PI Controller Tuning Research Articles

A novel optimally tuned super twisting sliding mode controller for active and reactive power control in grid‐interfaced photovoltaic system

AbstractIn photovoltaic (PV) systems, inverters play a crucial role for supplying electricity to meet the demand while maintaining power quality. For a local load connected to a grid‐interfaced photovoltaic (GIPV) system, active and reactive power control is necessary at the distribution level. Thus, the foremost purpose of this article is to get the best optimally designed robust controller for control of active and reactive power. A GIPV system with Improved Arithmetic Optimisation Algorithm (IAOA)‐based Super Twisting Sliding Mode Controller (ST‐SMC) methodology has been proposed in this article for active and reactive power management. The conventional PI controller in the GIPV system that is most frequently used has considerable undershoot and a long settling period. PI controller tuning parameters were also changed to account for the wide change in the reference pattern. Therefore, STSMC and SMC are used for ensuring robustness against external disturbances. The conventional SMC comes out to have a chattering issue. Furthermore, the proposed IAOA technique is validated through some benchmark functions. The proposed IAOA technique outperforms Particle Swarm Optimisation (PSO), Forensic Based Investigation (FBI), and Traditional Arithmetic Optimisation Algorithm (TAOA) in terms of the number of iterations and accurately achieving optimal solutions for active and reactive power control. The results show that the proposed IAOA‐based STSMC technique has an improved performance of settling time and undershoot for active and reactive power control. This article also presents stability analysis and robustness test of the above mentioned controllers to illustrate the effectiveness of each optimally designed controller. A 40 kW GIPV system performance is evaluated using the MATLAB environment, and the results are validated in a real‐time simulator platform OPAL‐RT 4510.

Real-Time Implementation and Control of Multi-Source Electric Vehicle Traction Motor under Various Drive Conditions

The hybridization of multiple energy sources is crucial for electric vehicles to deliver the same performance as modern fossil fuel-based automobiles. This paper presents a torque and speed control strategy for an electric vehicle DC traction motor by regulating the power flow from two energy sources, namely battery and supercapacitor systems, to resist unpredictable disturbances. A control system with three control loops is applied to regulate the speed of the traction motor. The outer speed control loop is a nonlinear state feedback controller with a disturbance observer, which is capable of handling non-linear systems with unpredictable disturbances. The inner voltage and current control loop are precisely tuned PI controllers. Using an adaptive energy management method that varies depending on the state of charge of the source, the total reference current needed to support the load demand is split between two sources. A laboratory prototype system model is generated, and the performance is analyzed under motoring and regenerative braking conditions. For monitoring and controlling the system, the dSPACE DS1202 real-time simulator is employed. The performance of the proposed control system is evaluated and it is found that the settling time to settle within a tolerance band of 1% is 0.75 s.

Solar photovoltaic‐interfaced shunt active power filter implementation for power quality enhancement in grid‐connected systems

SummaryThis paper proposes a synchronous reference frame (SRF) control with a photovoltaic (PV)‐based shunt active power filter (SAPF) that is utilized to mitigate harmonics impacted by fast‐growing nonlinear load (NL), including compensation of reactive power in distribution systems. The PV module is constructed with an enhanced incremental conductance (EINC) maximum power point tracking approach with an interconnected SAPF. It can support load reactive power demand, eliminating harmonics obtained by NL and the active power supported by the PV arrays. Traditional tuning PI controllers perform poorly under variable load conditions. An adaptive intelligent nonlinear controller capable of simultaneously addressing the impacts of variable load, NL generation of harmonic current, and reactive power demand. This research aims to improve power quality based on an adaptive neuro‐fuzzy inference system (ANFIS) to enhance PV‐SAPF dynamic performance and achieve optimized performance. This control technique reduces the percentage of total harmonic distortion (THD) of the source current below IEEE standards from generating an NL and balanced/unbalanced load condition and regulates the DC‐link voltage constant. Moreover, the presented SRF theory, solar PV, and step‐up converter are addressed. The proposed system control strategy is implemented in the MATLAB/Simulink platform and the Texas Instruments Cyclone IV EP4CE30F484 FPGA board to carry out the hardware implementations of the proposed PV‐SAPF system and validated experimentally.

