Fractional Order PID Research Articles

GWO based Robust Stabilization of DC Motor Fractional Order Speed Control System with Interval Coefficients

Robust stability analysis (RSA) is of significant concern for the robust behaviour of real-world control system applications. A stabilization strategy that assures stability and exhibits robust performance for a specified limit of system perturbations is necessary. This article presents an optimal robust stabilization method for a closed loop fractional order proportional integral derivative (FOPI^λD^µ) system involving DC motor with interval parametric uncertainty. To determine the optimum value of parameters for a FOPI^λD^µ controller to control the speed of a DC motor, Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Nelder-Mead (NM), Jaya and Whale Optimizer Algorithm (WOA) are applied with the same objective function involving ITAE criterion. FOPI^λD^µ offers two additional tuning parameters unlike a nominal PID controller and hence the former gives more flexibility in controller design than the latter in terms of transient response. The FOPID controller provides a faster closed-loop output augmented with improved robust properties of the system. Despite inherent non-linearities and time variation in system parameters, FOPI^λD^µ controllers depict enhanced performance. Using the concept of conformal mapping, robust stability analysis of fractional order polynomials is done with uncertain interval structure using Vertex and Edge theorem. Based on the value set, this paper demonstrates numerical and graphical optimal robust stability analysis of a system with variations observed in five parameters, considering the minimum argument root of the polynomial of the aforementioned closed-loop system.

Enhanced MPPT-Based Fractional-Order PID for PV Systems Using Aquila Optimizer

This paper proposes a controller to track the maximum power point (MPP) of a photovoltaic (PV) system using a fractional-order proportional integral derivative (FOPID) controller. The employed MPPT is operated based on a dp/dv feedback approach. The designed FOPID-MPPT method includes a differentiator of order (μ) and integrator of order (λ), meaning it is an extension of the conventional PID controller. FOPID has more flexibility and achieves dynamical tuning, which leads to an efficient control system. The contribution of our paper lies is optimizing FOPID-MPPT parameters using Aquila optimizer (AO). The obtained results with the proposed AO-based FOPID-MPPT are contrasted with those acquired with moth flame optimizer (MFO). The performance of our FOPID-MPPT controller with the conventional technique perturb and observe (P&O) and the classical PID controller is analyzed. In addition, a robustness test is used to assess the performance of the FOPID-MPPT controller under load variations, providing valuable insights into its practical applicability and robustness. The simulation results clearly prove the superiority and high performance of the proposed control system to track the MPP of PV systems.

Adaptive Active Disturbance Rejection Load Frequency Control for Power System with Renewable Energies Using the Lyapunov Reward-Based Twin Delayed Deep Deterministic Policy Gradient Algorithm

The substitution of renewable energy sources (RESs) for conventional fossil fuels in electricity generation is essential in addressing environmental pollution and resource depletion. However, the integration of RESs in the load frequency control (LFC) of power systems can have a negative impact on frequency deviation response, resulting in a decline in power quality. Moreover, load disturbances can also affect the stability of frequency deviation. Hence, this paper presents an LFC method that utilizes the Lyapunov reward-based twin delayed deep deterministic policy gradient (LTD3) algorithm to optimize the linear active disturbance rejection control (LADRC). With the advantages of being model-free and mitigating unknown disturbances, LADRC can regulate load disturbances and renewable energy deviations. Additionally, the LTD3 algorithm, based on the Lyapunov reward function, is employed to optimize controller parameters in real-time, resulting in enhanced control performance. Finally, the LADRC-LTD3 is evaluated using a power system containing two areas, comprising thermal, hydro, and gas power plants in each area, as well as RESs such as a noise-based wind turbine and photovoltaic (PV) system. A comparative analysis is conducted between the performance of the proposed controller and other control techniques, such as integral controller (IC), fractional-order proportional integral derivative (FOPID) controller, I-TD, ID-T, and TD3-optimized LADRC. The results indicate that the proposed method effectively addresses the LFC problem.

Disturbance rejection controller design based on nonlinear with fuzzy approximation technique for a tidal turbine system

In this work, a novel disturbance rejection controller based on a nonlinear fractional order proportional integral derivative with fuzzy (NLFOPIDF) approximation and an adaptive law technique for a 160 kW two horizontal axis parts tidal turbine model is proposed. The tidal turbine system encounters gigantic unknown uncertainties with external and internal disturbances induced by fatigue forces and non-uniform operative thrust and turbulence deviations caused by the effect of wind and wave movements. The main working purpose of the tidal turbine system is to extract the maximum power generated by obtaining and tracking the optimal turbine speed. To track the optimal speed turbine, two controller loops are synthesized for the tidal turbine dynamics (outer loop) and current dynamics in the q-axis component (inner loop). The stability and convergence are verified for the outer and inner loops using a candidate Lyapunov function. An approximation fuzzy function is proposed to estimate the nonlinear dynamics of the tidal turbine system, and an adaptive technique is applied to adapt the tidal turbine system against the variation in nonlinear dynamics. The results demonstrate that the NLFOPIDF controller is superior to other works in optimal power generation and optimal turbine speed tracking. Moreover, this controller can be used to achieve the maximum power coefficients to get the optimal power generation.

Optimal fractional SSSC auxiliary controller for power system low frequency oscillations damping improvement

This work presents a new controller design to improve inter-area low frequency electromechanical oscillations (LFEOs) damping and enhance the overall stability of the electrical network. The control strategy used the static synchronous series compensator (SSSC) based flexible AC transmission systems (FACTS) series-type device mainly performed for power flow and voltage regulation. Then, a fractional order proportional integral derivative (FOPID) is introduced as an auxiliary controller for the SSSC using the difference of rotor speed deviations of generators as input signal to improve the oscillations damping. Moreover, genetic algorithm (GA) is applied to seek for optimum controller gains. The proposed control approach is examined on two-area four-machine (2A4M) test system. The FOPID performance is compared with the integer order proportional integral derivative (PID). Obtained results show that the proposed SSSC-based FOPID controller achieves high performance for enhancement of inter-area low frequency oscillations damping.

Fractional order controller design via gazelle optimizer for efficient speed regulation of micromotors

In this paper, the design of an efficient fractional-order proportional-integral-derivative (FOPID) controller, tailored specifically for the regulation of micro direct current (DC) motors, is explored. A fresh approach is introduced using the gazelle optimization algorithm (GOA), a cutting-edge method set to manage the speed control of these small but vital motors. With the adoption of GOA, fine-tuning the parameters of the FOPID controller is aimed. This is achieved by employing a time-based performance metrics-based cost function as the guiding compass. The GOA is implemented to discover the optimal controller settings for the attainment of peak performance. Through thorough simulations and careful statistical analysis, the worth of GOA-based FOPID controller is demonstrated. Not only are top-notch cost function values achieved, but excellence across various time domain-based performance metrics is also excelled in. The results suggest that the GOA proves to be efficient in fine-tuning of FOPID controller parameters. A comprehensive analysis in the time domain further solidifies the superiority of GOA-based FOPID-controlled micro-DC motor system. Outperformance is observed when compared to alternative algorithms employing different controllers, such as an advanced hybrid stochastic fractal search-based FOPID controller, a grey wolf optimizer-based FOPID controller, a slime mold algorithm-based PID controller, an enhanced arithmetic Harris hawks optimization-based PID controller, and a hybrid atom search optimization and simulated annealing-based PID controller. In essence, the results highlight the potential of the GOA as a powerful tool poised to reshape the optimization of FOPID controllers for micro-DC motor speed regulation.

Neural Fractional Order PID Controllers Design for 2-Link Rigid Robot Manipulator

The robotic manipulator is considered one of the complex systems that include multi-input, multi-output, non-linearity, and highly coupled. The uncertainty in the parameters and external disturbances have a negative influence on the performance of the system. Therefore, the controllers that will be designed for these systems must be able to deal with these complexities and difficulties. The Proportional, Integral, and Derivative (PID) controller is known to be simple and well robust, while the neural network has a solid ability to map complex functions. In this paper, we propose six control structures by combining the benefits of PID controller with integer and fractional order and the benefits of neural networks to produce hybrid controllers for a 2-Link Rigid Robot Manipulator (2-LRRM) handling with the problem of trajectory tracking. The Gorilla Forces Troops Optimization algorithm (GTO) was used to tune the parameters of the proposed controller schemes to minimize the Integral of Time Square Error (ITSE). In addition, the robustness of the performance of the suggested control systems is tested by altering the initial position, external disturbances and parameters and carried out using MATLAB. The best performance of the proposed controllers was the Neural Network Fractional Order Proportional Integral Derivative Controller (NNFOPID).

