Conventional Proportional Integral Derivative Controller Research Articles

Adaptive neuro fuzzy technology to enhance PID performances within VCA for grid-connected wind system under nonlinear behaviors: FPGA hardware implementation

Wind Energy Conversion Systems (WECSs) exhibit nonlinear behavior due to internal and external disturbances, which can significantly impact the control performance. Firstly, this paper proposes a Vector Control Approach (VCA) based on Adaptive Neuro Fuzzy (ANF) of Proportional Integral Derivative (PID) controllers for a variable speed Permanent Magnet Synchronous Generator (PMSG)-based WECS connected to the grid to address these issues. The proposed ANF-PID controller combines the robustness of the fuzzy logic with the neural network's performance to ensure high accuracy, excellent tracking, and robustness against the WECS's nonlinear behavior. The neural network automates the placement of fuzzy logic membership functions. Secondly, this paper suggests a real-time hardware implementation of the VCA based on ANF-PID using a Field Programmable Gate Array (FPGA) to reduce the processing time and the WECS's sampling period. The effectiveness of the VCA based on ANF-PID controllers is validated by conducting a numerical simulation within the Matlab-Simulink using the Xilinx System Generator tool, alongside an experimental in-the-loop hardware implementation using FPGA board. Different scenarios are conducted to validate the robustness and stability of the proposed VCA based on ANF-PID control schemes, which is compared to Conventional Fuzzy PID (C-FPID) and PID controllers. The results exhibit that the VCA based on ANF-PID controllers outperforms VCA-C-FPID and VCA-PID controllers in terms of precision, tracking accuracy, and robustness against the WECS's nonlinear behavior and the PMSG parameter variations. The performances of the proposed controllers are tested under some criteria. In fact, the proposed ANF-PID controller provides a Mean Absolute Error (MAE), a Mean Squared Error (MSE) and a Root Mean Squared Error (RMSE) of 8.1911 × 10−04, 8.4181 × 10−05 and 0.0092, respectively. Compared to the C-FPID controller, the proposed controller offers an improvement gain of 98.96 %, 99.61 %, and 93.79 % of MAE, MSE, and RMSE, respectively. When compared to the PID controller, its improvement is even more significant, with a reduction of 99.9919 %, 99.9999 %, and 99.9288 % of MAE, MSE, and RMSE, respectively. In addition, the ANF-PID-based VCA utilizes hardware resources on the FPGA efficiently, requiring only 65.60 % slices LUT, 0.28 % LUTRAM, and 22.99 % FF. This makes it a more cost-effective option compared to the VCA-C-FPID controller for diverse control systems.

Trajectory Tracking Nonlinear Hybrid Control of Automated Guided Vehicles

Automated guided vehicles (AGVs), so necessary in industrial environments, require precise control of trajectory tracking to make accurate stops at logistics stations, such as loading stations, or to pick up or drop off trolleys, pallets, or racks. This paper proposes a hybrid control architecture for trajectory tracking of a hybrid tricycle-differential AGV. The control strategy combines conventional proportional integral derivative (PID) control with advanced nonlinear Lyapunov control (LPC). The LPC is used for trajectory tracking while the PID is used for speed control of the robot. The stability of the controller is demonstrated for any differentiable trajectory. When a PID optimized with genetic algorithms is compared with the proposed controller for several trajectories, the LPC outperforms it in all cases.

Modeling and adaptive neuro-fuzzy inference system control of quarter electric vehicle

Electric vehicles (EVs) have gained importance in recent years, prompting the development of several control systems to improve their efficiency and performance. In this work, a quarter electric vehicle (QEV) was controlled using a conventional proportional integral derivative (PID) and fuzzy controller to examine and compare with the response of the adaptive neuro-fuzzy inference system (ANFIS) controller. The response of the ANFIS controller was evaluated using MATLAB/Simulink according to different parameters and compared with those of other controllers. In addition, the simulation was based on different driving conditions such as the acceleration and deceleration modes and the type of road: wet and dry. The simulations were carried out on a longitudinal electric vehicle model based on a brushless DC motor, including the Pacejka tire model. The results showed that the ANFIS controller outperformed the PID and fuzzy logic controllers, providing superior dynamic responsiveness and stability when the ANFIS controller smoothly followed the input speed and the longitudinal slip value reached 3%.