Active Disturbance Rejection Control of Five-Phase Motor Based on Parameter Setting of Genetic Algorithm

Five-phase induction motors have the characteristics of high torque density, low torque ripple, and flexible control, making them suitable for medium- and low-voltage power supply situations. However, with the expansion of application scenarios, five-phase motors need to cope with increasingly complex operating conditions. Five-phase motors for propeller propulsion will face various complex sea conditions during actual use, and five-phase motors for electric vehicles will also face various complex road conditions and operating requirements during use. Therefore, as a propulsion motor, its speed control system must have strong robustness and anti-disturbance performance. The use of traditional PI algorithms has problems, such as poor adaptability and inability to adapt to various complex working conditions, but the use of an active disturbance rejection controller (ADRC) can effectively solve these problems. However, due to the significant coupling between the variables of induction motors and the large number of parameters in the ADRC, tuning the parameters of the ADRC is complex. Traditional empirical tuning methods can only obtain a rough range of parameter values and may have significant errors. Therefore, this paper uses ADRC based on genetic algorithm(GAADRC) to tune the parameters of the control and design an objective function based on multi-objective optimization. The parameters to be adjusted were obtained through multiple iterations. The simulation and experimental results indicate that GAADRC has lower startup overshoot, faster adjustment time, and lower load/unload speed changes compared to the empirically tuned PI controller and ADRC. Meanwhile, using a genetic algorithm for motor ADRC parameter tuning can obtain optimal control parameters while the control parameter range is completely uncertain; therefore, the method proposed in this paper has strong practical value.

Harmonic Mitigation Using Meta-Heuristic Optimization for Shunt Adaptive Power Filters: A Review

Shunt Adaptive Power Filter (SAPF) is widely used in the performance of power quality improvement activities in the power supply industry for processing industries or civil power sources in the world today based on its simplicity, transparency, high reliability, efficiency, and reliability, and their powerful compensating current-providing nature. The PI controller integrated into the SAPF operation mechanism works with extra high efficiency in selecting the current to compensate for the lost current generated in the power supply due to harmonics generated by the Kp, Ki parameter values. The system operates by the PWM method for bridge rectifier circuits that perform the function of selecting the appropriate compensating current, providing correct compensation for the amount of current loss in the power supply. Adjusting the Kp, Ki parameter to reach the optimal value by different methods is a promising and popular research direction at present. The Kp, Ki parameter serves the right purpose for the PI controller to generate enough PWM pulses to excite the bridge rectifiers to generate just the right amount of compensating current and enough current to be compensated on the power supply. The commonly used Kp, Ki parameter adjustment methods include the Ziegler Nichols closed-loop vibration method, the P-Q theoretical method, and several other methods. This study conducts a comprehensive review of the literature on modern strategies for adjusting the Kp, Ki parameters in the PI controller in the SAPF suite by using the meta-heuristic optimization method. This study performs classification according to the operation mode of meta-heuristic optimization methods to adjust the Kp, Ki parameter to control the PI to select the correct PWM frequency to activate bridge rectifiers to select the most optimal compensation current to compensate for the loss of current on the power supply to meet the goal of improving power quality in accordance with IEEE 519-2022 standard, leading to the total harmonic distortion (THD) value is below 5%. The study presents in detail some meta-heuristic optimization algorithms, including applications, mathematical equations, and implementation of flow charts for SAPF and provides some open problems for future research. The main objective of this study is to provide an overview of applying meta-heuristic optimization algorithms to the Kp, Ki parameter tuning of PI controllers.

Efficiency analysis of grid tied PV system with KY integrated SEPIC converter

Today's power generation relies heavily on renewable energy, which also contributes to distributed generation, microgrids and smart cities. PV system is a rapidly developing source of renewable energy and acts as stand-alone or grid-connected systems. The utilization of PV energy faces a drastic increase due to the high efficiency of solar cells and enhancements in the design of PV panels. It demands high gain converters for major applications but existing converters generate pulsating outputs leading to output voltage with ripples. In this view, researchers are constantly focusing on converters with increased efficiency and gain generating minimized ripples in resulting current and voltage. Considering these dilemmas, an efficient PV system with KY integrated SEPIC converter is introduced in this article. The KY converter provides improved voltage conversion ratio as well as minimizes the voltage stress while the SEPIC converter offers added step up gain. The proposed converter is regulated by a GWO tuned PI controller for solving the optimization issues. It prevents the local optimum and is robust, simple with the application to complicated optimization tasks. A grid connected 1φ VSI is adopted for ensuring smooth operation with the aid of PWM generator. The simulations are carried out in MATLAB simulation software to prove the efficacy of the introduced approach. The obtained outputs reveal that this developed system has minimal THD value of 1.6% and hence it has higher efficiency which is marked as 97%.

