using armature current.
However we can control armature current through the armature voltage by using feed forward control as
Where ˜*™ denotes the commanded value
Modified feed forward control:
This method is to merge the feed forward control with the feedback control by adding a feedback term as equation (13)by selecting a suitable feedback gain kf the control performance of the torque control system can be improved. If kf = -La*H is substituted in equation (13) the command voltage becomes
From the command voltage (14) the armature current dynamic of adaptive observer can be built by equation (14) into (6) yields
Block diagram
Wave forms:
Waveforms obtained on applying commanded speed in pulse form i.e. of amplitude 1200 r.p.m .load torque was applied . The various waveforms obtained are as follows Commanded speed w*
A commanded speed w* which is a small sample of amplitude 1200 r.p.m is given to the fallowing block diagram in the pulse form.
Actual speed waveform
A second order response obtained for a given command increase in speed. It should fallow the command increase in speed.
Estimated speed waveform
It is an estimated speed wave form which fallows the actual speed wave form .its response is also a second order response .it estimates the actual speed. Steady state speed error is zero.
Armature current wave form
It increases suddenly when the speed of a motor suddenly increases and it comes to steady state value that means the electromagnetic torque equals the load torque.
Load torque TL wave form
Load torque was not applied so load torque remains at a zero value.
Electromagnetic torque waveform
Load torque was not applied when the speed increases suddenly the electromagnetic torque raises suddenly and comes to a steady state that means load torque equals the electromagnetic torque.
Waveforms obtained when a load torque of 15 nm are applied for a few seconds are as follows
Load torque TL Waveform
A Load torque of 15 nm is applied in pulse form for a small amount of time.
Electromagnetic torque waveform
When the load torque increases suddenly the electromagnetic torque increases suddenly to cope up with load torque to keep the motor in steady state.
Actual speed waveform
The speed is at 1200r.p.m actually but when the load torque was applied speed decreases suddenly and again cope up with the steady state speed i.e. at 1200 r.p.m
Estimated speed waveform
Estimated speed waveform is same as actual speed as it fallows the actual speed waveform.
Difference in estimated and actual speed
There will be a little error in estimating the actual speed .Whenever there is a change in sudden change in commanded speed or torque both actual speed and estimated speed will respond with some time difference. Steady state speed error is zero
Armature current waveform
As torque is proportional to armature current, armature current waveform will be same as that of armature torque waveform.
In this project and implimentation speed sensorless separately excited dc motor drive was proposed which uses the adaptive observer to estimate the rotor speed, we analyzed the stability of the speed estimation drive system by using the Routh Hurwitz criterion. The modified feed forward control is integrated with the adaptive observer to reduce the complexity of the whole system and to simplify the implementation. The design guideline for the feedback gain and the speed controller are also given to assure the system stability for the entire operating region. Simulation results were presented to verify the validity of the system.
The motors parameters and ratings are as fallows
Power=0.75 kw
Speed=2000 rpm
Ra= 7.55
La= 0.114H
J= 0.01287kg.m2
Adaptation gain:
Kp= 111
Ki = 55510
1. P.C.Sen, Electric Motor Drives and Control : Past, Present and Future,
IEEE Trans on Industrial Drives, Vol.IE37, No.6, pp.562-575, 1990.
2.M.I.Jahmeerbacus, M.K.Oolun, C.Bhurtun and K.M.S. Soyjaudah,
Speed- Sensor less control of a Converter “fed Dc Motor, In Africa.
IEEEâ„¢99, pp. 453- 456, 1999.
3. S.Suwankawin and S.Sangwongwanich, A Speed-Sensorless IM Drive
With Decoupling control and Stability Analysis of Speed Estimation, IEEE
Trans on Industrial Electronics, Vol.49, No.2, pp 444-455, 2002.
4. W. Leonhard, control of Electric drives, Third edition, Springer-Veralg,
Berlin, Heidlberg, Germany, 2001.
5. Dc machines by R.K.Rajput

Abstract 4
Chapter 1 Introduction 5
Chapter 2.0 Separately excited dc motor drive 6
2.1 Introduction to dc motor 7
2.2 Principle of operation 8
2.3 Characteristics of separately excited dc
motor 11
Chapter 3.0 Dynamic model of separately excited dc
motor 15
3.1 Equations governing the operation of
dc motor 16
3.2 Block diagram representation 17
Chapter 4.0 Controllers 18
4.1 Introduction 19
4.2 Proportional controller 20
4.3 Integral controller 22
4.4 PID controller 25
Chapter 5.0 Adaptive observer 29
5.1 Introduction 30
5.2 Building of an adaptive observer 31
Chapter 6.0 Stability analysis 33
6.1 Conditions for a stable system 34
6.2 Routh Hurwitz criteria 36
6.3 Condition for the stability of speed
Estimation 37
Chapter 7 Simulation 38
Chapter 8 Results 41
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20-06-2010, 11:00 PM

i'm doing a paper on SPEED SENSORLESS SEPARATELY EXCITED DC MOTOR DRIVE WITH AN ADAPTIVE OBSERVER. i require a detailed information on creating a motor, with the necessary tools and instructions to create it.. pls help...!

thank you,
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05-10-2010, 03:01 PM

.pdf   getPDF.pdf (Size: 2.05 MB / Downloads: 82)

*Samart Yachiangkam **Cherdchai Prapanavarat **Udomsak Yungyuen and **Sakorn Po-ngam
* Department of Electrical Engineering, Rajamangala Institute of Technology, Northern Campus
128 Huay Kaew Road, Muang District, Chiangmai 50300 Thailand.
** Department of Electrical Engineering, Faculty of Engineering
King Mongkut's University of Technology Thonburi
91 Prachauthit Road, Bangmod, Thungkhru District, Bangkok 10140 Thailand.

This paper presents a speed-sensorless separately excited dc motor drive which uses the adaptive observer to estimate the rotor speed. In this paper, a stability analysis of the speed estimation is carried out. The modified feedforward control is integrated with the adaptive observer to reduce the model redundancy and to simplify the implementation. The design guideline for the feedback gain and the speed controller are also given to assure the system stability for the entire operating region. Simulation and experimental results are given to verify the validity of the proposed method.

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