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This research presented a holistic approach in determining the trade-off optimized Proportional-Integral-Derivative (PID) tunings for both servo and regulatory controls of the cascade control loop by using Genetic Algorithm (GA). Performance of GA-based PID tunings was significantly compared with the IMC-based single loop tunings and conventional cascade control tunings. GA-based PID tunings eliminated the complicated mathematic calculations in obtaining the correlation PID tuning values and also reduce the dependency on engineering knowledge, experience, and skills. The performance of transient and steady-state responses was compared through time domain specification, performance index, and process response curve. It is concluded that the GA-based PID tunings for the cascade control loop had produced the best result for both servo and regulatory control objectives, which is eventually determined.

A decentralized PID control scheme is widely used in the process industry owing to its simplicity. Due to process interactions in a given multivariable process such as the extractive co-fermentation of bioethanol, the tuning of decentralized PID controllers can be challenging. This paper presents a new decentralized PI controller tuning that considers the process interactions and loop stability. The effectiveness of this new tuning approach is demonstrated using the extractive co-fermentation of the bioethanol process.

Controlling the nonlinear process is a very challenging task in the process plant, whereby it is highly dependent on the knowledge and skills of the practitioners. This paper aimed at developing a Genetic Algorithm (GA) based adaptive optimization algorithm to obtain the trade-off controller tunings for the satisfactory performance of both servo and regulatory control objectives at the Low, Medium and High operating levels. The model identification and computational analysis is integrated into a Graphical User Interface (GUI) platform of MATLAB, which allows automated generation of the controller tunings for the tested models. The research is begun with empirical model identification of both process and disturbance models at the Low, Medium and High operating levels. Controller tunings are then analysed by GA algorithms and displayed on the GUI boards. The obtained controller tunings are applied to the Gravity Drained Tank function of LOOP-PRO software and its process responses and performance indexes are compared with manually calculated controller tunings. The results show that GA-based optimization analysis gives better control performance than the manually calculated controller tunings.

This research developed a low cost smart control system that enables virtual monitoring and control of a closed-loop system using a NodeMCU microcontroller linked to the cloud or internet network. The designed system allows adjustments for Proportional-Integral (PI) controller and setpoint settings of the real-time operating system to be performed anywhere and anytime via Blynk IoT platform. The analysis is demonstrated using experimental test to a light control system, where the PI controller controls light luminosity emitted by Light Emitting Diode (LED). The designed Graphical User Interface (GUI) is capable of notifying system failure by sending an alarm notification message to the electronic device connected to the physical system virtually, so the biased or unstable process parameters could be immediately mitigated when it occurs. Empirical model identification is conducted via MATLAB simulation, followed by the calculation of controller tunings. MATLAB analysis further analyses gain values, that can affect the adjustment of proportional and integral gains for the stability and good control. Data downloading can be performed either via the Integrated Development Environment (IDE) software or the Superchart in the Blynk. The experimental analysis showed that the light control system has perform well while the Blynk is connected virtually to the light control system.

Boiler is an important utility system to support operations in the industry. The control of water level in the steam drum is a complicated task due to the non-minimum phase (NMP), which possibly will cause instability to the controlled water level in the steam drum. Process identification and controller design are difficult tasks for the steam drum because of non-minimum phase. Following the previous literature, this paper proposed process identification to 3rd order transfer function and optimization of Proportional-Integral-Derivative (PID) tunings of the water level by using Particle Swarm Optimization (PSO). A Graphical User Interface (GUI) has been developed to provide a direct platform to deal with these tasks. The result of PSO is compared with other tuning methods in terms of performance indicator and index. An analysis of the performance curve in 3-dimension graphs is also presented to visualize the output performance of various proportional and integral gain settings. The study has concluded that PSO provided better PI tunings for the best control of the Heat Exchanger function in the LOOP-PRO software.

Integrating process is applied to many industries however there are very few research done on it. Determining PID settings for the closed-loop control of integrating process is a challenging task due to its inherent characteristic, which is only stable at one equilibrium operating point. This paper highlighted First Order plus Dead Time in representing process and disturbance model. Improvement of relative performance for transient and steady state response is achieved by using feedforward plus feedback control scheme. Moreover, computational optimization analysis was presented for developing a systematic way to design PID controller for the optimal performance of both servo and regulatory control problems. Performance of the controlled process were then compared in term of graphs, performance index and performance indicator. It is proven and concluded that designed PID controller settings by using computational optimization analysis eventually gives the best performance compared to other tuning methods for a Pumped-tank function of LOOP-PRO simulation software.

Computational optimization via artificial intelligence has been considered as one of the key tools to gain competitiveness in Industrial Revolution 4.0. This paper proposes computational optimization analysis for designing the widely used industrial control systems - feedforward and feedback control schemes. Although several different optimal tunings for servo and regulatory control problems exist, their applications often present some challenges to plant operators. Plant operators often face difficulties to obtain satisfactory PID controller settings by using the conventional tuning methods, which rely heavily on engineering experience and skills. In the proposed intelligent tuning method for the feedforward plus feedback control system, the closed-loop stability region was first established, which then shall provide the upper and lower limits for computational optimization analysis via Genetic Algorithm. Based on a jacketed reactor case study, the performance of feedforward plus feedback control scheme tuned via Genetic Algorithm was compared to that tuned via Ziegler-Nichols tuning. Comparison of performances showed that computational optimization method via Genetic Algorithm gave improved performances in terms of servo and regulatory control objectives.

Single loop feedback control is commonly used in process control. The main drawback of single loop feedback control is its less effectiveness in rejecting the external disturbances. In order to improve speed of disturbance rejection and stability of closed-loop system, cascade control was studied and analyzed. To design the cascade control, first-order plus deadtime (FOPDT) models of both inner and outer loop were developed and applied for both sequential and simultaneous tuning methods. For sequential tuning, an IMC-based tuning was used whereas for simultaneous tuning method, Multiscale and Enhanced Cascade Control tunings were chosen. Relative performance of various controller settings for single and cascade control were compared. Moreover, recommendation for optimized tuning used “Step Response Checker” from System Design Toolbox was also elaborated and tested. Performance results were evaluated through Minimum Integral Error measurement. The effectiveness of the tuning methods was compared and evaluated using a lab-scale air flow rig.

A computational analysis using Artificial Intelligence techniques has drawn its attention in Industrial Revolution 4.0. There are self-regulating and integrating processes involved. The commonly used control method involves single-loop feedback control. Embedding feedforward algorithm is aimed to improve the steady state response of the controlled process without affecting the performance of transient response. Apart of it, two optimal tunings are respectively applied to suit to the different control objectives. Correlation PID tunings are applied to respective objective somehow is very relying to the engineering experience and skills. This paper proposed using Genetic Algorithm for optimization analysis to search a trade-off optimized PI controller settings, which reduces dependency on skills as well as provide better insights to all practical engineers for commending effective PI tunings to the real control practices of plants. Initially, the respective process and disturbance models of LOOP-PRO software for both Jacketed-reactor and Pumped-tank were developed. Then, the simulation analysis of PI tunings was conducted to feedback control, feedforward and feedback and Genetic Algorithm scheme. Relative performance was compared in terms of graphs, performance index, and performance indicator. It is concluded that Genetic Algorithm has consistently provided a trade-off optimized PI controller settings for both Jacketed-reactor and Pumped-tank.