Adaptive Impedance Control for Force Tracking in Manipulators Based on Fractional-Order PID

Force tracking control in robot arms has been widely used in many industrial applications, particularly in tasks involving end effectors and environmental contact, such as grinding, polishing, and other similar operations. However, these environments are not always precisely known. In order to address the force tracking control problem in unknown environments, this paper proposes a fractional-order PID adaptive impedance control strategy based on traditional impedance control. The unknown environmental information is estimated online using the adaptive impedance control algorithm, and the estimated parameters are used to generate reference trajectories to reduce force tracking errors. Fractional-order PID control is then introduced into the system to improve the control performance of the system model, and the theoretical proof of strategy stability is conducted. Finally, a comparison of four strategies was conducted through simulations: traditional impedance control, adaptive hybrid impedance control, adaptive variable impedance control, and the fractional-order PID impedance control proposed in this paper. The simulation results demonstrate that the strategy proposed in this paper exhibits robustness, virtually eliminates overshoot, and enhances response speed. In contrast, both adaptive hybrid impedance control and adaptive variable impedance control exhibit approximately 30% to 45% overshoot during interactions with the environment. Furthermore, in terms of force tracking error, the proposed strategy in this paper outperforms the above two strategies by approximately 29% to 60%, achieving excellent force tracking control performance.

Enhancing time-domain performance of vehicle cruise control system by using a multi-strategy improved RUN optimizer

This paper addresses the pressing concern of traffic safety by focusing on the optimization of vehicle cruise control systems. While traditional control techniques have been widely employed, their design procedures can be time-consuming and suboptimal. To overcome these limitations, metaheuristic algorithms have been introduced as promising solutions for complex optimization problems. In this study, an improved Runge Kutta optimizer (IRUN) is developed and applied to enhance the control performance of a real PID plus second-order derivative (RPIDD2) controller for vehicle cruise control systems. The IRUN optimizer incorporates advanced strategies such as quadratic interpolation, Laplacian segment mutation, Levy flight, and information-sharing-based local search mechanisms. By integrating these strategies, the IRUN algorithm demonstrates enhanced optimization capabilities, making it well-suited for tuning the controller. The proposed approach utilizes a master–slave system, where the ideal reference model sets the desired response and the RPIDD2 controller adjusts its parameters accordingly. The integral of the square error is employed as the objective function to evaluate the control system's performance. Statistical analyses, convergence analyses, and stability evaluations and robustness analysis are performed to demonstrate the effectiveness of the IRUN-based RPIDD2 controller. Comparative studies are conducted against established approaches using PID, fractional-order PID (FOPID), and RPIDD2 controllers, showcasing the superiority and effectiveness of the proposed approach. Overall, this paper presents a comprehensive study on enhancing the time-domain performance and stability of vehicle cruise control systems, providing significant improvements in control accuracy and efficiency. The subsequent sections delve into the proposed approach, experimental setup, and obtained results, further emphasizing the significance and potential impact of this research.

New Cascaded 1+PII2D/FOPID Load Frequency Controller for Modern Power Grids including Superconducting Magnetic Energy Storage and Renewable Energy

Having continuous decrease in inertia and being sensitive to load/generation variation are considered crucial challenging problems for modern power grids. The main cause of these problems is the increased penetration capacities of renewables. An unbalanced load with generation power largely affects grids’ frequency and voltage profiles. Load frequency control (LFC) mechanisms are extensively presented to solve these problems. In the literature, LFC methods are still lacking in dealing with system uncertainty, parameter variation, structure changes, and/or disturbance rejection. Therefore, this paper proposes an improved LFC methodology using the hybrid one plus proportional integral double-integral derivative (1+PII2D) cascaded with fractional order proportional-integral-derivative (FOPID), namely, the proposed 1+PII2D/FOPID controller. The contribution of superconducting magnetic energy storage devices (SMES) is considered in the proposed design, also considering hybrid high-voltage DC and AC transmission lines (hybrid HVDC/HVAC). An optimized design of proposed 1+PII2D/FOPID controller is proposed using a new application of the recently presented powerful artificial rabbits optimizers (ARO) algorithm. Various performance comparisons, system changes, parameter uncertainties, and load/generation profiles and changes are considered in the proposed case study. The results proved superior regulation of frequency using proposed 1+PII2D/FOPID control and the ARO optimum parameters.

Disturbance rejection controller design based on adaptive nonlinear FOPID controller and chaotic WOA with a neuro-fuzzy approximation for URV robot

This paper presents an adaptive neuro-fuzzy-based nonlinear fractional order proportional integral derivative (ANF-NLFOPID) controller to stabilize six degrees of freedom (6-DOF) underwater robotic vehicle (URV). Generally, the stability of the URV system is jammed due to internal and external variations related to unknown uncertainties and disturbances in the URV model, which lead to trajectory tracking problems. Thus, to solve unknown uncertainties and disturbances problems, an ANF network is proposed to estimate unknown uncertainties and disturbances. At the same time, an NLFOPID controller is proposed to minimize the error effect generated by the variation of the URV model parameters. Considering the unknown and unpredictable behavior of the URV system in the presence of uncertainties and disturbances, the ANF-NLFOPID controller parameters are tuned continuously using an improved whale optimization algorithm based on chaotic theory (IWOA-CT). The main working principle of the IWOA-CT is to solve the local minimum (optimum) problem and achieve the global optimum points to deliver the best parameter values for the ANF-NLFOPID controller. A simulation and experimental results are conducted to measure the performance of the ANF-NLFOPID controller compared to other existing works. MATLAB software is used to simulate four trajectory tracking cases, where the first case proved the superiority of the ANF-NLFOPID controller by 52.559%, 70.028%, and 73.83%, the second case shows that the ANF-NLFOPID improves the trajectory tracking performance by 34.399%, 46.094%, and 59.143%, the third case demonstrates the effectiveness of the ANF-NLFOPID controller by 41.922%, 51.205%, and 62.252%, and the fourth case shows that the ANF-NLFOPID enhance the overall trajectory tracking efficiency by 29.356%, 38.413%, and 44.795% compared to adaptive neural network-based nonlinear FOPID (ANNFOPID), adaptive fuzzy-based nonlinear PID (AFNLPID), and disturbance rejection based on adaptive neural network (DR-ANN) controllers respectively. In the end, the experimental results were conducted using a real-world URV model and showed that the ANF-NLFOPID controller presents superior in solving trajectory tracking problems compared to the ANNFOPID controller.

Quadruple spherical tank systems with automatic level control applications using fuzzy deep neural sliding mode FOPID controller

The premier goal of this research is to develop the Fuzzy Deep Neural Sliding Mode Fractional Order Proportional Integral Derivative (FDN-SM-FOPID) controller system for controlling liquid in quadruple spherical tank systems. This is used in non-linear spherical systems to control the level of liquid in real time. These models' dynamics allow for a more accurate identification of the spherical tank system that generates control signals from liquid samples obtained at reference levels. However, because the system is susceptible to outside disturbances, error minimization is not done. Therefore, it requires the addition of a special controller to lessen this flaw. The suggested Deep Neural Fuzzy model's six-layered network is optimized using the back-propagation method. As a result, the system's efficient training reduces offset model errors, steady state errors, and unmeasured disturbances. The liquid level is maintained and controlled by this neural intelligence system, which meets the necessary design requirements such as no overshoot, time constant, less settling and rise time, which is used in various platforms. The FOMCON toolbox in MATLAB software is used for research simulation work. The chemical industry, wastewater treatment, the aerospace industry, and the pharmaceutical industry have all employed the suggested quadruple spherical tank system to test its practicality. The experimental and simulation results are demonstrated by a real-time liquid control experimental setup.

TSKARNA-Norm Adaption Based NLMS with Optimized Fractional Order PID Controller Gains for Voltage Power Quality

TSKARNA-Norm Adaption Based NLMS with Optimized Fractional Order PID Controller Gains for Voltage Power Quality

Hybrid Deep Neural Network for Data Driven Missile Guidance with Maneuvering Target

Missile guidance, owing to highly complex and non-linear relative movement between the missile and its target, is a challenging problem. This is further aggravated in case of a maneuvering target which changes its own flight path while attempting to escape the incoming missile. In this study, to achieve computationally superior and accurate missile guidance, a deep learning is employed to propose a self-tuning technique for a fractional-order proportional integral derivative (FOPID) controller of a radar-guided missile chasing an intelligently maneuvering target. A multi-layer two-dimensional architecture is proposed for a deep neural network that combines the prediction feature of recurrent neural networks and estimation feature of feed-forward artificial neural networks. The proposed deep learning based missile guidance scheme is non-intrusive, data-based, and model-free wherein the parameters are optimized on-the-run while predicting the target’s maneuvering tactics to correct for processing time and loop delays of the system. Using deep learning for online optimization with minimal computational burden is the core feature of the proposed technique. Dual-core parallel simulations of missile-target dynamics and the control system were performed to demonstrate superiority of the proposed scheme in feasibility, adaptability, and the ability to effectively minimize the miss-distance in comparison with traditional and neural offline-tuned PID and FOPID based techniques. Compared to state-of-the-art offline-tuned neural control, the miss-distance was reduced by 68.42% for randomly maneuvering targets. Furthermore, a minimum miss-distance of 0.97 m was achieved for intelligently maneuvering targets for which the state-of-the-art method failed to hit the target. Overall, the proposed technique offers a novel approach for addressing the challenges of missile guidance in a computationally efficient and effective manner.

Modern PID/FOPID controllers for frequency regulation of interconnected power system by considering different cost functions

This article presents frequency regulation of an interconnected three-area power system (Thermal + Wind + Hydro). Fractional Order PID (FOPID) and Proportional-Integral-Derivative (PID) controllers are applied as subsidiary regulators to control the electrical power interconnected system at the time of sudden load variation. To accomplish this study, Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Sine Cosine Inspired Algorithm (SCIA) and Atom Search Inspired Algorithm (ASIA) are implemented to optimize the secondary regulators' gains (PID and FOPID) by considering various cost functions such as Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), Integral Square Error (ISE), and Integral Time Square Error (ITSE). Performance analysis in this work is conducted using various cost functions based on GA, GWO, SCIA and ASIA. The comparative analysis of the attained results reveals that GWO-PID and ASIA–PID settle at (83.83 s) and (30.31 s), respectively and ASIA-FOPID at (25.12 s). The controllers based on ITSE as a cost function outperform the comptrollers with other cost functions (ISE, IAE and ITAE). In addition, the ISE-based GA–PID and SCIA–PID settle at (113.92 s) and (35.1 s), respectively and SCIA-FOPID at (24.78 s). The ISE-based regulators yield improved response equated to other cost functions (ITSE, IAE and ITAE) optimized controllers. The robustness test also is carried out to validate the effectiveness of the proposed optimization techniques by changing the system parameters within ± 25% and ± 50% from their nominal values as well as changing the load pattern.

Designing a Secure Mechanism for Image Transferring System Based on Uncertain Fractional Order Chaotic Systems and NLFPID Sliding Mode Controller

A control method for the robust synchronization of a class of chaotic systems with unknown time delay, unknown uncertainty, and unknown disturbance is presented. The robust controller was designed using a nonlinear fractional order PID sliding surface. The Lyapunov method was used to determine the update laws, prove the stability of the proposed mechanism, and guarantee the convergence of the synchronization errors to zero. The simulation was performed using MATLAB software to evaluate the performance of the proposed mechanism, and the results showed that it was efficient. Finally, the proposed method was combined with a secure communication application to encrypt images, and the results obtained were favorable regarding the standard criteria of correlation, NPCR, PSNR, and information entropy.

Performance analysis of buck converter with fractional PID controller using hybrid technique

In this paper, a hybrid technique is proposed for analyzing the performance of buck converters with fractional-order proportional integral derivative (FOPID) controllers. The hybrid approach is a combination of Capuchin Search Algorithm (CapSA) and Golden Jackal Optimization (GJO). The update behavior of Golden Jackal Optimization2 is enhanced with Capuchin Search Algorithm (CapSA) therefore it is called the improved GJO (IGJO) technique. The power converters are hard to manage based on their nonlinear nature and therefore the search for smart and effectual controllers is ongoing and continual. In current years, fractional order controllers have shown greater efficiency in power electronic systems. The IGJO technique is used to establish the optimum design of a fractional-order proportional integral derivative (PID) controller for the buck converter. The FOPID controller parameters are considered to diminish several performance metrics, with a particular focus on the Integral Squared Error (ISE). The proposed method is performed in the MATLAB, and its execution is analyzed by using the existing methods. From the simulation result, the proposed method reduces the error more effectively than existing methods.

A cascaded NPID/PI scheme for the regulation of stack voltage in proton exchange membrane fuel cell

In this work, a higher-order Proton Exchange Membrane (PEM) Fuel Cell system is controlled using a cascaded control approach. The primary and secondary controllers, NPID/PI, are non-linear proportional-integral-derivative and proportional-integral, respectively. The suggested cascade approach controls the stack voltage by adjusting the air compressor voltage to maintain the oxygen excess ratio value within limits. Two additional cascade control structures, PID/PI and FOPID/FOPI (fractional order PID and fractional order PI), are also created for a fair comparison. A genetic algorithm is used to determine the controller's optimal parameters by minimising the time integral absolute error of the primary controller. Results show that NPID/PI control structure achieves the minimum value of settling time and overshot (0.9514 s, 0.021%) compared to FOPID/PI (1.8980 s, 0.036%) and PID/PI (3.2308 s, 0.092%), respectively. Finally, it can be concluded from the result of setpoint tracking, disturbance rejection, and noise suppression that the proposed controller is efficient and robust compared to other designed controllers.

Tuning of controller parameters using Pythagorean fuzzy similarity measure for stable and time delayed unstable plants.

This paper proposes a tuning method based on the Pythagorean fuzzy similarity measure and multi-criteria decision-making to determine the most suitable controller parameters for Fractional-order Proportional Integral Derivative (FOPID) and Integer-order Proportional Integral-Proportional Derivative (PI-PD) controllers. Due to the power of the Pythagorean fuzzy approach to evaluate a phenomenon with two memberships known as membership and non-membership, a multi-objective cost function based on the Pythagorean similarity measure is defined. The transient and steady-state properties of the system output were used for the multi-objective cost function. Thus, the determination of the controller parameters was considered a multi-criteria decision-making problem. Ant colony optimization for continuous domains (ACOR) and artificial bee colony (ABC) optimization are utilized to minimize multi-objective cost functions. The proposed method in the study was applied to three different systems: a second-order non-minimum phase stable system, a first-order unstable system with time delay, and a fractional-order unstable system with time delay, to validate its effectiveness. The cost function utilized in the proposed method is compared with the performance measures widely used in the literature based on the integral of the error, such as IAE (Integral Absolute Error), ITAE (Integral Time Absolute Error), ISE (Integral Square Error), and ITSE (Integral Time Square Error). The proposed method provides a more effective control performance by improving the system response characteristics compared to other cost functions. With the proposed method, the undershoot rate could be significantly reduced in the non-minimum phase system. In the other two systems, significant improvements were achieved compared to other methods by reducing the overshoot rate and oscillation. The proposed method does not require knowing the mathematical model of the system and offers a solution that does not require complex calculations. The proposed method can be used alone. Or it can be used as a second and fine-tuning method after a tuning process.

Revolutionizing Vehicle Cruise Control: An Elite Opposition-Based Pattern Search Mechanism Augmented INFO Algorithm for Enhanced Controller Design

This paper presents a groundbreaking approach to enhance the performance of a vehicle cruise control system—a crucial aspect of road safety. The work offers two key contributions. Firstly, a state-of-the-art metaheuristic algorithm is proposed by augmenting the performance of the weighted mean of vectors (INFO) algorithm using pattern search and elite opposition-based learning mechanisms. The resulting boosted INFO (b-INFO) algorithm surpasses the original INFO, marine predators, and gravitational search algorithms in terms of performance on benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions. Secondly, a novel proportional, fractional order integral, derivative plus double derivative with filter (P{I}^{lambda }DN{D}^{2}{N}^{2}) controller is proposed as a more efficient control structure for vehicle cruise control systems. An objective function is utilized to determine the optimal values for the controller parameters, and the proposed method's performance is compared against a range of recent approaches. Results demonstrate that the b-INFO algorithm-based P{I}^{lambda }DN{D}^{2}{N}^{2} controller is the most efficient and superior method for controlling a vehicle cruise control system. Moreover, this work represents the first report of a P{I}^{lambda }DN{D}^{2}{N}^{2} controller’s implementation for vehicle cruise control systems, underscoring the novelty and significance of this research. The proposed method's exceptional ability is further confirmed by comparisons with the genetic algorithm, ant lion optimizer, atom search optimizer, arithmetic optimization algorithm, slime mold algorithm, Lévy flight distribution algorithm, manta ray foraging optimization, and hunger games search-based proportional–integral–derivative (PID), along with Harris hawks optimization-based PID and fractional order PID controllers. This work marks a remarkable milestone toward safer and more efficient vehicle cruise control systems.