Controller Design Research Articles

ABSTRACT Renewable energy is widely used due to its low cost, availability, and minimal environmental impact. Power electronic converters enable energy conversion. Among them, the boost converter is commonly used in photovoltaic systems to increase solar panel voltage, thanks to its simple and cost-effective design. The boost converter model has uncertainty, and its parameters are considered to vary over time. Also, the step-up converter is a non-minimum phase system. Therefore, the controller design must be taken seriously, considering such reductions due to zeros on the right, as well as the impact of both uncertainty and parameter variation on the stability of the closed-loop system. Here, fractional-order control has been proposed to regulate the output voltage in a cascade structure. FOC, based on numerous research studies, consistently shows superior results against noise, disturbance, parametric changes, and uncertainty. Also, FOC has better results for non-minimum phase systems. The cascade controller structure controls both the current and the voltage. This structure makes the output voltage more stable. The Antlion algorithm (ALO) has been used to optimise the FOC’s parameters. This algorithm has the ability for global optimisation, and the adjustment parameters are removed in this algorithm, which is one of its advantages.

ABSTRACT This work focuses on the trajectory tracking control of robot manipulators subject to model uncertainties and unknown additive disturbances. The controller design makes use of a self‐adjusting adaptive fuzzy logic‐based term, fused with a robust integral of the sign of the error feedback. In the proposed adaptive fuzzy logic framework, means and variances of the membership functions are updated dynamically during each iteration, allowing for a more precise estimation of the parametric uncertainties. The stability of the closed‐loop system and the convergence properties of the states are established via Lyapunov‐based arguments, where asymptotic stability of the joint tracking error is ensured. Numerical simulations have been conducted to further support the theoretical findings.

ABSTRACT The primary objective of this study is to develop an adaptive controller for constrained nonlinear systems to achieve tracking control effectively in finite time. The research is conducted in three systematic steps: First, a novel practical finite‐time stability (PFTS) criterion is established, providing the theoretical foundation for the design of controllers. Second, the original system with extended state constraints is converted into an unconstrained one via nonlinear mapping (NM). Third, building upon the established PFTS criterion and the command‐filtered backstepping technique, an adaptive finite‐time control scheme is proposed. This innovative control algorithm ensures superior tracking performance while strictly adhering to extended full‐state constraints. Additionally, all system signals maintain PFTS. Both stability analysis and simulations are conducted to validate the effectiveness of the proposed control strategy.

This study addresses developing systematic guidelines for the design of concentration control in the oxidation of benzene to maleic anhydride within a tubular reactor. The influence of step size selection and temperature sensor location on the tuning and performance of a PI/P cascade control system applied to the oxidation process was evaluated. The reactor’s dynamic behavior was analyzed using numerical simulations based on the solution of the Fortran mathematical model. Sensor positions and multiple step sizes (from +10% to −10%) were analyzed to characterize reactor dynamics and optimize control parameters. The results show that a controller design corresponding to a −9% step in the jacket temperature offered the best performance, ensuring process stability and selectivity. In contrast, step changes between +10% and −8% caused temperature deviations beyond safe limits. Since maleic anhydride is an essential precursor in the production of resins, plastics, lubricants, and pharmaceutical intermediates, optimizing the efficiency and safety of its production represents a significant benefit to the global chemical industry.

Abstract This paper presents the optimization of hybrid energy storage system for an electric vehicle, by using particle swarm optimization and genetic algorithm techniques including the design of conventional controller. It utilizes the steady-state filtered power as the reference output power of the battery. To regulate the steady-state current output of the battery, the output power of the ultracapacitor is adjusted dynamically with help of a proportional-integral-derivative controller, such that the power difference controlled structure is obtained. The PID controller parameters are optimized through the particle swarm optimization algorithm, and genetic algorithm. The output is compared with the federal test procedure drive cycle, and the optimized power output and state of charge of the battery is obtained. In this paper the result obtained shows that the SoC of battery is increased by 30.12% with the help of PSO-PID and 0.1% with the help of GA-PID and almost similar results were obtained using conventional PID controller. The battery power ratio in the total power demand is 0.6667 with PSO-PID, 0.8871 with GA-PID and 0.9109 with conventional PID controller. The results obtained shows that the proposed control strategy PSO-PID is capable of eliminating the deviation in power quickly and accordingly achieving the best suited global optimization of EV. In comparison with the optimized PID strategy the result obtained by the proposed strategy shows improvement in the energy consumption and battery life of EV.

The increasing adoption of high-performance DC motor control in embedded systems has driven the development of cost-effective solutions that extend beyond traditional software-based optimization techniques. This work presents a refined hardware-centric approach implementing real-time particle swarm optimization (PSO) directly executed on STM32 microcontroller for DC motor speed control, departing from conventional simulation-based parameter-tuning methods. Novel hardware-optimized composition of an interval type-2 fuzzy logic controller (FLC) and a PID controller is developed, designed for resource-constrained embedded systems and accounting for processing delays, memory limitations, and real-time execution constraints typically overlooked in non-experimental studies. The hardware-in-the-loop implementation enables real-time parameter optimization while managing actual system uncertainties in controlling DC micro-motors. Comprehensive experimental validation against conventional PI, PID, and PIDF controllers, all optimized using the same embedded PSO methodology, reveals that the proposed FT2-PID controller achieves superior performance with 28.3% and 56.7% faster settling times compared to PIDF and PI controllers, respectively, with significantly lower overshoot at higher reference speeds. The proposed hardware-oriented methodology bridges the critical gap between theoretical controller design and practical embedded implementation, providing detailed analysis of hardware–software co-design trade-offs through experimental testing that uncovers constraints of the low-cost microcontroller platform.

Navigating the Physics of Virtual Power Plants: The Role of Devices, Controller Design, and the Grid

A Vector Control-based Design of Multi-port Interline DC Power Flow Controller for HVDC System

Stability region patterns in dominant pole placement based PID controller design for SOPTD systems

In this paper, we investigate the stability of a triangularly coupled triple-loop thermosyphon system with momentum and heat exchange at the coupling point as well as the existence of disturbances. The controller consists of a single, local-state feedback. From the stability analysis, we obtain explicit bounds on the feedback gains, which depend on the Rayleigh numbers and the momentum coupling parameter, but independent of the thermal coupling parameter. The existence of the stability bounds allows us to design decentralized adaptive controllers to automatically search for the feasible gains when the system parameters are unknown. In the case of existing disturbances in the system, we approximate the disturbances via an extended-state observer for the purpose of disturbance rejection. Numerical results are given to demonstrate the performance of the proposed decentralized disturbance rejection controller design.

This paper presents an experimental longitudinal mode control approach for a biomimetic underwater robot. Input–output models for surge velocity and pitch angle were derived through experiments, considering the fish robot body with servo motors and control pins as a single system to solve the problem of fish robots, which are complex and nonlinear, and also contain uncertainty. Closed-loop control systems were designed using PID controllers based on these models, and their performance was verified through simulations and experiments. Surge velocity and pitch angle response models were developed for nominal surge velocities of 0.2 m/s and 0.4 m/s. The surge velocity response models exhibited high agreement rates of 75.25% and 81.23% between the identified linear models and experimental results at 0.2 m/s and 0.4 m/s, respectively. In contrast, the pitch angle response model showed lower agreement rates of 68.02% and 34.24% between the identified linear model and experimental results at 0.2 m/s and 0.4 m/s, respectively. The gain margin and phase margin of the surge controller were 28.7 dB and 116°, and 37.2 dB and 70.6°, respectively. For the pitch response model, the low-frequency gain of the transfer function was very small at −31 dB when the nominal surge velocity was 0.2 m/s; this gain increased to −8 dB when the nominal surge velocity was increased to 0.4 m/s. It was observed that the initial value responses of the pitch angle converged to 0° with some oscillations in both the simulations and experiments. Therefore, it is believed that by identifying a linear model and subsequently designing a controller based on it, the surge velocity of the fish robot can be effectively controlled while stabilizing its pitch angle.

A deep reinforcement learning-based controller design framework for Lipschitz continuous nonlinear systems

Boundary synchronization controller design for a network of Euler–Bernoulli beam equations with both ends free

Accurate trajectory tracking is crucial for fixed-wing unmanned aerial vehicles (UAVs) in executing diverse missions. However, the inherent strong nonlinearities, parametric uncertainties, and external disturbances in the UAV model present significant challenges for controller design. To address these challenges, this paper proposes a robust adaptive control strategy based on the backstepping technique. The proposed strategy effectively addresses a class of uncertainties with norm bounds that are unknown and state-dependent. An adaptive law is constructed to estimate the unknown parameters online, thereby enabling compensation for the effects of these uncertainties. Furthermore, to mitigate chattering, the controller is modified to generate smooth control inputs, ensuring that the steady-state tracking error is ultimately bounded and converges to an arbitrarily small neighborhood of zero. Simulation results demonstrate that, under realistic flight control sampling frequencies, the proposed controller achieves accurate trajectory tracking and eliminates the chattering phenomenon.

A sliding mode predictive control (SMPC) scheme integrated with an extreme learning machine (ELM) disturbance observer is proposed for the trajectory tracking of a flexible air-breathing hypersonic vehicle (FAHV). To streamline the controller design, the longitudinal model is decoupled into a velocity subsystem and an altitude subsystem. For the velocity subsystem, a proportional-integral sliding mode surface is designed, and the control law is derived by minimizing a cost function that weights the predicted sliding mode surface and the control input. For the altitude subsystem, a backstepping control framework is adopted, with the SMPC strategy embedded in each step. Multi-source disturbances are modeled as composite additive disturbances, and an ELM-based neural network observer is constructed for their real-time estimation and compensation, thereby enhancing system robustness. The semi-globally uniformly ultimately bounded (SGUUB) stability of the closed-loop system is rigorously proven using Lyapunov stability theory. Simulation results demonstrate the comprehensive superiority of the proposed method: it achieves reductions in Root Mean Square Error (RMSE) of 99.60% and 99.22% for velocity and altitude tracking, respectively, compared to Prescribed Performance Control with Backstepping Control (PPCBSC), and reductions of 98.48% and 97.12% relative to Terminal Sliding Mode Control (TSMC). Under parameter uncertainties, the developed ELM observer outperforms RBF-based observer and Extended State Observer (ESO) by significantly reducing tracking errors. These findings validate the high precision and strong robustness of the proposed approach.

Robust optimal fractional-order proportional-integral and proportional-derivative controller design for integrating systems with time delays: Real-time application to quadrotors.

ABSTRACT In this paper, the problem of robust adaptive iterative learning control (RAILC) with switching ‐modifications is addressed for a class of nonlinear systems with unrepeatable uncertainties and initial errors. Different from the published learning algorithms, switching learning is introduced to ensure the robustness of the parametric estimation. The causal contradiction caused by the switching ‐modification is solved by applying an open‐loop learning law. Sufficient conditions for initial rectifying functions (IRFs) are given for constructing prespecified tracking error trajectories, which are adopted in RAILC algorithms to cope with initial errors. S‐class function with a series convergence sequence is utilized in the controller design to guarantee the perfect tracking performance. Simulations are given to compare the switching‐ and the saturated learning that demonstrate the effectiveness of the proposed learning control scheme.

Abstract This paper presents a novel decentralized Proportional- Integral- Derivative (PID) controller design approach for Two input Two Output (TITO) systems based on Grey Wolf Optimization (GWO) method with Integral Time-weighted Absolute Error (ITAE) as an objective function. The control of TITO systems is a challenging task due to the interaction between control loops, requiring efficient and robust strategies for optimal performance. To enhance the tuning process, the GWO algorithm is employed, leveraging its simplicity and effective convergence properties. The proposed method ensures improved dynamic performance and robustness. In this paper, a case study of an Industrial Scale Polymerization (ISP) reactor is analyzed to evaluate the suggested controller. The performance of the proposed controller is assessed through both simulation and experimental results. The results from the simulation indicate that the PID controller tuned using GWO exhibits improved performance by reducing loop interactions. Key performance metrics, including overshoot, settling time, and control effort, are evaluated and compared against traditional PID tuning methods like the characteristic ratio assignment method and the Firefly Algorithm. Experimental results demonstrate the successful implementation of a decentralized PID controller for the coupled tank system.

Abstract This paper proposed a novel fractional-order filter-PID controller design approach for Second-Order Plus Time Delay (SOPTD) processes within an Internal Model Control (IMC) based Smith predictor framework, using robustness criterion, i.e., maximum sensitivity (Ms) as a design specification. The proposed controller design is systematically structured into a fractional-order IMC filter and an integer-order PID controller, with a systematic optimized methodology for tuning the fractional filter parameters based on a predefined maximum sensitivity. Adding fractional-order parameters extends the range of accomplishable control dynamics beyond those of traditional integer-filter-PID controllers. The proposed approach enhances flexibility in tuning, performance and robustness by employing fractional-order IMC filter with conventional integer-order counterparts. The proposed approach’s superiority is demonstrated by comparing evaluations with the literature, depicting that it significantly minimizes control effort, reduces the Integral of Absolute Error, and fast settling time. Additionally, robust analysis of parameter uncertainties shows that the controller can maintain the required performance even in significant process fluctuations. The proposed method is experimentally validated on a non-interacting liquid level system.

Abstract Current research on active flutter suppression considering time delays tends to focus on fixed time delays. To address situations where the control loop may experience time-varying delays with uncertainty, a time-varying-delay Active Disturbance Rejection Control (TVD-ADRC) is proposed. First, a parameterized unsteady aerodynamic reduced-order model (ROM) based on a long short-term memory network is introduced into the aeroservoelastic modeling. This model is applied to predict unsteady aerodynamic forces and aeroservoelastic (ASE) behaviors across a wide range of Mach numbers. Its effectiveness in capturing the characteristics of unsteady aerodynamics is validated through comparisons with the high-fidelity computational fluid dynamics (CFD) simulations. Second, the proposed method integrates ADRC with a delayed input and a time-delay identification module in the controller design. Specifically, the time-varying delay is identified using the cross-correlation function method with a moving window, and this method dynamically updates the time-delay compensation module. Additionally, a genetic algorithm is employed to optimize controller parameters, and the integral of the time-weighted absolute error is selected as the performance evaluation index for the control system. Finally, a three-degree-of-freedom aeroservoelastic system of an airfoil with a trailing-edge control surface is studied for flutter suppression. Flutter control under uncertain time-varying delays during flutter occurrence is investigated, and the impact of the magnitude of the time delay on the effectiveness of the flutter control is analyzed. Simulation results indicate that the proposed TVD-ADRC controller could effectively suppress the aeroelastic instabilities across a wide range of Mach numbers and effectively counteract the negative effects of time-varying delays.