Carlos L. Castillo, PhD.



My research interests are in Machine Learning, Robotics, Control Systems, Estimation theory, and Fault-tolerant Systems . I am currently working in the implementation of Real-Time Advanced Control Systems and Observers/Estimators.

Machine Learning

Next, some definitions of Machine Learning are presented:

  • "Machine learning is the systematic study of algorithms and systems that improved their knowledge or performance with experience."[5]
  • "Machine Learning is programming computer to optimize a performance criterion using example data or past experience. We have a model defined up to some parameters, and learning is the execution of a computer program to optimize the parameters of the model using training data or past experience" [6]


Robotics is the branch of technology dealing with the design, construction and use of robots[7]. The evolution of robots, from the industrial arms , like the UNIMATE in the 1960s to the Robonaut 2 developed by NASA and General Motors, has been amazing. There has been an nonstop progress in the mechanical structures, actuators, processing power of their controller and the advanced algorithms used to control them. The last two decades has seen an increasing use of Artificial Intelligence/Machine Learning algorithms to make more versatile and capable robots. The use of robots and Artificial Intelligence will be fundamental for the success of the called Factory of the Future or Industry 4.0

Control Systems

Since their inception, control systems have been an enabling technology. Control systems were introduced during the industrial revolution with devices like the James Watt flyball governor,. Over the past 40 years, the developments in analog and digital electronics have resulted in dramatic increases in the computational power of microcomputers and microcontrollers. These developments provided for the implementation of advanced control techniques. These advanced control techniques enabled the successful development of high performance applications such as:

  • Control systems in the manufacturing industries from automotive to integrated circuits, which are associated with computer-controlled machines, provide the precise positioning and assembly required for high-quality, high-yield fabrication of components and products, [1].
  • Industrial process control systems, particularly in the hydrocarbon and chemical processing industries, maintain high product quality. Product quality is maintained by monitoring thousands of sensors signals and making corresponding adjustments to hundred of valves, heaters, pumps and other actuators, [1].
  • Control of communication systems such as the telephone system, cell phones, and the Internet are especially pervasive. These control systems regulate the signal power levels in transmitters and repeaters, manage packet buffers in network routing equipment and provide adaptive noise cancelation to respond to varying transmission line characteristic, [1].

Control systems have reached a high level of theoretical development and there exists a myriad of applications. However, the development of new sensors and actuators for old and new applications continues. Therefore, the demand for new theoretical concepts and approaches, to handle increasingly complex applications remains high.

Estimation theory

The need to extract or estimate useful information, from noisy signals or from partial information sources, is almost pervasive in most of the real-world signal processing and control systems. Estimating the values of signals or parameters is a fundamental part of many signal-processing systems. In the particular case of control systems, the requirement is pervasive to use an algorithm to obtain measured outputs and the estimated values of the state variables of the process from noise. The Kalman filters are the most commonly used algorithm for the purposes of extracting information from noise.

Fault-tolerant Systems

Stringent requirements for safety, reliability and profitability are demanded for the chemical and manufacturing industries. These requirements have generated the necessity of designing control systems with the ability of handling defects/malfunctions in process equipment, communication networks, sensors and actuators, [4]. Issues related to faults may include physical damage to the process equipment, misuse of raw material and energy resources, increase in the downtime for process operation resulting in significant production losses and jeopardizing personnel and environmental safety [2]. Management of abnormal situations is a challenge in the chemical industry since abnormal situations account annually for 10 billion in lost revenue in the U.S. alone, [3]. Aside from the economical implications, which failures in technological systems imply, the loss of life is also a fundamental reason for designing control systems capable of handling systems’ components faults or failures. Reliability and operational safety is one of the main research focus areas in the design of current and future control systems of UAVs.

  1. R. M. Murray, K. J. Åström, S. P. Boyd, R. W. Brockett and G. Stein, “Future Directions in Control in an Information-Rich World”, IEEE Control Systems Magazine, pp. 20-33, 2003
  2. C. W. McFall, “Integrated Fault Detection and Isolation and Fault-Tolerant Control of Nonlinear Process Systems”, Ph.D. thesis, Chemical Engineering Department, University of California Los Angeles, 2008
  3. A. Gani, “Fault-Tolerant Process Control: Handling Actuator and Sensor Malfunctions”, Ph.D. thesis, Chemical Engineering Department, University of California Los Angeles, 2007
  4. “Aerovironment, Inc.”,
  5. Peter Flach, "Machine Learning: The Art and Science of Algorithms that Make Sense of Data", Cambridge University Press, 2012
  6. Ethem Alpaydin, "Introduction to Machine Learning", 3rd edition, MIT Press, 2014
  7. Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, and Giuseppe Oriolo, "Robotocis - Modeling, Planning and Control, Springer, 2009