Optimization plays a pivotal role in controls engineering, fundamentally impacting the design, analysis, and implementation of control systems. Anything from minimizing energy expenditure to maximizing safety outcomes can be grouped under this umbrella. Collimator has a variety of tools to assist in meeting your specific optimization needs.


In controls engineering, optimal tuning helps to enhance the performance and efficiency of systems. By systematically tweaking design parameters to find the best possible settings, engineers can ensure that systems operate at peak efficiency. This could mean faster response times, lower energy consumption, or achieving superior precision, all of which are critical in applications ranging from aerospace to manufacturing.

Auto-tuning is a convenient tool that automates the process of fine-tuning your model parameters. This process involves a systematic search through a predefined parameter space, using a cost reduction algorithm to evaluate the performance of each parameter configuration. The goal is to identify the set of parameters that minimize cost of operation across a variety of situations.

Benefits of auto-tuning with Collimator:

Explore this example of auto-tuning a PID controller to see just how easy it is to get optimal results with Collimator.

Local Optimization

Local optimization is focused on finding the best solution within a specific area of the solution space rather than across the entire possible range. This approach is particularly useful in scenarios where the global optimization might be too time-consuming or computationally expensive, where operating parameters may be constrained to a relatively narrow range, or when a system requires only some additional fine tuning.

Many control systems involve nonlinearities and constraints that make global optimization challenging. Local optimization methods can be more robust in such settings, providing solutions that adhere to operational constraints and handle nonlinear behaviors more effectively. This is especially useful in applications like robotic control, aerospace, and automotive systems, where the operational envelope contains complex interactions that are difficult to model globally.

Benefits of local optimization:

Improving Stability and Robustness

Control systems must be able to cope with fluctuations in their operating environment and internal parameters. Optimization algorithms can ensure that systems maintain stability and perform reliably under a variety of conditions. This is especially important in safety-critical systems such as those used in the automotive or aviation industries, where ensuring robustness against disturbances and uncertainties is paramount.

Stochastic optimization involves intentionally introducing randomness into the system or the optimization process to improve the robustness and performance of control strategies. It can help in exploring the solution space more thoroughly, often leading to more globally optimal solutions. Stochastic perturbation can be used in adaptive control strategies, making the control system more flexible and responsive to changes in system dynamics, and inherently robust to uncertainties.

Benefits of using stochastic optimization methods:

Automatic Differentiation (AD)

Automatic Differentiation is a cornerstone technology in Collimator, enabling the efficient computation of derivatives of functions. This is crucial for all manner of optimization tasks, where gradients are needed to adjust parameters iteratively until an optimal solution is found. AD in Collimator is not only accurate but also significantly faster than traditional numerical differentiation methods.

Impacts of Automatic Differentiation in Collimator: