Intelligent Control Systems

Day 45/365 23 years ago while in medical school, and passionate about decoding the mind and brain, my curiosity led me to study neuroscience and later control engineering and computer science and I was fortunate to become a student of the Late Prof. Caro Lucas. He was one of the gurus of control engineering globally, a student of Prof. Zadeh, the father of fuzzy systems. After a couple of semesters, Caro asked me who are you, and I told him my story we became friends and eventually started working on intelligent control systems, specifically bio-inspired control systems, combining neuroscience and AI. With our unique approach and inspired by the emotional learning process in the mammalian brain, we invented the first controllers that made decisions based on emotions and named it BELBIC (stands for Brain Emotional Learning Based Intelligent Controller). Based on the computational model inspired by the neural structure and function of the amygdala and orbitofrontal cortex in the human brain, BELBIC is designed to process sensory inputs and emotional signals to generate appropriate control outputs. BELBIC has shown improved performance compared to traditional control methods, especially in handling uncertainties and disturbances. It's also way faster and computationally more efficient and is particularly effective in dealing with complex, non-linear systems and environments with high levels of uncertainty or disturbance. BELBIC can adapt to changing conditions and learn from past experiences, making it more flexible than many traditional control systems. It can process multiple inputs simultaneously, including both sensory and emotional signals, leading to more sophisticated decision-making. For the last 20 years, BELBIC has been applied in various fields, including robotics, industrial control systems, and autonomous vehicles. Here are some major categories among more than 400 applications: Industrial Control Systems Robotics Automotive Industry Power Systems Aerospace Consumer Electronics Medical Devices Financial Systems Environmental Control Transportation BELBIC's development and widespread application illustrate the rapid progress and far-reaching impact of AI technologies. As an early example of integrating emotional learning into control systems, BELBIC represents a stepping stone towards more sophisticated AI that can process and respond to complex, multifaceted inputs. This trajectory points toward future bio-inspired AI systems with increasingly human-like decision-making capabilities, potentially leading to advancements in areas such as natural language processing, adaptive learning systems, and even components of artificial general intelligence (AGI) and something more powerful, Artificial general emotional intelligence (AGEI). To be continued on day 46/365

I'm very pleased to share with you my project to modeling and control the Li-ion battery charging using Adaptive Neuro Fuzzy Inference Controller (ANFIS) developed for DC-DC charger. By incorporating inputs and desired output data pairs in the ANFIS toolbox of MATLAB software, a successful prediction model can be constructed with the lowest possible error margin, this technique is adapted for the control of Battery charging profile as an intelligent control technique. Approximate knowledge reasoning and uncertainties could be modelled by fuzzy logic, but it lacks learning rules whereas the neural network has learning capabilities which strengthen the adaptive learning rules. On the other hand, the neural network lacks representation of knowledge as compared to FL. Neuro Fuzzy systems (NFS) combines the main features of both NN and FL, ANFIS is the combination of the two soft-computing methods: ANN and FIS. ANFIS uses the NN learning algorithm to generate a Tagaki-Sugeno type Fuzzy Inference System that approaches a nonlinear system with a variety of linear systems. Fuzzy rules and Membership functions (MFs) are obtained by training the system using experimental data sets. In order to determine the parameters of the adaptive system, back propagation or hybrid learning methods are used in the learning process, however MATLAB / Simulink with its comprehensive and powerful control library. This combination makes ANFIS a robust and effective technique. At the same time, the adaptive capability of ANFIS is increased by a trial-and-error process where expert knowledge is not mandatory. In a fuzzy system, rules are generated with human knowledge and manually, where ANFIS generates sufficient rules with the reference of input and output data considering the benefits of ANN. However, ANFIS chooses the finest combination between these criteria to get the maximum output with a minimum error during training operation, It is generally used for complex and nonlinear systems in various fields. Used ANFIS approach to control with large uncertainties and highly nonlinearity, designed an ANFIS based energy management system. ANFIS is utilized as a modern controller by researchers because of its enormous advantages, and it has demonstrated its ingenious performance in different sectors. ANFIS controller outperforms the traditional controller with respect to time efficiency and optimization of membership functions (MFs). The use of ANFIS is broader in modern control systems. However, ANFIS makes the system easier in terms of parameter choice and MF optimization and is time efficient as ANFIS utilizes training data rather than human expert knowledge. Literature shows that ANFIS controllers have been employed to control systems with battery-integrated renewable energy resources better. One study showed that effective battery power management is possible with an ANFIS controller.

