What are the most common tuning methods for PID controllers?

1 Manual tuning

Manual tuning involves adjusting the PID parameters by trial and error, based on the observation of the system behavior and the feedback error. Manual tuning is simple and intuitive, but it can be time-consuming and imprecise, especially for complex or nonlinear systems. Manual tuning also requires a good understanding of the effects of each parameter on the system dynamics and stability.

2 Ziegler-Nichols tuning

Ziegler-Nichols tuning is a classical method that uses two empirical rules to determine the PID parameters, based on the frequency response of the system. The first rule applies to systems with a steady-state error, and uses the ultimate gain and ultimate period of the system to calculate the PID parameters. The second rule applies to systems without a steady-state error, and uses the critical gain and critical damping of the system to calculate the PID parameters. Ziegler-Nichols tuning is easy to implement and widely applicable, but it can result in aggressive or oscillatory responses, and it does not account for the effects of noise or disturbances.

3 Cohen-Coon tuning

Cohen-Coon tuning is another classical method that uses two empirical rules to determine the PID parameters, based on the step response of the system. The first rule applies to systems with a first-order lag and a dead time, and uses the process gain, time constant, and dead time of the system to calculate the PID parameters. The second rule applies to systems with a second-order lag and a dead time, and uses the same parameters plus the damping ratio of the system to calculate the PID parameters. Cohen-Coon tuning is more accurate than Ziegler-Nichols tuning for systems with a large dead time, but it can also result in aggressive or oscillatory responses, and it does not account for the effects of noise or disturbances.

4 Relay feedback tuning

Relay feedback tuning is a modern method that uses a relay (a switch that alternates between two values) to excite the system and measure its frequency response. The relay feedback tuning method can estimate the ultimate gain and ultimate period of the system, as well as the process gain, time constant, and dead time of the system, and use them to calculate the PID parameters. Relay feedback tuning is robust and adaptive, and it can handle nonlinearities and uncertainties in the system. However, it requires a stable system and a sufficiently large relay amplitude, and it can cause large variations in the system output.

5 Optimization-based tuning

Optimization-based tuning is a general method that uses an optimization algorithm (such as genetic algorithm, particle swarm optimization, or simulated annealing) to search for the optimal PID parameters that minimize a cost function. The cost function can be defined based on different criteria, such as the error, the overshoot, the settling time, or the robustness of the system. Optimization-based tuning is flexible and powerful, and it can handle complex or nonlinear systems and multiple objectives. However, it requires a mathematical model of the system and a suitable optimization algorithm, and it can be computationally expensive and sensitive to initial conditions and parameters.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?