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PIDAnalytix.COM
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Since 1911, Tuning of PID control is mired..
AI-PID Tuning will unlock its TRUE power...
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Drive to Plant Wide Optimization with First AI-PID Tuning
Welcome to AI-PID Tuning
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True Optimal PID Tuning
In 1911, the businessman and inventor Elmer Sperry created the PID control (Proportional – Integral – Derivative), which combines these three actions. Sperry designed this controller for the United States Navy. He aimed to automate ship steering and emulate the behavior of a helmsman, who was capable of compensating persistent variances and predict future variations in the high seas. A few years after this creation, the engineer Nicolas Minorsky published the first theoretical analysis of PID control, describing its behavior in a mathematical formula that is used as a basis for calculation until today.
Reinventing PID Tuning for next 100 years...
Automatic control is classified as being Hilbert's 24th unsolved problem. PID control still remains the workhorse of controlling the simplest of process (such as home thermostat) to the most challenging of process (such as flying an aircraft). Tuning PID control loops is over 100 years old problem. PID Tuning is a hard problem. Average age of practicing this craft in the industry is less than 5 years.
PIDAnalytix has reinvented PID tuning to make it optimal consistently and painless across all processes, no matter how widely they vary. The AI-PID Tuning is highly adaptively intelligent (AI) to work consistently no matter how little or accurately the process is known.
AI-PID tuning offers a new paradigm of what to expect from an automatic control system, which is that quality of control to be stable and optimal no matter what...
New Paradigm In PID Control Tuning
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Unison of Stability and Optimality for Perfect Control
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Adaptive Intelligent (AI) Tuning for Known/UnKnown Process
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Based on Universal Control Law (UCL)
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Optimal Control Performance (OCP) for RobustTuning
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Entirely Closed Loop Data Centric, No plant testing
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AI-Algorithmic Globally Optimal
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Huge Process Efficiency Improvement
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Trial & Error Hassel Free
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Seamless Migration from First PID Loop To All Loops
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AI-Tuned Multi PID Loops To Plant Wide Optimization
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We are confident to offer our AI-PID optimal tuning on the terms of pay on performance, every time.
PID Tuning Methods of last 100 years...
Over last 100 years, there have been various methods of PID Tuning evolved in their attempts to improve performance of control. In the above trends, performance of a group of 8 most commonly practiced Tuning methods in the industry is shown. Every one of these PID Tuning methods perform differently, with one distinct common behavior, which is that all of them produce oscillatory change, some are more so than others. The only way to avoid oscillation is to "de-tune" the controller, but that will increase the time to come to target value of "y" ever so slowly, which is also not desirable. Thus, right from its inception, all PID control loops have had this counteracting tradeoff between speed of control and avoidance of oscillations.
Automatic control in general and PID Tuning in particular are classified as being "hard" problems to solve, they have been considered to be in the realm of Hilbert's 24th unsolved problem.
AI-PID Tuning Method of next 100 years...
After 20 years of intensive and singularly focused research on improving PID tuning performance, PID Analytix has made a break through into the fundamental barrier to overcome the above mentioned tradeoff of faster response and avoidance of oscillation in tuning PID controllers which has eluded many practitioners and researchers in the discipline of control engineering.
In the trends comparison, the AI-PID Tuning solution is distinctly straight line to the target value of "y" making a sharp turn without any oscillation (see dotted line in red).
Huge Process Efficiency Improvement 10-30% Energy Saving +..
For many, this kind of straight line with sharp turn response will come as a big surprise with disbelief, never seen before and imagined. This kind of closed loop performance of PID control loop has been validated with a wide range of random variations in the process parameters, namely gain, dead time, first order time constant, second order time constant for stable process and even for unstable process. The fidelity of AI-PID tuning method has been tested widely with 10000+ random cases, a subset of 526 cases of random variations in process parameters are presented herein.
The expected improvement in loop performance can lead to significant and consistent process efficiency in terms saving of energy and improvement in yield of products with much reduced variance in product quality, in the range of 10-20%. In the field of process control improvement, this range of improvement value is considered to not achievable nor experienced. This is entirely due to NOT being able to tune PID loops to the extent AI-PID tuning is able to do.
Easy and Best Part of this Huge Efficiency Benefits is they are low hanging fruits..
The most easy part of realizing this huge efficiency benefits is that all of this can be extracted from current plant operations as is with no step testing required. Just collect the data as the plant is operating in closed loop and upload the data to us and we will download you the results of improvement achievable. Best part of this is that it will not cost you a dime up front to know the improvement in loop performance achievable. If you like it and want to go ahead, contact us.
AI-PID will make plant operations incredibly robust and optimal..
At PIDAnalytix.com, we believe that AI-PID Tuning solutions will soon become one of the biggest segments in the control industry. We’ve only just started, but we already know that every AI-PID Tuning we provide will yield our customer real significant benefits. We have seen the light at the end of the long tunnel of the past methods of PID-Tuning. Therefore we embark on this with daring attitude and high expectations. Continue reading and learn all there is to know about the smart tech behind our successful Startup.
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"Big results require big ambitions"
Heraclitus
aboutus
Life's Learning Experience:
Classical Control, MPC, PID to AI-PID
The quest to conquer the challenge of tuning PID loops was born out of personal experience of trials and tribulations of working in various chemical plants and refineries, namely Imperial Chemical Industries UK, Sun Oil and Exxon-Mobil in USA.
The advanced degrees in Control Engineering (M S and Ph D) and research provided the theoretical understanding of challenges of controlling various types of processes but it did not provide the wisdom of understanding the true power of PID controller. Around 1990, Dynamic Matrix Control (DMC) was hoisted in the industry for plant wide control & optimization.
Model Predictive Control (MPC) was viewed as "best thing after sliced bread", i.e. "best thing after PID control", except that it was supposed to be used as an advanced level control over a bunch of interacting PID loops. It was assumed that PID loops can be "tuned" prior to plant testing etc. Given that tuning PID loops and re-tuning still remains a challenging task, DMC, RMPCT and other clones as MPC are operating on shaky and unreliable foundation even now, over 25 years. Personally practicing MPC over a period of 10 years left an indelible work experience.
Spent a lot of time away from home doing long hours of trial and error MPC and PID tuning and watching the screens while cajoling the operators to try new tuning. After each late evening, last tuning change, came next morning to find that the loop was in MAN; sometimes with a note from the operator explaining what he did. This endless efforts were fruitless and not good for career progression.
When the opportunity arose following the merger of Exxon and Mobil in 2000, the founder took an early retirement from Corporate America to be free. However, this did not result in being free of PID tuning. Instead, it became more of a personal challenge and desire to succeed in devising a better mouse trap, except on my own.