A novel modified mountain gazelle optimizer for tuning parameter proportional integral derivative of DC motor

This article presents a modified method of mountain gazelle optimizer (MMGO) as a direct current (DC) motor control. Mountain gazelle optimizer (MGO) is an algorithm inspired by the life of the mountain gazelle animal in nature. This animal concept has five essential steps that are duplicated in mathematical modeling. This article uses two tests to get the performance of the MMGO method. The first test uses a benchmark function test with a comparison method, namely the sine tree seed algorithm (STSA) and the original MGO. The second test is the application of MMGO as a DC motor control. The simulation results show that MMGO can reduce the overshoot of conventional proportional integral derivative (PID) control by 0.447% and has a better integral time square error (ITSE) value of 5.345 than conventional PID control. Thus, the MMGO method shows promising performance.

Temperature control of beer fermentation based on variable domain fuzzy PID and neural network technology and its application analysis

AbstractTemperature control in the beer manufacturing process is crucial for product quality. Given the gap between China's automation in beer production and the international level, improving the technology in this area has gradually become a core issue in optimizing domestic beer production. This study combines a proportional integral derivative controller with a fuzzy modeling strategy and incorporates a variable‐domain structure to propose a variable‐domain fuzzy proportional integral derivative controller control method. To cope with the challenges of production interaction, the study also introduces neural network technology. The experimental data indicated that the variable‐domain fuzzy proportional integral derivative controller outperforms the conventional proportional integral derivative controller and the fuzzy proportional integral differential controller in terms of overshooting, with a maximum overshoot of only 1.0, compared with 0.50 and 0.70, respectively. The variable‐domain fuzzy proportional integral differential controller exhibited a minimal overshoot of only 0.01 when the model parameter is increased by 20%. In comparison, the other methods reach overshoot values of 0.92 and 1.0. The proposed method maintained superior stability even under the influence of impulse disturbance, step disturbance, and modeling variations. These results demonstrated that the research method is significantly more stable than both the proportion integration differentiation (PID) controller and fuzzy PID controller in complex dynamic parameter environments. The proposed method involved 60 rounds of neural network control, which was successfully implemented. The temperature readings T1 and T2 remained stable within the range of 1.0%–1.02% throughout the experiments. The study demonstrates that the proposed methods have higher accuracy and less fluctuation in actual application, making them more available. Taken together, the above results show that the combination of variable‐domain fuzzy PID controller and neural network technology in beer production has achieved excellent control results. This study not only provides a strong technical support for the progress of beer production technology in China, but also has important industrial application value and wide promotion prospects.

Jellyfish Search Optimized Gated Recurrent Unit Based FOPID/PID for Enhanced Automatic Voltage Regulator

The most essential mechanism for regulation in power systems is the Automatic Voltage Regulator (AVR) device, which maintains generator terminal voltage within predefined parameters. This research paper introduces an innovative approach to enhance AVR system performance through the ideal setting for a fractional-order PID (FOPID) controller that leverages Bidirectional Gate Recurrent Unit (BiGRU)-based deep learning techniques in conjunction with the Jellyfish Search Optimization (JSO) algorithm. To enhance flexibility and precision in control, the conventional proportional integral derivative (PID) controller is replaced with a FOPID controller, whose parameters are optimized by means of the JSO algorithm. The effectiveness of the proposed Jellyfish Search Optimization-based most advanced controllers (JSO-FOPID/PID) is compared with the existing FOPID controller implemented using the Seagull Optimization Algorithm (SOA-FOPID). Comprehensive analyses, including transient time, robustness, stability, and convergence analysis, are conducted subject to applying both stable and alternating load situations to validate the proposed approach. Experimental validation is performed using the MATLAB R2022a application with FOMCON tools and a laboratory prototype for testing. The simulations and experiments demonstrate that the projected AVR consistently delivers optimum dynamic responses and enhances overall stability when compared to existing methods, underscoring its efficacy in voltage regulation and power system control.

A Multi-Source Power System’s Load Frequency Control Utilizing Particle Swarm Optimization

Electrical power networks consist of numerous energy control zones connected by tie-lines, with the addition of nonconventional sources resulting in considerable variations in tie-line power and frequency. Under these circumstances, a load frequency control (LFC) loop gives constancy and security to interconnected power systems (IPSs) by supplying all consumers with high-quality power at a nominal frequency and tie-line power change. This article proposes employing a proportional–integral–derivative (PID) controller to effectively control the frequency in a one-area multi-source power network comprising thermal, solar, wind, and fuel cells and in a thermal two-area tie-line IPS. The particle swarm optimization (PSO) technique was utilized to tune the PID controller parameters, with the integral time absolute error being utilized as an objective function. The efficacy and stability of the PSO-PID controller methodology were further tested in various scenarios for proposed networks. The frequency fluctuations associated with the one-area multi-source power source and with the two-area tie-line IPS’s area 1 and area 2 frequency variations were 59.98 Hz, 59.81 Hz, and 60 Hz, respectively, and, in all other investigated scenarios, they were less than that of the traditional PID controller. The results clearly show that, in terms of frequency responses, the PSO-PID controller performs better than the conventional PID controller.

Fuzzy Logic Speed Regulator for D.C. Motor Tuning

A D.C. motor's rotational speed is regulated in this study using a PID controller and a fuzzy logic controller. In contrast to the fuzzy logic controller, which uses rules based on knowledge and experience, the proportional-integral-derivative (PID) controller requires a mathematical system model. This study investigates the regulation of a DC motor's velocity using PID and fuzzy logic controllers. The PID controller utilizes a mathematical model and parameter tuning by trial and error. Still, the fuzzy logic controller (FLC) operates on rule-based knowledge, enabling it to handle the nonlinear features of the DC motor effectively. The FLC design entails intricate determinations, including the establishment of a rule base and the process of fuzzification. A total of 49 fuzzy rules have been devised to achieve precise control. Based on MATLAB/SIMULINK simulations, the study concludes that the Fuzzy Logic Controller (FLC) beats the Proportional-Integral-Derivative (PID) controller. The FLC exhibits superior transient and steady-state responses, shorter response times, reduced steady-state errors, and higher precision. This study emphasizes the efficacy of the FLC (Fuzzy Logic Controller) in dealing with the difficulties associated with DC motor control. It presents a strong argument for the suitability and efficiency of FLCs in industrial environments compared to conventional PID (Proportional-Integral-Derivative) controllers. There are a wide variety of ways to construct a fuzzy logic controller. The speed error and the rate of change in the speed error are two inputs to the FLC. Defuzzification is done by focusing on the core of the problem. The results show that FLC is superior to PID controllers in efficiency and effectiveness due to its reduced transient and steady-state factors.

Two Feedback PID Controllers Tuned with Teaching–Learning-Based Optimization Algorithm for Ball and Beam System

The proportional integral derivative (PID) controller continues to be the most popular and widely used in the industry despite the development of various artificial intelligence-based controllers due to its simplicity and ease of use. Setting PID parameters, especially in non-linear systems, is still a significant problem. This study presents a cascade PID tuning technique based on the teaching–learning-based optimization (TLBO) algorithm. The proposed method is tested for the position control of a non-linear ball and beam system. A comparative analysis of the proposed method is done with the conventional tuning method and particle swarm optimization-tuned cascade PID controller. The optimization was carried out using integral time absolute error, integral time error, and integral square error as objective functions. It was observed that the evolutionary algorithm-based controller tuned by TLBO gives a much better response regarding rise time, settling time, and overshoot. To test the effectiveness and validity of the proposed controller robust analysis of the proposed controller is carried out with a step disturbance applied at t = 3 s. The comparative study proves that TLBO-tuned response is better than other controllers. Sensitivity analysis of the proposed controller is also performed by varying system parameters.

Dual fuzzy logic PID controller based regulating of dc motor speed control with optimization using Harmony Search algorithm

This paper discusses the implementation of a Proportional-Integral-Derivative (PID) controller for regulating the speed of a closed loop four quadrant chopper fed DC motor. The PID controller is combined with a Dual Fuzzy Logic Controller to form a DFPID controller for enhancing the performance of speed control of the DC motor. The DFLC is optimized using a metaheuristic algorithm known as Harmony Search Algorithm (HSA). The major aim of this research is to gain an effective control over the speed of the motor in the closed loop environment. For achieving this, the parameters for the DFPID are selected through time domain analysis which aims to satisfy the requisites such as settling time and peak overshoot. Initially, the fuzzy logic controller in the DFPID controls the coefficients of the PID achievement gain an effective control over the system error and rate of error change. Further, the DFPID is improved by the HAS for obtaining a precise correction. The solutions obtained by tuning the DFPID controller are evaluated from simulation analysis conducted on a MATLAB/SIMULINK platform. The closed loop performance is analyzed in both time and frequency domain analysis and the performance of DFPID is optimized using the HSA algorithm to obtain precise value of the control process. As observed from the Simulation analysis, the DFPID-HSA generates optimized control signals to the DC motor for controlling the speed. The performance of the intended speed control approach is analyzed in terms of different evaluation metrics such as motor speed, torque and armature current. Experimental outcomes show that the proposed approach achieves better control performance and faster speed of DC motor compared to conventional PID controllers and SMC controllers

A quantum particle swarm optimization-based optimal LQR-PID controller for load frequency control of an isolated power system

One of the characteristics of a robust power grid is minimal variations in its frequency to load change or loss in generating unit(s). From the perspective of optimal control theory, the issue of load frequency control in the context of the interconnected functioning of power systems is investigated in this work, and a novel load frequency controller is proposed for a single area isolated power network. This novelty incorporates all the primary characteristics of the solutions that are based on a mixture of optimal controller designs by establishing a linear quadratic regulator optimized with quantum particle swarm optimization to design a proportional integral derivative (PID) controller unlike the conventional PID controller designs that are based on a combined Ziegler-Nichols and root locus (ZN-RL) method and manual tuning. The simulation results of the proposed controller using MATLAB show its efficacy in not only ensuring that there is no steady-state error in terms of the system frequency with load changes but also in achieving smoother transients. Following these landmark achievements, a transfer function model of the resulting power grid is constructed. The outcome of the model reveals that the system transients have been improved while keeping the intended steady-state characteristics. Furthermore, it is observed that the proposed load frequency controller has the best performance when compared with the combined ZN-RL method and the manual PID designs. This, therefore, demonstrates the superiority of the proposed design for load frequency control in power systems.

Control of Linear Electric Actuator with Hybrid Fuzzy PID Controller

In this study, the actuator, which converts circular motion to linear motion, is controlled with a smart hybrid controller. The model includes the analysis of electrical and mechanical parts. The controller, on the other hand, has a design methodology that cleverly blends classical PID (Proportional Integral Derivative) and fuzzy controllers. In this design methodology, classical PID and fuzzy controller have an error parameter dependent blending mechanism. In order to compare the performance of the hybrid controller, the coefficients of the classical PI and PID controllers were obtained by tuning and the coefficients with the best response were used. For performance comparison of conventional controllers and hybrid controllers; rise time, settling time, maximum overshoot and common performance indices values are used. The hybrid controller showed higher performance in terms of maximum overshoot, settling time, rising time and performance indices. The potential benefits of using a hybrid controller for the control of linear electric actuators were demonstrated by the fact that it performed better than conventional PID controllers on the majority of evaluation criteria.

Ters Sarkaç Sisteminde Referans Model Kullanarak Kesir Dereceli PID Denetleyici Tasarımı

The proportional Integral Derivative (PID) controller has three basic parameters: Proportional gain (Kp), Integral gain (Ki) and Derivative gain (Kd). In a conventional PID controller, integral and derivative operators are integer order. The researchers proposed a fractional order PID (PIλDµ) controller by using the fractional integral and derivative operators instead of the integer order integral and derivative operators in the traditional PID controller because it improves the control performance. The PIλDµ controller has an additional fractional integrator degree (λ) and fractional derivative degree (µ). In this study, the focus is on the design of a fractional-order PID controller according to a reference model in the time domain. Bode's ideal transfer function was used as the reference model. It is aimed to obtain PIλDµ parameters by minimizing the error between the time domain response of Bode's ideal transfer function model and the output of the system to be controlled by using the optimization method. Genetic Algorithm (GA) optimization was used as the optimization method. The study was carried out as a simulation study on an inverted pendulum system with a single-input multiple-output (SIMO) structure.

Design and Control of a Novel Variable Stiffness Series Elastic Actuator

This article expounds the design and control of a new variable stiffness series elastic actuator (VSSEA). It is established by employing a modular mechanical design approach that allows us to effectively optimize the stiffness modulation characteristics and power density of the actuator. The proposed VSSEA possesses the following features: no limitation in the work range of output link; a wide range of stiffness modulation (∼20 N·m/rad to ∼1 KN·m/rad); low-energy-cost stiffness modulation at equilibrium and nonequilibrium positions; compact design and high torque density (∼36 N·m/kg); and high-speed stiffness modulation (∼3000 N·m/rad/s). Such features can help boost the safety and performance of many advanced robotic systems, e.g., a cobot that physically interacts with unstructured environments and an exoskeleton that provides physical assistance to human users. These features can also enable us to utilize variable stiffness property to attain various regulation and trajectory tracking control tasks only by employing conventional controllers, eliminating the need for synthesizing complex motion control systems in compliant actuation. To this end, it is experimentally demonstrated that the proposed VSSEA is capable of precisely tracking the desired position and force control references through the use of the conventional proportional–integral–derivative controllers.

Control of Oxygen Excess Ratio for a PEMFC Air Supply System by Intelligent PID Methods

The hydrogen fuel cell is a quite promising green device, which could be applied in extensive fields. However, as a complex nonlinear system involving a number of subsystems, the fuel cell system requires multiple variables to be effectively controlled. Oxygen excess ratio (OER) is the key indicator to be controlled to avoid oxygen starvation, which may result in severe performance degradation and life shortage of the fuel cell stack. In this paper, a nonlinear air supply system model integrated with the fuel cell stack voltage model is first built, based on physical laws and empirical data; then, conventional proportional-integral-derivative (PID) controls for the oxygen excess ratio are implemented. On this basis, fuzzy logic inference and neural network algorithm are integrated into the conventional PID controller to tune the gain coefficients, respectively. The simulation results verify that the fuzzy PID controller with seven subsets could clearly improve the dynamic responses of the fuel cells in both constant and variable OER controls, with small overshoots and the fastest settling times of less than 0.2 s.

Research trends in methods for controlling macro-micro motion platforms

With ongoing economic, scientific, and technological developments, the electronic devices used in daily lives are developing toward precision and miniaturization, and so the demand for high-precision manufacturing machinery is expanding. The most important piece of equipment in modern high-precision manufacturing is the macro-micro motion platform (M3P), which offers high speed, precision, and efficiency and has macro-micro motion coupling characteristics due to its mechanical design and composition of its driving components. Therefore, the design of the control system is crucial for the overall precision of the platform; conventional proportional–integral–derivative control cannot meet the system requirements, and so M3Ps are the subject of a growing range of modern control strategies. This paper begins by describing the development history of M3Ps, followed by their platform structure and motion control system components, and then in-depth assessments of the macro, micro, and macro-micro control systems. In addition to examining the advantages and disadvantages of current macro-micro motion control, recent technological breakthroughs are noted. Finally, based on existing problems, future directions for M3P control systems are given, and the present conclusions offer guidelines for future work on M3Ps.

Thermodynamic modeling and control of hybrid solar-fossil fuel power generation and storage system

Hybrid solar-fossil fuel power generation and storage (HSFF-PGS) is an innovative technology characterized by renewable and conventional hybrid energy utilization and thermochemical energy storage. The HSFF-PGS system has prominent advantages in improving the solar energy grade and storing thermal energy with a high energy density. However, investigations on its dynamic characteristics and real-time control are inadequate, which hinders the development of the system’s automation level to some degree. To deal with this issue, first, in this paper a dynamic model that includes the thermal inertia and reaction kinetics is constructed. Simulations under the nominal conditions show that the net solar utilization efficiency and energy savings ratio can reach 48.28% and 34.5%. Second, the design/off-design working condition properties are presented and the dynamic characteristics, such as nonlinearity and coupling, are explored to identify the control difficulties. The calculation results show that the relative gain array value ranges from 1 to 5 and the Vinnicombe gap is greater than 0.4 under most working conditions. Based on the dynamic analysis results, distributed active disturbance rejection control (ADRC) strategies for power generation and exhaust temperature regulation are developed. Then, the control performances of the proposed distributed ADRC and the conventional proportional integral derivative (PID) control are compared under nominal working conditions and typical-day scenarios. The control performance indexes are improved by 66.7% on average. Thus, the distributed ADRC is confirmed to be a better control method and has a good potential for dynamic operation of the HFSS-PGS system.

An adaptive control method for the covers on the south roof of Chinese solar greenhouses: A case study of insulation blankets

The Chinese solar greenhouse (CSG) enables farming activities in colder conditions and extends the harvest time. It is a crucial component of Chinese agricultural facilities. Rolling/unrolling the cover on the south roof is critical for modifying the greenhouse environment and improving the production efficiency of CSGs. However, since the structure of CSGs is constantly being updated, the existing winding equipment is inadequate because it causes deformation of covers. This study used as insulation blankets an example to proposed an adaptive control method of the rolling cover of CSGs. Deformation and force analysis showed that the fundamental reason for the deformation of the insulation blanket and the winding mechanism was the uneven load distribution in the length direction. A segmented winding mechanism and multi-motor scheme were used to reduce the deformation of the insulation blanket. A fuzzy proportional–integral–derivative (PID) control algorithm was developed to adjust the speed of the slave motors adaptively to roll/unroll the insulation blanket. The simulation results showed that the shorter winding mechanism resulted in lower torsional deformation. Experiments using a scale model demonstrated that the proposed double-motor scheme and fuzzy PID control method provided good performance for rolling/unrolling the insulation blanket. The double-motor scheme can significantly improve the operation efficiency of rolling insulation blankets. The running time of the double-motor scheme was 12.81% lower than that of the single-motor scheme, the average absolute position deviation was 41.95% lower, and the average absolute standard deviation was 44.51% lower. The proposed fuzzy PID control strategies enable the closed-loop control of the deceleration motor and the adjustment of the displacement deviation to reduce the deformation of the insulation blanket according to the feedback received from the slave motor. Unlike the conventional PID control, the fuzzy PID control automatically adjusts the system parameters to eliminate the system error. The control accuracy and stability of the fuzzy PID method have reached a good level, and the applicability is better than that of the conventional PID method. In the scale model experiments, the average position deviation remained within ±1 mm, and the average standard deviation was 1.32 mm. The proposed adaptive control method for the cover on the south roof of CSGs is highly suitable for practical applications. The study also provides application examples and theoretical references for the automation of rolling covers in greenhouses worldwide.

Speed Control of an Electrical Cable Extrusion Process Using Artificial Intelligence-Based Technique

Most cable manufacturing companies use Programmable Logic Controllers with conventional controllers to control line speed during cable extrusion. These traditional controllers have difficulties keeping the line speed constant, causing surface defects on the extruded cables and affecting the quality of the manufactured cables. To overcome these challenges, data on the causes of defects during cable manufacturing were collected from a cable manufacturing company in Ghana to ascertain the possible causes during cable manufacturing. Adaptive Neuro-Fuzzy Inference System (ANFIS) controller was designed to provide a constant line speed during the cable extrusion process. To ascertain its robustness, the ANFIS controller was compared to a conventional Proportional Integral Derivative controller and a Fuzzy Logic controller. The controllers were designed and simulated using MATLAB/Simulink software. The analysis of the collected data indicated that a break in insulation/ sheath was a frequently occurring defect during the cable manufacturing process due to improper line speed control of the machines used in the cable manufacturing process. Based on the results obtained from the various controllers, it was concluded that the ANFIS controller was robust in achieving stability regarding line speed variations.

Robust cascade control strategy for trajectory tracking to decouple disturbances using 3-degree-of-freedom inertial stabilized platform and its experimental validation

Inertial stabilized platform (ISP) is a vital component in modern tracking system which can segregate unsettling influence, keep altitude reference and rapidly realize identification and tracking of the target. For accurate tracking, the platform should be able to continuously point towards the target in presence of disturbances as well as parametric uncertainties associated with system modelling which normally degrade system performance. To tackle these challenges, different kinds of control strategies have been reported to achieve desired performance. However, there is an inevitable design trade-off between tracking and disturbance rejection capability as control performance decreases with the increase in system robustness. To alleviate this problem, the present work proposes a robust two-loop cascade control strategy where genetic algorithm (GA) optimized proportional–integral–derivative (PID) in the outer-loop facilitates precision tracking of the target. In the inner loop, lower-order robust controller designed by approximate generalized time moment (AGTM)/approximate generalized Markov parameter (AGMP) model matching approach is used to retain the robustness characteristics of [Formula: see text] controller to mitigate disturbances and model parametric uncertainties. System performance is verified through simulation as well as hardware implementation on a laboratory-fabricated three-axis gimbal platform prototype. The results show 48% improvement in tracking performance and 39.69% improvement in the rejection time of step disturbance in comparison with conventional PID control strategy.