Nonlinear and Adaptive Control of Intelligent Machines
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".
Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 12272
Special Issue Editor
Interests: nonlinear and optimal control; radio resource management in wireless communication; adaptive optimization methods in nonlinear process; estimation theory
Special Issue Information
Dear Colleagues,
The control of modern intelligent machines presents new challenges due to their complexity, nonlinearity, and uncertainty. Conventional control methods may not be sufficient to address these challenges, and, therefore, new approaches based on soft computing and artificial intelligence have been developed. Soft computing and artificial intelligence techniques have been applied successfully to many areas of machine control, including robotics, automation, process control, and power systems. This Special Issue will cover recent advances and new directions in the theory and application of non-linear and adaptive control for intelligent machines, including robots, unmanned vehicle systems, autonomous vehicles, and more. We are particularly interested in papers that address challenges, such as modeling and control of complex systems, learning-based control, control of uncertain systems, and integration of machine learning techniques with control methods.
We encourage submissions of original research articles, reviews, and short communications that present innovative approaches, novel theoretical developments, and real-world applications in the field of non-linear and adaptive control for intelligent machines, including practical experimental results. We are looking for the state-of-the-art advances in research with the topics related to machine control, including, but not limited to, the following:
- Non-linear and adaptive control algorithms for robotic systems, unmanned aerial vehicles, and other intelligent machines.
- Real-time optimization of machine control systems.
- Machine learning and artificial intelligence techniques for control design.
- Control strategies for complex systems.
- Design and implementation of advanced sensor and actuator systems for machine control.
- Intelligent adaptive learning and control for industrial machines.
- Adaptive observer-based non-linear control techniques.
- Non-linear observer-based design approach.
- Autonomous machine dynamics and control.
Prof. Dr. Kwanho You
Guest Editor
Manuscript Submission Information
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