IPDT Model-Based Ziegler-Nichols Tuning Generalized to Controllers with Higher-Order Derivatives.

The paper extends the earlier work entitled "Making the PI and PID Controller Tuning Inspired by Ziegler and Nichols Precise and Reliable", to higher-order controllers and a broader range of experiments. The original series PI and PID controllers, based on automatic reset calculated by filtered controller outputs, are now augmented by higher-order output derivatives. This increases the number of degrees of freedom that can be used to modify the resulting dynamics, accelerates transient responses, and increases robustness to unmodeled dynamics and uncertainties. The fourth order noise attenuation filter used in the original work allows for the addition of an acceleration feedback signal, thus resulting in a series PIDA controller or even a jerk feedback that leads to a PIDAJ series controller. Such a design can further use the original process and filter approximation of the step responses through the integral-plus-dead-time (IPDT) model, while allowing experimentation with disturbance and setpoint step responses of the series PI, PID, PIDA and PIDAJ controllers, and thus, evaluating the role of output derivatives and noise attenuation from a broader perspective. All controllers considered are tuned using the Multiple Real Dominant Pole (MRDP) method, which is complemented by a factorization of the controller transfer functions to achieve the smallest possible time constant for automatic reset. The smallest time constant is chosen to improve the constrained transient response of the considered controller types. The obtained excellent performance and robustness allow the proposed controllers to be applied to a wider range of systems with dominant first-order dynamics. The proposed design is illustrated on a real-time speed control of a stable direct-current (DC) motor, which is approximated (together with a noise attenuation filter) by an IPDT model. The transient responses obtained are nearly time-optimal, with control signal limitations active for most setpoint step responses. Four controllers with different degrees of derivative with generalized automatic reset were used for comparison. It was found that controllers with higher-order derivatives may significantly improve the disturbance performance and virtually eliminate overshoots in the setpoint step responses in constrained velocity control.

Implementation and performance analysis of a novel sliding mode controller for bidirectional DC to DC converter in aircraft application

Purpose The purpose of this paper is to implement a closed loop regulated bidirectional DC to DC converter for an application in the electric power system of more electric aircraft. To provide a consistent power supply to all of the electronic loads in an aircraft at the desired voltage level, good efficiency and desired transient and steady-state response, a smart and affordable DC to DC converter architecture in closed loop mode is being designed and implemented. Design/methodology/approach The aircraft electric power system (EPS) uses a bidirectional half-bridge DC to DC converter to facilitate the electric power flow from the primary power source – an AC generator installed on the aircraft engine’s shaft – to the load as well as from the secondary power source – a lithium ion battery – to the load. Rechargeable lithium ion batteries are used because they allow the primary power source to continue recharging them whenever the aircraft engine is running smoothly and because, in the event that the aircraft engine becomes overloaded during takeoff or turbulence, the charged secondary power source can step in and supply the load. Findings A novel nonsingular terminal sliding mode voltage controller based on exponential reaching law is used to keep the load voltage constant under any of the aforementioned circumstances, and its performance is contrasted with a tuned PI controller on the basis of their respective transient and steady-state responses. The former gives a faster and better transient and steady-state response as compared to the latter. Originality/value This research gives a novel control scheme for incorporating an auxiliary power source, i.e. rechargeable battery, in more electric aircraft EPS. The battery is so implemented that it can get regeneratively charged when primary power supply is capable of handling an additional load, i.e. the battery. The charging and discharging of the battery is carried out in closed loop mode to ensure constant battery terminal voltage, constant battery current and constant load voltage as per the requirement. A novel sliding mode controller is used to improve transient and steady-state response of the system.