W𝐡a𝐭 𝐢f y𝐨u c𝐨u𝐥d c𝐨n𝐭r𝐨l i𝐧d𝐮s𝐭r𝐢a𝐥 𝐩r𝐨c𝐞s𝐬e𝐬 𝐰i𝐭h j𝐮s𝐭 𝐚 𝐰a𝐯e o𝐟 𝐲o𝐮r h𝐚n𝐝? 🤔 I recently worked on a project that blends 𝐏𝐲𝐭𝐡𝐨𝐧, 𝐎𝐩𝐞𝐧𝐂𝐕, 𝐚𝐧𝐝 𝐅𝐚𝐜𝐭𝐨𝐫𝐲 𝐈/𝐎 to create an AR-like system where I control the water flow rate of a tank using 𝐟𝐢𝐧𝐠𝐞𝐫 𝐦𝐨𝐭𝐢𝐨𝐧. 👆💧 Here’s how it works: ✅ 𝐎𝐩𝐞𝐧𝐂𝐕 tracks my finger movement. ✅ 𝐏𝐲𝐭𝐡𝐨𝐧 processes the data and sends control signals. ✅ 𝐅𝐚𝐜𝐭𝐨𝐫𝐲 𝐈/𝐎 simulates the industrial environment, making it feel like a real-world application. As an 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫, I’m always exploring ways to make automation more intuitive and human-centric. This project was a fun way to experiment with how we can bridge the gap between humans and machines. But here’s my question to you: ❓ 𝐖𝐡𝐚𝐭 𝐨𝐭𝐡𝐞𝐫 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐯𝐞 𝐰𝐚𝐲𝐬 𝐜𝐚𝐧 𝐰𝐞 𝐮𝐬𝐞 𝐡𝐮𝐦𝐚𝐧 𝐠𝐞𝐬𝐭𝐮𝐫𝐞𝐬 𝐨𝐫 𝐀𝐑 𝐭𝐨 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬?  ❓ 𝐇𝐚𝐯𝐞 𝐲𝐨𝐮 𝐰𝐨𝐫𝐤𝐞𝐝 𝐨𝐧 𝐬𝐢𝐦𝐢𝐥𝐚𝐫 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐨𝐫 𝐬𝐞𝐞𝐧 𝐭𝐡𝐢𝐬 𝐤𝐢𝐧𝐝 𝐨𝐟 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐢𝐧 𝐚𝐜𝐭𝐢𝐨𝐧? I’d love to hear your thoughts, ideas, or experiences! Let’s brainstorm how we can push the boundaries of automation together. 💡 #IndustrialAutomation #Python #OpenCV #FactoryIO #Innovation #Engineering #Automation #Industry40 #TechForGood #AR #HumanMachineInterface #IoT #ControlSystems #EngineeringLife #TechInnovation Siemens|Automators Industrial Projects Pvt Ltd|ABB|Mitsubishi Electric-FA-INDIA|Schneider Electric|Rockwell Automation|Rajvir Singh ✅|Jindal Steel & Power Ltd.|Vedanta Group|RealPars|Industrial Automation™|Emerson's Automation Technologies & Solutions|Fox Solutions Pvt.Ltd |COTMAC Let’s start a conversation! Drop a comment below or share your thoughts. 🚀 --- *P.S. If you’re curious about the code or simulation, let me know – I’d be happy to share more details! * 😊

INTELLIGENT FLIGHT CONTROL SYSTEMS ARTIFICIAL INTELLIGENCE is not magic; it is computer programming that is organized to emulate the logic of the human brain. Intelligent control functions fall into three categories: . DECLARATIVE actions involve DECISION MAKING, providing models for system monitoring, goal planning, and system/scenario identification. They are the OUTER LOOP of control functions, organizing commands to functional sub-systems. They emulate CONSCIOUS THOUGHT. . PROCEDURAL actions concern SKILLED BEHAVIOR and have parallels in guidance, navigation, and adaptation. They emulate PRECONSCIOUS THOUGHT.   . REFLEXIVE actions are SPONTANEOUS. They provide INNER-LOOP CONTROL AND ESTIMATION. They emulate SUBCONSCIOUS THOUGHT. Intelligent control systems may contain a HIERARCHY of conventional expert systems, procedural algorithms such as LQGR, and computational neural networks or gain scheduling. Current convention may be to implement large portions of control code using deep networks and LARGE LANGUAGE MODELING (LLMs). Neural networks may be continuously adapted through MACHINE LEARNING (ML); they do not predict but may extrapolate. They implement MULTIDIMENSIONAL SURFACE FITTING that may be adapted through ML. Intelligent flight control design FOR CREWED AIRCRAFT draws on two apparently unrelated bodies of knowledge. The FIRST is rooted in classical analyses of aircraft stability, control, and flying qualities. The SECOND derives from human psychology and physiology. The goal is to find control structures that are consistent with control objectives, that are compatible with human thought and physical function, and that bring controlled systems to a higher level of overall capability. UNCREWED AIRCRAFT (UAVs) may rely on human operators for declarative decisions, as in FIRST PERSON VIEW (FPV) DRONES or COLLABORATIVE COMBAT AIRCRAFT (CCA). Control logic is thus semi-autonomous. Fully autonomous UAVs would make their own declarative decisions and should be approached with caution for both transport and combat aircraft. While the distinction may be described as algorithmic vs. non-algorithmic, ALL of the logic is executed by computers and is, therefore, ALGORITHMIC. A better distinction is SUPERVISED vs. UNSUPERVISED LEARNING. References . 'Toward Intelligent Flight Control,' https://lnkd.in/eYCFbyz5. 'Flight Dynamics, Second Edition,' https://lnkd.in/ePctugN8. . Piloting Actions and Aircraft Flying Qualities, Section 4.7, . Gain-Scheduled and Neural Network Control, Section 8.6, . Adaptive and Failure-Tolerant Control, Section 8.8.