Compensating for Sensor Noise with Kalman Filters: A Key Service from Spectrum Engineering

In the field of control engineering, precision is paramount. From guiding laser beams in optical systems to stabilizing robotic platforms, accurate sensor data is the backbone of any high-performance control system. However, sensors often face a persistent challenge like noise, particularly at high frequencies. This can degrade performance and limit operational capabilities.

A groundbreaking research paper, “Cap sensor signal correction via Kalman filter v2” (published December 19, 2021) shows how Kalman filters can address this issue in Micro-Electro-Mechanical Systems (MEMS) Fast Steering Mirrors (FSMs). Experts like Spectrum Engineering specializes in delivering custom control solutions and controller implementation that leverage advanced techniques like Kalman filters to tackle sensor noise, ensuring precision and reliability for their clients’ most demanding applications.

The Challenge of Sensor Noise in Control Systems

Control systems depend on sensors to give real-time feedbacks on things like position or speed. In MEMS-based Fast Steering Mirrors (FSMs), which are used in things like optical switches, beam tracking, or image rendering, capacitive sensors track the mirror’s position. But here’s the catch: these sensors often get noisy at high frequencies (above 1 kHz), which messes up their accuracy. On top of that, the electrostatic actuation in these systems—where the force depends on the square of the voltage—adds a layer of complexity because it’s not a straightforward, linear relationship. Together, these issues shrink the control bandwidth, making it tough to get the precision needed for fast-moving, dynamic applications.

The research highlights that the resonance dynamics of FSMs further complicate control, as the system’s natural frequency can amplify disturbances. Traditional open-loop control is impractical due to the nonlinear relationship between voltage and force, which can cause instability. Closed-loop control is essential, but it requires accurate sensor data—something the noisy capacitive sensors struggle to provide at high frequencies.

Kalman Filters: A Powerful Solution

The research proposes a transformative approach: using a parameter-varying Kalman filter to estimate the position and velocity of the MEMS FSM accurately, despite noisy sensor data. A Kalman filter combines a predictive model of the system’s dynamics with noisy sensor measurements, optimally weighing them to produce a refined estimate of the system’s state. In the study, the MEMS device is modeled as a second-order mass spring-damper system with a delay and the Kalman filter uses this model to predict the mirror’s behavior. The noisy capacitive sensor data is then used to correct these predictions, with the filter’s gain tuned to balance model accuracy and measurement reliability.

To account for the nonlinearity introduced by varying input voltages, the research employs a Linear Parameter Varying (LPV) model, adjusting parameters like resonance frequency and stiffness based on the control signal. This parameter-varying Kalman filter was implemented in real-time using a MAT-LAB/Simulink-based controller and tested on a gimbal-less MEMS FSM from MIRRORCLE. The results were striking: the filter significantly improved sensor accuracy at high frequencies, aligning the capacitive sensor’s output with that of an external optical sensor used as a reference. This enabled two key control strategies:

• Damping: Using velocity feedback to calm down resonance, keeping the system steady.
• Stiffening-Damping: Applying a Proportional-Integral (PI) controller to boost both damping, and stiffness. This makes the system respond faster and more accurately.

These improvements widened the FSM’s operational range, making it perfect for high-precision tasks.

Spectrum Engineering’s Expertise in Custom Control Solutions

Spectrum Engineering brings over 25 years of experience in servo control systems to deliver custom control solutions tailored to their clients’ unique challenges. The research on MEMS FSMs exemplifies the kind of problems they excel at solving. Their expertise in controller implementation includes:

• Kalman Filter Implementation: They design and deploy Kalman filters to compensate for sensor noise, ensuring accurate state estimation in noisy environments, as demonstrated in the research.
• Controller Optimization: They fine-tune controllers to meet specific performance goals, such as damping resonance or increasing bandwidth, aligning with the research’s outcomes.
• Automatic Parameter Tuning: Their automated tuning processes optimize controller parameters, mirroring the research’s approach to tuning the Kalman filter’s noise covariances.
• Custom Algorithm Development: They create innovative algorithms to address constraints like nonlinearity, friction, backlash and saturation, which are critical in systems like MEMS FSMs.
• Support for System Design: They assist with mechanical and electronic design to optimize control performance, ensuring seamless integration of control solutions.

Spectrum Engineering’s ability to implement Kalman filters, and other advanced control techniques makes them a trusted partner for industries requiring precision, such as aerospace, telecommunications and medical imaging.

Controller Implementation: Bringing Research to Reality

The research’s real-time implementation of the Kalman filter, using a MAT-LAB/Simulink-based controller, highlights the importance of robust controller implementation. Spectrum Engineering excels at translating theoretical solutions into practical, real-world applications. Their process involves:

• Modeling and Simulation: Spectrum Engineering develops accurate models of the system, similar to the second-order model used in the research, to predict behavior and design effective controllers.
• Real-Time Integration: They implement controllers on embedded systems, ensuring they operate reliably in real-time environments.
• Testing and Validation: They rigorously test controllers, as seen in the research’s frequency response comparisons, to verify performance against reference standards.
• Adaptability: Their solutions adapt to varying conditions, such as the input-dependent parameters in the research, using techniques like interpolation and LPV modeling.

This approach ensures that their clients’ control systems perform optimally, even in challenging conditions.

Bottom Line

Struggling with sensor noise or complex control challenges in your optical systems, robotics, or precision-driven projects?  Spectrum Engineering is here for you. Partnering with Spectrum Engineering gives you access to top-notch control engineering expertise and guess the best part? Skip hassle and cost of building your own in-house team. Whether you’re working on optical systems, robotics, or other precision-driven projects, they can help you achieve superior performance. Reach out to Spectrum Engineering to see how they can take your control systems to the next level!