Rapid design and protoyping of Proportional-Integral-Derivative (PID) controllers
Proportional-integral-derivative (PID) controllers are ubiquitous. Designing and tuning them may appear simple in theory, but it can be difficult and time-consuming in practice. A common method of tuning PID controllers is to manually adjust controller gains while the controller runs the plant. This time-consuming method requires access to the plant hardware and can lead to plant damage if chosen gain values result in unstable plant behavior. In this article we show how a development process can be improved by using Model-Based Design to systematically design, test, and implement PID controllers on programmable logic controllers (PLCs), programmable automation controllers (PACs), and microprocessors.
With Model-Based Design we use a block-diagram environment to create system models of the plant and controller. These models can be simulated, allowing us to quickly iterate and refine controller design before it is implemented and deployed. The need for access to plant hardware is reduced because we can do a lot of testing through simulations instead of involving the plant. Early verification through simulation ensures that the controller performs as expected when deployed to the actual plant.
Model-Based Design of PID controllers involves the following four steps:
- Creating the plant model
- Designing the PID controller
- Testing controller in real-time
- Implementing design
Using a digital-motion control system as an example, we describe how to apply Model-Based Design for rapid design and prototyping of a PID controller.
Digital Motion Control System
Figure 1 shows the plant, or the physical system, that we are trying to control.
Figure 1: Picture of the digital motion control system.
That plant consists of a power amplifier driving a DC motor and two rotary optical encoders for measuring the position of the motor shaft and the load. The motor is connected to the load through a small flexible shaft to approximate the compliance found between the actuator and the load in many motion control systems. The control system ensures that the load follows the specified trajectory by measuring the error between the commanded and measured load angle. It then uses a PID controller to calculate and send a voltage request to the motor. Our design is to improve machine performance from the current maximum speed of 150 rad/sec and acceleration of 2000 rad/sec^2 to new speed and acceleration targets of 250 rad/sec and 5000 rad/sec^2, respectively. We want to achieve these performance gains without losing any position accuracy. Specifically, we want the error between commanded and measured load angle to be less than one degree. The existing controller design does not meet the new performance specifications, as shown in Figure 2.
Figure 2: Old controller design with a maximum position error of more than 4 degrees (bottom) when trying to follow reference trajectory (top, red line – commanded load angle; yellow line-measured load angle) corresponding to new speed and acceleration targets. The performance specification is for the error to be less than 1 degree.
Instead of manually tuning PID gains on the actual plant hardware, we can use Model-Based Design to develop, test, and implement the controller.
Creating the Plant Model
There are two primary approaches for creating a plant model: data-driven modeling and first-principles modeling. With data-driven modeling we create a plant model that fits measured input-output test data. We want to operate the controller during data collection to ensure minimal disruption of plant operation. To collect the input-output data, we add as our input signal a random white noise signal onto the voltage fed to the DC motor. Our output signal is the overall voltage that the controller commands to the DC motor. After we collect this input-output data, we can calculate the frequency response of the closed-loop system. And because we know the exact gains in our current controller design (the one we are trying to improve), we can obtain the frequency response of the plant. Finally, to be able to simulate our controller in the time domain, we use the frequency response of the plant to estimate the plant transfer function using system identification techniques.
PID Controller
Figure 3: Estimated plant frequency response and plant transfer-function model obtained using system identification.
With first-principles modeling we create the underlying dynamic equations of the plant using standard block-diagram modeling by connecting together gain, summation, and integrator blocks. 
Figure 4: Block-diagram model implementing dynamic equations of the plant.
When modeling mechanical and electrical systems it is often more convenient to create a plant model by taking “physical” components, such as inertia, stiffness, damping, resistance, and inductance, and connecting them together, just as we would draw a mechanical diagram or electric circuit of the system. This variation of first-principles modeling technique, called physical modeling, enables us to create plant models without deriving the underlying equations of plant dynamics.
Figure 5: Physical model of the motion-control system.
Often it is beneficial to blend these two distinct modeling approaches (data-driven and first-principles). To do this, we use measured input-output data from the actual system to adjust parameters of the physical model (Figure 5). Parameters (motor and load inertias, stiffness, and damping) are adjusted by using numerical optimization techniques. We play the measured input through the model and compare the model output to the measured output from the actual system. We iteratively adjust model parameters until we get the best possible fit between the output of the calibrated model and measured output. This iterative adjustment is completely automated using optimization techniques.
Designing the PID Controller
PID design and tuning is easy once the plant model is available. We add PID Controller block to the block diagram model of our plant to create a closed-loop system model. PID Controller block is parameterized with proportional, integral, and derivative gains that need to be updated to achieve the desired system performance. Rather than manually tuning these gains, we use an automatic PID tuning method, shown in Figure 6, that automatically calculates PID gains for our plant model. To speed up our controller, we use an interactive slider for a faster response time.
Figure 6: Automatic PID tuning. The PID Tuner automatically calculates PID controller gains and allows interactive fine-tuning with the slider to specify the desired response time.
When we attempt to increase the controller speed, we notice that the system becomes unstable. Further analysis of the system in frequency domain reveals a resonant peak that is causing unstable behavior.
Figure 7: Frequency response (left) and step response (right) of a closed-loop system with PID controller, showing resonant peak.
Such instability is a well-known issue in motion control systems and is easily fixed by using a notch filter to cancel the resonance. We can add the notch filter by using the interactive control design tool that lets us interactively place a notch filter on top of the resonant peak. Doing this allows us to stabilize the system and increase controller speed, as shown in Figure 8.
Figure 8: System response with a notch filter added to the controller.
We now test our design (retuned PID controller and a notch filter) by running the nonlinear simulation of our closed-loop system on our desktop. This nonlinear model includes saturation effects in the motor. Running the simulation with the reference speed corresponding to the new desired system performance confirms that the new design meets the specifications, as shown in Figure 9.
Figure 9: System response for desired reference trajectory. The new controller design has an error of less than 1 degree.
Use of simulation enables us to see the issue with the control system – increasing the PID gains in this case was not sufficient to meet the performance specifications. We need a notch filter to cancel the resonant peak. Automated PID tuning allows us to quickly find stabilizing controller gains and fine-tune the design to meet desired performance targets. If we tried to tune our PID controller on the actual machine, we’d run into an unstable regime of operation that could have damaged our machine.
Testing the Controller in Real-Time
After the controller design has been validated via desktop simulation, the next step is to test the controller in real time. First we need to establish a real-time testing platform, which includes a real-time target computer with IO boards, as shown in Figure 10. These IO boards are typically connected to the actual sensors and actuators on the machine (or machine prototype). We now reuse our controller model and automatically generate C code for the PID controller and the notch filter, and then download this C code to the real-time target computer. We now have our controller code running in real time on the target computer and directly controlling the operation of the plant via inputs and outputs from plant sensors and actuators. Real-time testing lets us confirm that the entire system works properly under realistic conditions with realistic inputs and outputs before we go through the effort of implementing the controller on production hardware, such as PLCs or PACs. This is sometimes known as rapid control prototyping. We are also able to verify that real-time controller performance is still in line with what we observed in desktop simulation. If we find any discrepancies, we’re at an early enough stage in the development process to quickly and easily diagnose and fix the problem in the simulation model and then verify that the controller works properly in real-time.
Figure 10: Real-time testing of the digital-motion control system. The plant hardware sits atop a real-time target computer with IO boards.
Implementing the Design
Our last step is to deploy our controller to a target PLC, PAC, or a microprocessor. We do that by automatically generating IEC 61131 structured text from our controller model. This structured text is generated in PLCopen XML and other file formats that are supported by widely used integrated development environments (IDEs). As a result, we can compile and deploy our controller to numerous PLC and PAC devices. If the final target is a microcontroller, we can instead automatically generate C code.
Conclusions
This paper shows how to develop a controller for a digital motion control system. Plant modeling, closed-loop simulation, and automatic PID tuning allows us to tune and test our PID controller in desktop simulation. Our simulation model gives us the ability to identify a resonance in the system and to develop and test a notch filter to solve this problem. Real-time testing confirms that our control design matches desktop simulation results and meets design specifications. Implementation through automatic code generation means we are able to quickly implement our design on a PLC or microprocessor. Finally, the use of Model-Based Design allows us to quickly develop, test and prototype our design, without damaging plant hardware or disrupting its operation.
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