Robotic arm operating with precision, symbolizing advanced control systems

Smarter Tech, Safer Systems: How Estimation Techniques Are Revolutionizing Control Engineering

Lena Kashyap in Tech & Innovation November 2025 • 4 min read.

"Explore how advanced estimation techniques, particularly sliding mode approaches, are enhancing the robustness and reliability of control systems in the face of uncertainties and disturbances."

In an era where automation and precision control are paramount, the ability to manage uncertainties—both predictable and unpredictable—is critical. Control and estimation systems are constantly challenged by manufacturing tolerances, unpredictable disturbances, and the inherent noise in sensor measurements. These factors complicate the task of maintaining stable and robust system performance, especially when key parameters are inexact or system states are difficult to measure accurately.

Traditional control methods often fall short in the face of these complexities. However, a new generation of techniques is emerging to address these challenges head-on. Sliding mode techniques, known for their robustness, are at the forefront, offering innovative solutions for handling uncertainties and estimating both unmeasurable states and unknown parameters. These advancements promise not only to enhance the reliability of existing systems but also to open doors to new applications previously deemed too challenging.

This article explores how these advanced estimation techniques are being validated and implemented across various industries. We'll delve into the principles behind sliding mode control, examine real-world applications, and discuss the future of control systems in an increasingly uncertain world. Whether you're an engineer, a tech enthusiast, or simply curious about the next wave of technological innovation, this is a must-read.

The Power of Sliding Mode Techniques

Robotic arm operating with precision, symbolizing advanced control systems

Sliding mode control operates on a simple yet powerful principle: forcing a dynamic system to adhere to a predefined stable operation mode—referred to as the 'sliding surface.' Imagine a train that, regardless of external disturbances, is magnetically pulled back to its tracks; that’s the essence of sliding mode control. The beauty of this approach lies in its ability to divide a complex system into manageable parts—a linear component and a nonlinear component that may include unknown disturbances.

The primary task then becomes compensating for the nonlinear part. This is achieved by incorporating a switching term into either the control law for trajectory tracking or the observer part for estimating non-measurable system states. The advantage? Finite-time convergence, meaning the system quickly reaches and maintains the desired sliding surface. In practical terms, this translates to more reliable and stable control, crucial for technical applications where precision is key.

Here are some key advantages of sliding mode techniques:

Robustness: Maintains stability despite uncertainties.
Finite-Time Convergence: Quickly achieves the desired state.
Adaptability: Can handle nonlinear systems effectively.
Compensation: Mitigates the impact of disturbances.

Consider a robotic arm in a manufacturing plant. The arm needs to perform precise movements, but it’s subject to various disturbances like friction, wear, and external forces. Using sliding mode control, the arm can maintain its trajectory with high accuracy, compensating for these disturbances in real-time. This ensures consistent performance, reduces errors, and extends the lifespan of the equipment.

Looking Ahead: The Future of Control Systems

The integration of interval sliding mode observers (ISMO) represents a significant leap forward in control engineering. These techniques provide a robust framework for managing uncertainties and disturbances, paving the way for more reliable and efficient systems. As research continues, the application of ISMO and related methods is expected to expand across various industries, from aerospace and automotive to robotics and manufacturing. In a world that demands precision and resilience, these advancements are not just incremental improvements—they are game-changers.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1007/978-3-319-23413-7_91, Alternate LINK

Title: Experimental Validation Of State And Parameter Estimation Using Sliding-Mode-Techniques With Bounded And Stochastic Disturbances

Journal: Mathematics in Industry

Publisher: Springer International Publishing

Authors: Luise Senkel, Andreas Rauh, Harald Aschemann

Published: 2016-01-01

Everything You Need To Know

How do advanced estimation techniques improve the robustness and reliability of control systems?

Estimation techniques, especially sliding mode techniques, enhance the robustness and reliability of control systems by effectively managing uncertainties and disturbances. These methods ensure stability and precision in dynamic applications, overcoming challenges posed by manufacturing tolerances, unpredictable disturbances, and sensor noise. By compensating for nonlinear components and estimating unmeasurable states, these techniques provide more reliable and stable control in technical applications.

Can you explain the core principle behind how sliding mode control operates?

Sliding mode control forces a dynamic system to adhere to a predefined stable operation mode, known as the 'sliding surface'. This is achieved by dividing a complex system into linear and nonlinear components, with the primary task being to compensate for the nonlinear part, which includes unknown disturbances. A switching term is incorporated into the control law or the observer part to achieve finite-time convergence, allowing the system to quickly reach and maintain the desired sliding surface.

What are the key advantages of using sliding mode techniques in control systems?

Sliding mode techniques offer robustness by maintaining stability despite uncertainties, finite-time convergence for quickly achieving the desired state, adaptability to handle nonlinear systems effectively, and compensation to mitigate the impact of disturbances. This makes them particularly valuable in applications like robotics, where precise movements are required despite disturbances such as friction, wear, and external forces.

What are interval sliding mode observers (ISMO) and what impact do they have on the future of control systems?

Interval sliding mode observers (ISMO) represent a significant advancement in control engineering by providing a robust framework for managing uncertainties and disturbances. ISMO are expected to expand across various industries, including aerospace, automotive, robotics, and manufacturing. While the text specifically highlights ISMO, other advanced estimation techniques like Kalman filters and particle filters also contribute to improving control system performance in uncertain environments. Further research in these areas promises to enhance the reliability and efficiency of systems requiring precise control.

What are the broader implications of utilizing sliding mode techniques and interval sliding mode observers (ISMO) in real-world applications?

The implications of using sliding mode techniques and interval sliding mode observers (ISMO) extend to improving the reliability, stability, and performance of various systems. This leads to reduced errors, extended equipment lifespan, and the ability to tackle previously challenging applications. For example, in autonomous vehicles, these techniques can ensure precise navigation and control despite unpredictable road conditions and sensor noise. Additionally, the ability to handle nonlinear systems effectively opens doors to more complex and advanced control strategies.