Beyond PID: Exploring Control Strategies in Material Testing Machines

Proportional-Integral-Derivative (PID) control is a cornerstone algorithm in control theory. PID algorithm offers simplicity and reliability across a wide range of applications, including material testing machines. However, certain testing scenarios demand more nuanced control strategies to achieve precise results. This blog explores alternative and additional control strategies and algorithms that complement or enhance traditional PID control, helping engineers optimize performance in material testing.

The Limitations of PID Control

While PID controllers are widely used due to their simplicity and effectiveness, they can fall short in scenarios involving:

Dynamic changes in system parameters.
Nonlinear or asymmetric response requirements.
The need to suppress high-frequency noise while maintaining control accuracy.

Alternative control strategies provide solutions to these limitations, enabling engineers to tackle specific challenges with greater precision and adaptability.

Asymmetric PID Control

Consider a scenario of testing steel wire ropes, synthetic ropes, chains, slings, or shackles. In such high force application PID should provide smooth control both when the machine pulls the rope or chain, and when it is releasing it. In such cases usually different gain parameters are required. Asymmetric PID control applies different gain settings depending on the movement direction, accommodating systems with directional asymmetries.

How It Works:

The PID controller is configured with separate proportional gains for different movement directions, e.g. moving up or down, pulling or pushing the device under test, etc.
This allows the system to handle asymmetrical responses, such as materials that exhibit different stiffness in tension versus compression.

Applications:

Tensile and Compression Testing: Materials like composites or elastomers often behave differently under tension and compression. Asymmetric PID ensures precise control by adapting to these variations.
Nonlinear Material Behavior: For materials with distinct loading and unloading characteristics, asymmetric PID provides tailored control to handle directional differences.

Benefits:

Improved control accuracy in systems with nonlinear or asymmetric behavior.
Greater flexibility for handling complex material responses or test scenarios.

Feedforward Control

Feedforward control enhances system performance by anticipating system behavior and applying corrections proactively, rather than reacting to errors as they occur. This method complements PID control by reducing the error burden on the feedback loop.

Feedforward control has three components:

Velocity Feedforward: Predicts the required control signal to maintain a desired velocity, enhancing tracking precision.
Acceleration Feedforward: Anticipates the forces needed to achieve target acceleration, improving system responsiveness during rapid changes.
Jerk Feedforward: Predicts the rate of change of acceleration (jerk) to smooth out rapid transitions in control signals, reducing vibrations and mechanical stress in the system.

How It Works:

Feedforward uses a model of the system’s dynamics to predict the required control actions based on known inputs, such as desired velocity or acceleration.
Feed forward control outputs usually are calculated using the setpoint value and its derivatives – velocity, acceleration and jerk. These parameters are multiplied by coefficients defined for each of these derivates.
This prediction is applied as a correction signal directly to the control system. So the control signal in this case will be the sum of the PID control’s output and feedforward control output.

Applications:

Dynamic Motion Control: In material testing, feedforward is particularly useful in cyclic loading or dynamic displacement tests, where predictable profiles require precise control.
Acceleration and Velocity Compensation: It compensates for lag during acceleration phases and minimizes overshoot during deceleration phases, ensuring smoother transitions.

Benefits:

Reduced Lag and Overshoot: By applying corrective inputs in advance, feedforward minimizes delays and deviations that are common with PID-only systems.
Improved Stability: Reducing the error handled by the feedback loop decreases the likelihood of oscillations and instability.

Adaptive Amplitude and Mean Control

Adaptive amplitude and mean control is used for high-frequency cyclic waveforms such as a sinusoid. The adaptive amplitude control changes the amplitude and mean (offset) of the set point signal (such as position of force), so that the actual amplitude reaches the desired amplitude. This strategy is particularly effective when test conditions vary between cycles, and manual retuning is impractical. This algorithm can work in conjunction with the PID algorithm.

Adaptive amplitude and mean control is particularly useful in scenarios where tuning a PID algorithm in the desired control mode — such as load control — proves challenging or impractical. In such cases, the system can operate in an alternative control mode, such as position control, while the adaptive control algorithm dynamically adjusts the load’s desired amplitude and offset to meet target values. This approach ensures accurate and consistent results even when direct control is not feasible.

How It Works:

The algorithm monitors the actual amplitude and mean of the measured signal (e.g., force or displacement) and compares it with the desired target values.
Adjustments are made to the amplitude and mean of the set point signal in real-time to minimize discrepancies.

Applications:

High-frequency Testing: Used in dynamic material testing setups requiring precise waveform replication despite system drift or varying conditions.
Fatigue Testing: Adaptive amplitude control ensures consistent force or displacement amplitudes, critical for long-term cyclic tests.

Benefits:

Improved stability in dynamic conditions.
Enhanced accuracy for long-duration cyclic tests.

Choosing the Right Control Strategy

Selecting the appropriate control strategies in material testing machine depends on the test requirements and system characteristics. Key factors to consider include:

The type of test (e.g., fatigue, tensile, or dynamic load tests).
The precision and stability needed for the application.
The dynamic behavior of the material or system under test.

By combining or replacing traditional PID control with these alternative strategies, engineers can achieve higher accuracy, stability, and adaptability in material testing machines.

Conclusion

While PID control remains a reliable standard in control, its limitations in specific scenarios highlight the value of alternative or complementary strategies like adaptive control, feedforward control, and asymmetric PID. These approaches address challenges such as system drift, stiction, and nonlinear material responses, enabling precise and reliable testing.

TACTUN’s software platform integrates these advanced control strategies, offering flexibility of algorithm selection depending on the use cases, and seamless integration with testing workflows. By leveraging TACTUN, engineers can implement these strategies effortlessly, ensuring optimal performance and adaptability in material testing systems.

Share this post: