AC servo motors are often used in industrial settings such as robotics, CNC machines, and packaging systems that need very precise control of speed and position. Control methods have evolved from simple linear methods such as PI and PID to more advanced methods like model predictive control (MPC), adaptive control, sliding mode control, and AI-based schemes. Each of these is meant des to deal with system nonlinearities and disturbances. But we still need industrial-ready technologies that balance dynamic performance under different load conditions, ease of use, and ease of deployment. Recent surveys have given detailed overviews of the newest servo control technologies1.

AC servomotors are used in many different fields to control both stationary and moving loads. The main requirement is that they can precisely control position, speed, and torque. Several commercially available control approaches have been developed for AC Servomotors operating under static and dynamic load conditions. However, each of these control strategies has advantages and disadvantages. Although numerous studies have addressed AC servomotor control, relatively little in-depth research has been conducted1,2. AC servo motors are preferred in non-linear and dynamic operating environments where an exact feedback response for the reference position is required. The feedback signals are essential for servomotors to respond quickly to on-off actions and maintain their steady state despite unexpected load changes. Various strategies may be used to resolve challenging computational problems driven by process requirements, and industries highly value AC Servomotors due to their precision in responding to dynamic load conditions. The vector control method used with an AC servo motor position Sensor provides less control and functions similarly to a DC motor system. The controller needs information about the magnetic pole location on the motor’s rotor when using vector control with AC servo motors. The controller can estimate both the magnetic pole position and the mechanical position, demonstrating the latter as a first step in the new control technology by employing the AC servo motor perfect position sensor, a less control technique.

The industry placed a high value on precise servo motor control because it reduced error rates in production procedures that demanded extreme precision. To control the servo motor’s rotation angle, PWM was used as the control signal, allowing the motors to operate at maximum torque and high speeds. Servo motors have built-in motor shaft position sensors that are already coupled to the control. In paper1, an experimental analysis of the performance of two different types of electric motors, namely permanent magnet synchronous motors (PMSMs) and AC servo motors (ACSMs), is presented for linear positioning applications. The study compares the two motors in terms of their dynamic response, accuracy, and stability under various operating conditions, such as different speeds and load profiles. The results can provide valuable insights into the selection and design of electric motors for linear positioning applications in various industrial and robotic systems. The study presented in3 investigated and compared the performance of three control strategies (PI, PD, and PID) for speed control of an AC servo motor through experimental work. The study aimed to evaluate the effectiveness of each controller in terms of steady-state error, rise time, overshoot, and settling time, and to identify the best controller for the given application3. The encoder is a device mounted on the AC servo motor shaft that detects the motor’s rotational position and speed, and sends this information to the PLC, which uses it to control the motor’s speed and position accurately. The proximity sensor detects the presence of a pipe and sends a signal to the PLC to initiate the cutting process. With these sensors and the precise control provided by the AC servo motor and PLC, the pipe-cutting machine can cut pipes to very accurate, consistent lengths, making it ideal for industrial applications where precision is critical1,2.

Based on this estimated disturbance, the controller adjusts the control action to counteract it and maintain accurate position control. The suggested SMCDE method has been successfully used to control the position of an AC servo motor. The experimental results show that it works well for getting accurate position control even when there are outside disturbances. In terms of accuracy and robustness, the SMCDE outperforms the traditional sliding mode controller, which requires the disturbance bound to be known in advance. In general, the suggested SMCDE method is a new way to get AC servo motors to move exactly where you want them to, even when external forces are at work. This can be very useful for many industrial uses, such as robotics, automation, and manufacturing. Identifying parameters and auto-tuning are two important steps in getting permanent-magnet AC servo motor drives to work well. This process involves identifying and tuning various parameters to achieve optimal motor control. Initially, the electrical parameters of the motor, such as resistance and inductance, which are necessary for tuning the current control loop, are determined. Subsequently, the torque constant and mechanical parameters are determined to tune the velocity and position control loops. The auto-tuning scheme employs various techniques to identify these parameters, eliminating the need for manual intervention and reducing the time and effort required for tuning, while ensuring optimal performance of the motor drive system. The adaptive iterative learning control strategy addresses trajectory tracking for tank gun servo systems with input dead zones and arbitrary initial states. To compensate for the nonzero initial error during the iterative learning controller construction, a time-varying boundary layer is created. The boundary layer is introduced to allow limited deviation from the desired trajectory, avoiding overshooting or instability while adapting to changing conditions. To account for uncertainties and dead-zone nonlinearity, a combination of neural network control and robust control is employed, in which a trained neural network approximates the system’s dynamics and provides feedback to the control system. Robust control employs a control law designed to provide stable performance in the presence of uncertainties and disturbances. The friction compensation scheme for adaptive backstopping control is presented. The friction force is described by the LuGre dynamic friction model, which assumes no uniform variations in friction force, whereas the AC motor control system is designed using a third-order linear dynamic model. Linear controllers are widely used in industrial applications because they perform well in simple situations. In complex situations, intelligent control systems are necessary. PID and fuzzy PID systems were compared, and a mathematical model was developed using MATLAB. They were proven to be important to the outcomes because the system achieved stability more quickly than PI & PID, according to the study technique3.

A servo motor is controlled to reach the desired position using various techniques to optimize performance4. These methods are categorized as either offline or online, and most identify mechanical and/or electrical parameters during tuning. Establishing all the controllers within a short time is crucial for the scheme to be acceptable in practice. Backlash is always present in the servo system’s transmission, causing a high error rate that degrades the control process. Therefore, a predictive model and a simulation of the automatic control process were developed to ensure system stability and prevent backlash-induced damage. Fuzzy controllers have been used to regulate the backlash motion of servo motors; however, the analysis is complicated by the problem’s nonlinearity. Using genetic algorithms, a set of solutions has been found through a trial-and-error method. Nonlinear electrical loads generate harmonic distortion, which in turn causes a backlash that drives the system out of the dead point. Therefore, a mathematical model and simulation of the electrical load and its distortion, as well as the importance of harmonic processing in reducing backlash, were developed. THD results were recorded before and after the placement of harmonics treatment, demonstrating their significance. However, conventional PID controllers may be limited when dealing with highly nonlinear or uncertain systems. To address these challenges, several studies have proposed enhanced control structures, such as augmented linear and nonlinear PD/PID controllers, which improve tracking accuracy, stability, and robustness5,6.

In7, the effectiveness of PI controllers in enhancing the control accuracy and stability of DC servo motors in industrial settings was demonstrated. Building on this foundation, the present work extends the investigation to AC servo systems, emphasizing real-time performance evaluation and comparison between PI and PID control approaches.

In8, a thorough comparison of servomotor control principles and their real-world applications across industries is presented, highlighting the pros and cons of standard methods such as PI and PID strategies in various situations. The results underscore the importance of selecting control methods based on how the system operates and the application’s needs. This aligns with the ongoing interest in improving servo performance for greater accuracy and stability. These kinds of insights give useful background for more experimental testing in real-time settings.

A thorough and current analysis of Model Predictive Control (MPC) techniques for AC motor drives is given in9, focusing on managing operating limits, improving transient performance, and adding AI-based improvements, while also pointing out both industrial readiness and computational problems when looking at uses in precision mechatronic systems, PMSM motors, and multi-objective current control. In10, a composite MPC architecture for PMSM servo systems is presented10, which includes an Extended State Observer (ESO) to account for current loop execution delays and demonstrates improvements such as. Rapid transient response, resilience against load disruptions, and decreased tracking error. An event-triggered finite-control-set MPC (FCS-MPC) architecture for PMSM drives is proposed in11, along with a sliding-mode observer (SMO) for estimating disturbances. The event-triggering technique maintains good control precision under dynamic disturbances while lowering the computational and communication burden.

To improve flexibility and resilience in nonlinear control tasks, several high-impact modern PID variants have been introduced: Sigmoid-based PID (SPID) uses a sigmoid function to dynamically adjust gains, thereby speeding up settling in voltage-regulator systems12. Sigmoid-based Fractional-Order PID (SFOPID) uses a dandelion optimizer to improve dynamic response by combining sigmoid gain scheduling with fractional calculus13. Data-driven BELBIC-PID incorporates the principles of brain emotional learning into a PID framework for robust control of MIMO systems14, and hardware Implementations of FOPID Controllers demonstrate practical fractional-order PID applications using FPGA and NI myRIO platforms15.

Although newer PID types are more flexible and better handle nonlinear problems, traditional PID controllers remain widely used in industry because they are reliable, easy to integrate with PLCs such as the Siemens S7-1200, require little computing power, and are standardized. In this study, their performance is further improved by GA, GWO, and HGAGWO for intelligent tuning of PID controllers. This combines industrial-strength with ease of use and intelligent tuning to deliver better dynamic performance while ensuring it is fully feasible for real-time PLC-based applications.

They investigate the performance accuracy of various control methods for AC servo motors, including PID controllers, using both theoretical and experimental approaches. The high-speed servomotor typically drives the transmission, completing the energy conversion by driving the load. This was the construction of the conventional PMSM servo system. Gears and ball screws are commonly employed in transmission mechanisms and inherently exhibit backlash nonlinearity16,17.

Recent advances in motor-drive control have increasingly focused on improving robustness and tracking accuracy under parameter uncertainties and external disturbances. Their results highlighted the importance of advanced control frameworks in maintaining stability and tracking precision in practical motor-drive applications. Nevertheless, such approaches often rely on sophisticated control structures and accurate system modeling, which may increase implementation complexity in industrial servo systems18. Senseless control techniques have attracted significant attention in PMSM drive systems due to their ability to reduce hardware requirements while maintaining reliable operation. Advanced observer-based approaches have achieved accurate rotor position estimation and improved low-speed performance, thereby enhancing efficiency and reliability in modern motor-control applications19. Machine-learning-based approaches have recently been introduced to improve prediction and control performance in PMSM drives. Differential neural network techniques have shown promising results in current prediction and adaptive learning under dynamic operating conditions, offering new opportunities for intelligent motor-drive control and performance enhancement20.

Recent studies have demonstrated the effectiveness of reinforcement learning-based model predictive control and adaptive fuzzy PID control in enhancing the dynamic performance, tracking accuracy, and robustness of motor-drive systems. These intelligent control strategies highlight the growing trend toward integrating advanced optimization and adaptive techniques to improve the performance of modern electromechanical systems21,22.

Table 1 summarizes the most important prior research on controlling servo motors, including the system components, the control methods used, and the areas where they can be applied. It shows how PI/PID controllers have evolved into MPC methods and intelligent PID variants, offering better performance and a wider range of applications. The proposed study utilizes a GA, GWO, and HGAGWO PID controller to integrate industrial reliability with improved real-time performance in PLC-based control systems.

Table 1 Comparative summary of previous studies on servo motor control strategies.

In automation, robotics, automotive, and industrial applications, backlash is a crucial component that influences the servo system’s dynamic tracking and steady-state performance. Backlash research is challenging complicate servo system control problems. Consequently, in-depth research on backlash nonlinearity for servo systems is required. Nonlinearities such as nonlinear friction, backlash, and external disturbances impede control performance. The transmission device’s backlash, which degrades transmission performance, was one such nonlinearity. Some cutting-edge control techniques have been developed to lessen the effects of blowback, such as robust control, adaptive control, and sliding mode control. Additionally, artificial intelligence technologies, such as neural networks and fuzzy logic control, have been utilized to compensate for nonlinearities due to their ability to approximate and learn nonlinear functions.

The following is a summary of this study’s primary contributions:

  • A comprehensive review of conventional and advanced control techniques for AC servo motors is presented, highlighting existing research gaps, particularly in practical PLC-based implementations and backlash-affected systems.

  • A HGAGWO approach is developed for optimal tuning of PID controller parameters, and its performance is benchmarked against standalone GA and GWO techniques.

  • The proposed optimized PID controllers are implemented on a Siemens S7-1200 PLC, ensuring industrial feasibility and real-time applicability of the control strategy.

  • Extensive experimental validation under real-time operating conditions is conducted, demonstrating significant improvements in dynamic response, reduced steady-state error, reduced overshoot, and quicker settling time when compared to traditional tuning techniques.

  • Backlash compensation techniques are integrated with the optimized control framework, leading to enhanced positioning accuracy and improved robustness in the presence of nonlinearities.

  • A combined simulation and hardware-based validation framework is established, effectively bridging the gap between theoretical design and practical industrial implementation.

The primary aim of this study is to conduct an in-depth analysis of AC servo motor control performance using PID control strategies under realistic operating conditions. In a real-time setting, the system uses a programmable logic controller (PLC), a human-machine interface (HMI), and a servo drive. An external encoder provides accurate feedback. The study focuses on regulating both the motor’s position and velocity, and assesses system performance under diverse dynamic and static load conditions. To assess how well a control system performs, we examine key performance indicators such as rise time, settling time, overshoot, and steady-state error. Through validation, the experimental outcomes are compared with simulation data generated in MATLAB. The study also suggests examining real-world problems, such as the backlash effect, and proposes a way to pay people to improve control accuracy and stability. The goal of this work is to connect theoretical modeling with real-world use by giving a practical evaluation of control methods for AC servo motor systems.