Solar-powered five-leg inverter-driven quasi-dynamic charging for a slow-moving vehicle

Road transport is becoming increasingly electric as it becomes more environmentally friendly. A green transportation system includes solar arrays along the roadside, encouraging the eco-friendly EV charging system. This paper proposes a road-side photovoltaic system to charge the batteries of slow-moving electric vehicles using a five-leg inverter. The five-legged inverter, which utilizes a stand-alone PV system to drive the charging pads, enhances the probability of achieving the sustainability goal. The limitations of the conventional H-bridge inverter, such as its more prominent design and higher number of switches or straightforward design and restricted power level, are addressed by this converter. The proposed 3.3 kW, 85 kHz inverter energizes the four transmitter pads while a receiver pad moves over the transmitter pads and inductively extracts the power. The d.c.-d.c. converter is used to feed the power to the proposed inverter. The P and O-based MPPT algorithm with a tuned PI controller is used to generate the driving pulses of the d.c.-d.c. converter. The signals are generated based on the voltage and current output of the solar panel output. This control algorithm ensures the stability of the system output response. Additionally, the tuned d.c.-d.c. converter achieves maximum efficiency independent of the load resistance. The system maintains constant power transfer profile concerning load resistance variations. The 520*520 mm Double D-pad transmits the power, while the series-series compensation network assists the charging pads in achieving resonance. The developed systems’ nominal charging voltage and current are 144 V, 20 A, with an equivalent battery resistance of 7.2 Ω.

Dynamic Direct Voltage MTPA Current Sensorless Drives for Interior PMSM-Based Electric Vehicles

This research presents a current sensorless dynamic direct voltage method based on the maximum torque per ampere (MTPA) technique to control the rotor speed of interior permanent magnet synchronous motors (IPMSMs) for electric vehicle (EV) applications. It considers the dynamic model of the machine and generates a particular pair of voltage amplitude and its angle to track the speed profile of the driving cycle. The proposed control strategy contributes by eliminating the need for a current sensor, avoiding cascaded PI controllers, and overcoming the limitations of transient states. Consequently, the measurement noise and equipment failure do not affect the control loop, the tuning of a single PI controller becomes much simpler than two cascaded PI controllers, and the absence of MTPA tracking and current or voltage overshooting at transient states cannot limit the performance of the control technique. Additionally, the aforementioned modifications improve the response time of the proposed strategy and make it nimble enough for EV applications. A comparative analysis is carried out on the proposed current sensorless dynamic direct voltage MTPA control, simplified dynamic direct voltage control (S-DDVC), and the field oriented control (FOC) models. The outcomes confirm that the introduced model substantially reduces the inrush current and voltage. Experimental results with quantitative tracking and energy consumption assessments reveal that the proposed method demands 9.7% lower energy than the FOC counterpart in urban driving plans by operating IPMSM in high efficiency region.

Cooling/heating dual-stage actuators decoupling structure design and energy management strategy for thermostatic airflow control

The temperature control system is required to have both cooling and heating actuators to achieve a high-precision thermostatic indoor air environment of industrial buildings. The coupling of the two actuators and the generation of redundant energy consumption leave a sufficient gap to be optimized. Combining the Linear Quadratic Regulator (LQR) approach with the sensitivity decoupling structure, we construct a dual-stage PI controller for this type of dual-input single-output system. Meanwhile, a proposed energy management technique for lowering energy consumption can alter the controlled quantities adaptively and synergistically. Accordingly, the multi-objective teaching–learning-based optimization algorithm is implemented to determine the optimal parameters and establish a balance between temperature stability and energy consumption. Simulation and experimental platforms are built to verify the design effect. Compared with four typical single-stage PI controller tuning algorithms, the proposed method has better output temperature stability with an improvement of the index by at least 14%. And the results reveal that the control input is decreased by 45.6% when the energy management technique is implemented, while the stability index of the final output temperature is maintained at about the same level.

BWOA based metaheuristic approach for uncertain nonlinear milling CNC machine system

AbstractThe scientific advances in the machining process inculcate intelligence in computer numerical control (CNC), to enhance reliability and quality production. The study made in this article has been directed towards the metaheuristic‐based intelligent control of a nonlinear uncertain CNC machine for milling purposes. The presence of cutting force and frictional type constraints, along with parametric uncertainties, degrade the production quality in milling operations. Moreover, enhanced performance of milling operation can be achieved via intelligent control of position and velocity of machine table and tool. In this work, the black widow optimization algorithm (BWOA) driven proportional–integral–derivative (PID) and PI controllers are employed for the position and the velocity of machine table and tool, respectively. Servo and regulatory are the two important control problems that are considered, analysed, and addressed in this work. The most challenging task of simultaneous tuning of PI and PID controllers is also addressed in this brief. Finally, a vivid comparative analysis of five state‐of‐the‐art optimization techniques is also performed, and the obtained results demonstrate the superiority of the proposed control scheme. While dealing with the servo performance of x‐axis, the proposed controller gives a 100% improvement in overshoot and 61.54% improvement in settling time, and for regulatory performance, it gives a 98.45% improvement in undershoot. Moreover, for z‐axis control, the proposed controller gives a 100% improvement in overshoot and 61.54% improvement in settling time and for regulatory performance gives a 100% improvement in undershoot compared to published literature.

A Safe Bayesian Optimization Algorithm for Tuning the Optical Synchronization System at European XFEL

The European XFEL is one of the largest linear particle accelerator in the world used to generate extremely intense and ultra short X-Ray flashes to study ultra fast, time resolved chemical processes. In order to improve the quality of the observations, the laser-based optical synchronization system is optimized by tuning PI controllers using a safe Bayesian optimization approach. Since machine time on the European XFEL is very expensive, the algorithm needs to find the optimal parameters as fast as possible. In this contribution, we present a safe Bayesian optimization algorithm which, while guaranteeing safety, shows significantly improved convergence speed and noise robustness. Application and comparison results are presented in simulation for the optical synchronization system of the European XFEL and an experimental demonstration is performed for a laboratory synchronization system.

An optimization-based method for tuning PI controllers for systems subject to control and output constraints

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informujące swoich czytelników o najnowszych osiągnięciach naukowych i nowoczesnych rozwiązaniach technicznych w Polsce i na świecie, popularyzuje problemy techniczne oraz poszerza wiedzę i kulturę techniczną.

Solar PV integrated dynamic voltage restorer for enhancing the power quality under distorted grid conditions

• PV integrated DVR for voltage compensation is presented. • An enhanced INC controller is used for MPPT. • An optimum tuned PI controller using ANFIS controller is presented. • The load voltages with and without DVR along with the compensated load voltages of both sag and swell for existing PI-tuned controllers with the proposed PI-ANFIS controller are compared. Renewable energy sources (RES) play an essential role in the generation of electrical power, particularly in grid-connected systems. As a result of RES interaction with the grid, power quality issues arise. The grid suffers from voltage sag, swell, and harmonics due to power quality Problems, which have an impact on consumers. The most cost-effective alternative for overcoming the aforementioned issues is a series compensator, such as the Dynamic Voltage Restorer (DVR). This article presents a solar PV integrated DVR using a rotating dq reference frame controller to improve power quality issues. The application of an enhanced INC MPPT approach and the design of an efficient PI controller for DVR to improve power quality is the primary objective of this paper. The optimal tuning of the PI controller is achieved by using the Adaptive Neuro-Fuzzy Inference System (ANFIS). Furthermore, the proposed solar PV, boost converter, and rotating dq reference frame control are mathematically analyzed. In the presence of a balanced load, the system dynamic performance is tested. The suggested system performance is verified in real time with MATLAB/SIMULINK software, and Opal-RT closely resembles real-world hardware supported by FPGA processors. The results show that the design is effective at limiting voltage sag, swell, and THD% levels at the load side under the IEEE standard limit.

Analysis and Implementation of Sliding Mode Controller-Based Variable Frequency Drive Using the SCADA System

Vector control of an asynchronous machine is traditionally accomplished by analogizing it to a separately excited DC machine. It provides decoupled torque and flux control that is perpendicular to each other, ensuring that neither vector interferes with the other. So, despite their close interconnection, torque and speed control are accomplished separately. The rotor flux is aligned with the direct axis of the synchronously rotating reference frame to achieve this. The PI controllers are critical in achieving the variable frequency drive (VFD) desired topology. The system employs three types of controllers: flux, speed, and torque. The flux controller is easy to tune, but the speed and torque controllers are more difficult to tune because the speed controller's output is the torque controller’s reference signal. Furthermore, there is no well-defined method for tuning the controllers in a vector control system. However, perfect tuning is required for the machine’s better dynamic behavior. It is clear from the above analysis that system identification is critical for tuning PI controllers. However, as an asynchronous machine, obtaining a decoupled system transfer function is extremely difficult. To solve this problem, the proposed system combines a seven-level pulse width modulation (PWM) inverter for vector control of a three-phase asynchronous nonstandard induction machine used in critical applications in nuclear power plants with a sliding mode control technique that eliminates the complexity of PI tuning. A second-order sliding mode controller could be used in the future to reduce the chattering and parameter variation effects. This controller can be enhanced with fuzzy logic principles to make it more robust and reliable, allowing it to be used in future drive designs for high-rating motors with critical applications.

High fidelity modelling and GA optimized control of yaw dynamics of a custom built remotely operated unmanned underwater vehicle

In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities.

Meta-reinforcement learning for the tuning of PI controllers: An offline approach

Meta-learning is a branch of machine learning which trains neural network models to synthesize a wide variety of data in order to rapidly solve new problems. In process control, many systems have similar and well-understood dynamics, which suggests it is feasible to create a generalizable controller through meta-learning. In this work, we formulate a meta reinforcement learning (meta-RL) control strategy that can be used to tune proportional–integral controllers. Our meta-RL agent has a recurrent structure that accumulates “context” to learn a system’s dynamics through a hidden state variable in closed-loop. This architecture enables the agent to automatically adapt to changes in the process dynamics. In tests reported here, the meta-RL agent was trained entirely offline on first order plus time delay systems, and produced excellent results on novel systems drawn from the same distribution of process dynamics used for training. A key design element is the ability to leverage model-based information offline during training in simulated environments while maintaining a model-free policy structure for interacting with novel processes where there is uncertainty regarding the true process dynamics. Meta-learning is a promising approach for constructing sample-efficient intelligent controllers.

Design and implementation of optimal controller for DFIG-WT using autonomous groups particle swarm optimization

<span>There are many types of generators used within wind energy such as doubly fed induction generator (DFIG). Particle swarm optimization (PSO) algorithm is simple, robust and easy to implement. In addition to the privilege of PSO, autonomous groups particle swarm optimization (AGPSO) has the advantages of using diverse autonomous groups which result in more randomized and directed search. Applying AGPSO to tune PI controller to control DFIG is proposed in this paper. An implemented laboratory prototype consists of brushless DC motor (BLDC) for simulating the various wind speeds. Wound rotor induction machine, working as DFIG. This system is a stand-alone system. System identification strategy was introduced in this work. In this study, AGPSO is suggested for tuning the PI controller. Different case studies are performed, such as step changes in both speed and electrical load for showing the effectiveness of the proposed algorithm. For comparison PSO is used to tune the PI controller. Results from experiments clarify the feasibility of the proposed methodology. It is approved that AGPSO achieves the prevalent control execution (quicker transient response and more modest steady state error (ess)) contrasted with the PSO in tuning PI controller when applied to be used with off-grid systems. </span>

RETRACTED: Cuckoo Search Optimization based PI Controller Tuning for Hopper Tank System

The paper work focuses on soft computing and Conventional controller tuning approach to design of PI controller, for a nonlinear hopper tank liquid level control system process Industries. The automation industries provide conventional techniques where it is impossible to maintain its settling time which motivates to do the research in this field. The system processes the combination of a conical and cylindrical tank for providing Multi-region based mathematical modelling to obtain the first order with delay time (FOPDT) process transfer function model. The Ziegler Nichols, Cohen-coon, Tyreus Luben, CHR (Chien, Hrones, and Reswick), IMC (Internal Model Control), Direct Synthesis, FOPI(Fractional Order PI) Conventional tuning formulae and Cuckoo Search Optimization (CSO) algorithm are used to optimize the servo regulatory responses of PI controller. The integral and proportional gain of the PI controller is said to produce the fastest settling time and reduces the error using performance indices and achieves Liquid Level control in hopper tank. Comparison is made for the various conventional controller tuning methods with Cuckoo Search Optimization tuning responses and identified to CSO-PI method offers enhanced Optimized Performance of settling time which was about 105.58 s which is relatively less while comparing to Conventional PI controller tuning methods for a different region based system.