Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE) PDF Download

Lecture 33 - Set Point Tracking, Control Systems

 

1 Set Point Tracking 

We discussed in the last lecture that a state feedback design can be done to place poles such that the system is stable. However, the tracking is not guaranteed.

1.1 Feed Forward Gain Design 

Consider the state space model

x(k + 1) = Ax(k) + Bu(k)
y(k) = C x(k)

A control law is selected

u(k) = −K x(k) + N r(k)

as shown in figure 1 so that output can track any step reference command signal r. The closed loop dynamics of this configuration becomes

x(k + 1) = (A − BK )x(k) + BN r(k)

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

Figure 1: State feedback controller with feed forward gain for set point tracking

At steady state, say x = xss, y = C xss = r and u = uss.

Since the states or the output do not change with time in steady state, we can write

xss = Axss + Buss

Let us assume

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

Thus in shifted domain,

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

If we design a stable control Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE) = − Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE) in the shifted domain, it will drive the state variables in shifted domain to 0.

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

The problem of tracking is thus converted into a simple regulator problem.

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

We know that xss = Axss + Buss. Thus

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE).

Now,

C xss = −C (A − BK − I )−1B(uss + K xss) = r

Thus the possible solution for N is uss + K xss = N r where

N−1 = −C (A − BK − I )−1B

and the control input is

u(k) = −K x(k) + N r


Example 1: Consider the following system

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE).

Design a state feedback controller such that the output follows a step input with the desired closed loop poles at 0.5 and 0.6.

Solution: Desired characteristic equation:

z2 − 1.1z + 0.3 = 0

Open loop characteristic equation:

z2 − 1.47z + 0.47 = 0

The controller can be designed using the following expression u(k) = −K x(k) + N r

Since the system is in controllable canonical form, where the controllability matrix is UC = Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE).(non singular), the state feedback gain can be straight away designed as

K = [0.3 − 0.47 − 1.1 + 1.47] = [−0.17 0.37]
N can be designed as
N−1 = −C (A − BK − I )−1B = 0.08
Thus
u(k) = −[−0.17 0.37] x(k) + 12.5 r


1.2 State Feedback with Integral Control 

Calculation of feed forward gain requires exact knowledge of the system parameters. Change in parameters will effect the steady state error.
In this scheme we feedback the states as well as the integral of the output error which will eventually make the actual output follow the desired one.

One way to introduce the integrator is to augment the integral state v with the plant state vector x.

v integrates the difference between the output y(k) and reference r. By using backward rectangular integration, it can be defined as

v(k) = v(k − 1) + y(k) − r

Thus

v(k + 1) = v(k) + y(k + 1) − r
= v(k) + C [Ax(k) + Bu(k)] − r

If we augment the above with the state equation,

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

Since r is constant, if the system is stable, then x(k + 1) = x(k) and v(k + 1) = v(k) in the steady state. So, in steady state,

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

Let us define Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE). This implies

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

where Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

The design problem is now converted to a standard regulation problem. We need to design

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

where K = [Kp Ki], Ki is the integral gain. Now,

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

The steady state terms in the above expression must balance, which implies u(k) = −Kpx(k) − Kiv(k)

At steady state,

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

We can write from the above expression y(k) − r = 0 at steady state. In other words, y(k) follows r at steady state. The block diagram of state feedback with integral control is shown in Figure 2.

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

Figure 2: State feedback controller with integral control for set point tracking


Example: Consider the problem of digital control of the following plant

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

for a sampling period T = 0.1 sec using a state feedback with integral control such that the plant output follows a step input. The closed loop continuous poles of the system must be located at −1 ± j and −5 respectively.
Solution:

Discretization of the plant model gives

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

The discrete state space model of the plant is:

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

Augmenting the plant state vector with the integral state v(k), defined by v(k) = v(k − 1) + y(k) − r

we obtain

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

In terms of the state variables representing deviations from the steady state,

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

where Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)can be designed for the desired pole locations, 0.9 ± 0.1j and 0.6 (in discrete domain) using Ackermann’s formula, as,

Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

Thus
u(k) = −[−0.328 0.416] x(k) − 0.889 v(k)

The document Lecture 33 - Set Point Tracking | 6 Months Preparation for GATE Electrical - Electrical Engineering (EE) is a part of the Electrical Engineering (EE) Course 6 Months Preparation for GATE Electrical.
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FAQs on Lecture 33 - Set Point Tracking - 6 Months Preparation for GATE Electrical - Electrical Engineering (EE)

1. What is set point tracking in control systems?
Ans. Set point tracking in control systems refers to the ability of the system to accurately follow or track a desired set point or reference value. It ensures that the system output closely matches the desired value, even in the presence of disturbances or changes in the system.
2. Why is set point tracking important in control systems?
Ans. Set point tracking is important in control systems because it determines how well the system can achieve and maintain the desired performance. It allows the system to adapt and respond to changes in the environment or inputs, ensuring stable and accurate control of the system output.
3. What are some challenges in achieving accurate set point tracking?
Ans. Achieving accurate set point tracking can be challenging due to various factors such as nonlinearities in the system, time delays, uncertainties, and disturbances. These factors can introduce errors and affect the ability of the system to accurately track the desired set point.
4. How can set point tracking be improved in control systems?
Ans. Set point tracking can be improved in control systems by using advanced control techniques such as model predictive control, adaptive control, or robust control. These techniques take into account the system dynamics and uncertainties to optimize the control performance and enhance the set point tracking capability.
5. What are some applications of set point tracking in real-world systems?
Ans. Set point tracking is applicable in various real-world systems, including temperature control in HVAC systems, speed control in motor drives, position control in robotics, and flow control in chemical processes. Accurate set point tracking is crucial in these applications to maintain desired operating conditions and ensure optimal system performance.
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