algorithm for control Research Articles (Page 1)

A multi-stage-based discretized jumping control algorithm of wheel-legged jumping robots

Environmental benefits and impacts forecasting for three-phase induction motors operations in marine applications: A multiple linear regression approach

This paper presents an adaptive human-robot interaction framework designed to assist elderly individuals in navigating both flat and inclined surfaces using a mobile robotic platform. The proposed method integrates real-time gait estimation and slope detection to dynamically modulate the velocity of the robot, thereby ensuring stability and safety during locomotion. A hybrid sensor architecture, comprising ultrasonic distance sensors and force-sensitive handles, enables accurate estimation of human gait parameters, such as cadence, step length, and center of pressure variations. The inclination angles are computed through geometric analysis of the sensor arrays mounted on the robot, with real-time calibration techniques implemented via FPGA-based hardware acceleration for low-latency response. A torque and velocity control algorithm, informed by biomechanical changes in gait and user-applied interaction forces, adjusts the robot's behavior to match the user's walking intent on controlled planar inclines. Experimental validation was conducted with 30 participants on slopes of up to 40◦, demonstrating effective gait synchronization, reduced velocity mismatch, and improved safety through stability-aware motion control, including comparative evaluation against baseline controllers (Section IV-D). The current validation is limited to controlled, rigid ramp surfaces (0◦-40◦). The manuscript scopes claims accordingly and explicitly discusses ultrasonic sensing limitations on soft/irregular surfaces and non-planar terrain.

This work reports the experimental demonstration of a dual-wavelength L-band fiber ring laser for remote sensing applications. The system incorporates a polarization-sensitive semiconductor optical amplifier as the gain medium and two fiber Bragg gratings placed 25 km away from the laser cavity using standard single-mode fiber that serve both as wavelength-selective elements and sensing heads. Wavelength switching between single- and dual-channel lasing configurations is enabled by a simplified two-paddle motorized polarization controller. The system achieves optical signal-to-noise ratios exceeding 55 dB and power differences between lasing lines as low as 0.01 dB. To ensure long-term stability, an automatic control algorithm dynamically adjusts the polarization state in real time, compensating for environmentally induced polarization drift. The proposed setup provides a compact and robust solution for polarization-based wavelength switching in fiber lasers, with applications in the field of remote optical sensing.

This article aims to investigate the neural network (NN) nonsingular fixed-time adaptive consensus control issue for nonlinear multiagent systems (MASs) with parameter uncertainties. By introducing a generalized intermediate-variable-based disturbance observer (IVBDO), a novel distributed fixed-time NN adaptive controller is constructed based on the quartic Lyapunov function method. Under this protocol, the mismatched external disturbances of each agent are real-time online estimated; meanwhile, the singularity phenomenon during the fixed-time design process can be effectively eliminated. The presented control algorithm not only guarantees that the controlled system is semi-globally uniformly ultimately bounded (SGUUB) but also that the distributed output tracking errors converge to an adjustable compact set of the origin within a fixed-time interval. Simulation results are displayed to check the effectiveness of the suggested approach.

This paper presents a digital-twin-based model predictive control framework for autonomous process control, demonstrated in a virtual experiment on thermal food processing in a convection oven. In combination with prior work, this approach enables simulation-centered food scientists to deploy physics-based simulation models in live process control environments. The digital twin is realized as a physics-based, data-driven reduced-order model (ROM) that provides faster-than-real-time predictions. The ROM is trained on trajectories from a high-fidelity multiphysics finite-element model of chicken fillets. A central contribution is a model predictive control scheme that overcomes the common fixed-initial-condition limitation of augmented neural ordinary differential equation ROMs: a dedicated sub-optimization step re-synchronizes the surrogate to the measured state of the food item at each control instant, allowing reliable live re-optimization without access to internal ROM states. The controller optimizes oven temperature setpoints to meet target food-quality metrics (core temperature, moisture content, texture) and autonomously accommodates changes to the planned end time during operation. Quantitatively, the ROM achieves relative time-series errors of 0.18–0.49%, and the control algorithm evaluates 501 trajectories of 1800 s real time in a total of 46.6 s on a single core of a processor, demonstrating on-device feasibility without cloud or edge resources. Receding-horizon model predictive control of the remaining setpoints mitigates model–reality mismatch, enforces user-defined food metrics, and sustains closed-loop performance under autonomous operation. • Software-agnostic pipeline to deploy digital-twin surrogates for online control. • Accurate, faster-than-real-time predictions enable autonomous operation. • Online sub-optimization re-synchronizes predictions to measured core temperature. • Level 5 autonomy case beyond the trained time window. • On-device digital twin operation possible without cloud or edge resources.

ABSTRACT In wireless charging systems for electric vehicles, coil misalignment significantly reduces transmission efficiency. To address this issue, this paper proposes a four decoupled receiver coils structure based on the LCC‐S compensation topology. The four coils are arranged in two layers, achieving interlayer decoupling through parameter design. Coils within the same layer are connected in series via half‐bridge rectifiers, with their equivalent mutual inductance being the sum of the absolute values of the two mutual inductances. The rectifier outputs from different layers are connected in parallel to supply power to the load. This structure maintains stable equivalent mutual inductance under misalignment conditions, effectively suppressing output fluctuations without requiring additional control algorithms. This paper establishes a mathematical model of this coupling mechanism and provides design methods for key parameters. It analyzes system operating modes and discusses the impact of parameter variations on system performance. Experiments demonstrate that when coils are offset in two perpendicular directions, the output voltage fluctuation rate is only 10.09%. Transmission efficiency exceeds 90% across all operating conditions, with a peak efficiency of 92.65%.

Robust observer and long short-term memory-based maximum H2/CH4 value predictive intelligent tracking control for two-stage anaerobic digestion with input constraints and multi-rate sampling.

This paper presents the design and development of a self-balancing one-wheel bike as an innovative solution for compact and efficient personal transportation. The system is based on the principle of an inverted pendulum, where stability is achieved through continuous real-time adjustments. A combination of sensors, including a gyroscope and accelerometer, is used to detect the tilt angle and motion of the bike. These inputs are processed using a microcontroller that implements a closed-loop control algorithm, primarily based on PID control, to maintain balance. The motor dynamically adjusts torque to counteract disturbances and ensure rider stability. A prototype model is developed and tested under various operating conditions to evaluate performance. The results demonstrate effective balance control, quick response time, and energy efficiency. This study highlights the potential of single-wheel self-balancing systems for modern urban mobility. Future improvements focus on enhancing control strategies and safety features for practical deployment.

Heterogeneous application environments necessitate the coexistence of diverse Congestion Control Algorithms (CCAs). However, traditional active queue management (AQM) schemes are typically agnostic to specific CCAs and treat heterogeneous traffic flows uniformly, thereby overlooking the diverse buffering requirements of these CCAs. Consequently, bandwidth allocation does not explicitly consider latency and buffer utilization metrics. We present Niche, a lightweight, contention-aware AQM designed for programmable switches. Niche leverages local buffer—backlog statistics to dynamically classify and isolate flows into physical queues without incurring complex measurement overhead. Combined with dynamic bandwidth allocation, Niche not only improves inter-flow fairness but also optimizes comprehensive performance. Experimental validation on programmable switches confirms that in multi-CCA coexistence scenarios, Niche reduces total buffer occupancy by 50% while stabilizing the Jains’ Fairness Index at 0.98. Crucially, it lowers end-to-end latency for latency-sensitive flows like BBR and Vegas by an order of magnitude. Niche approximates the fairness of Fair Queuing (FQ) with limited queue resources, outperforming comparable baselines and demonstrating consistent performance gains as queue availability scales.

<div>In order to improve the comfort performance in commercial vehicles, this study proposes a hierarchical control strategy that integrates the evaluation and migration of control algorithms. First, a quarter-vehicle model with four-degree-of-freedom (4-DOF) is constructed, incorporating the dynamics of the wheel, frame, driver’s cab, and seat. The key modal characteristics of the model are then verified through amplitude–frequency analysis, confirming their consistency with the typical vibration patterns observed in actual commercial vehicles, which provides the foundation for subsequent control strategy evaluation and migration. Then, based on a standard two-degree-of-freedom (2-DOF) suspension model, a weighted comprehensive evaluation function is developed to account for comfort, structural safety, handling stability, and both time- and frequency-domain performance indicators. Using this evaluation function, various control algorithms—including Skyhook control (SH), acceleration-based damping control (ADD), and proportional–integral–derivative control (PID)—are systematically assessed. The control algorithm is migrated to the 4-DOF model to carry out the hierarchical collaborative control. The results show that this method can effectively inhibit vibration transmission to enhance ride comfort and improve structural safety at the same time, while maintaining an acceptable level of handling performance. The transferability and applicability of the hierarchical control method are validated for the considered vertical dynamics scenarios. This article provides a new theoretical method and technical pathway for the comfort-oriented performance optimization of commercial vehicles.</div>

This article investigates the sliding mode control problem of uncertain nonlinear systems and its application in tracking periodic motion. Traditional sliding mode control fulfils perfect tracking at the cost of chattering, especially in the case of high-frequency scenarios. To address the limitations of this method, this article introduces the internal model of the reference signal in building the sliding manifold, which fully takes the advantages of two fundamental methodologies. Specifically, the internal model of the reference signal is involved in the closed-loop system dynamics after the sliding manifold is reached. Thence, all terms related to the reference signal in the tracking error can be eliminated and high-precision tracking can be achieved while reducing chattering phenomenon. Remarkably, the detailed expressions of the derivatives of the reference signal are unnecessarily required, which simplifies the controller design and implementation. It is rigorously proved mathematically that the closed-loop system is asymptotically stable. Finally, the proposed control algorithm is applied for solving the position tracking control problem for permanent magnet synchronous motor servo systems, and experimental results verify the effectiveness and superiority.

The article addresses methods for measuring and controlling strain and temperature in polymer composite materials using fiber Bragg gratings (FBGs) as integrated sensing ele-ments. Polymer composite materials have found widespread application in the aerospace, en-ergy, transportation, mechanical engineering, instrumentation, and robotics sectors, where the combination of low weight, high specific strength, and stiffness is accompanied by increased requirements for reliability and continuous assessment of the structural condition during oper-ation. In this context, the development of non-invasive tools for continuous monitoring of the stress–strain and temperature state of composite elements is of significant relevance.The proposed approach is based on the integration of fiber Bragg gratings directly into the volume of the polymer composite material, enabling both local and distributed real-time monitoring of strain and temperature parameters. The paper analyses the physical principles underlying Bragg reflection formation, the mechanisms by which mechanical strain and temperature variations affect the spectral characteristics of the gratings, as well as methods for separating temperature and strain components of the measurement signal using reference sen-sors and compensation algorithms.The results of experimental studies are presented, confirming the linear response of FBGs within the investigated ranges of mechanical loading and temperature variation, high measurement repeatability, and good agreement between the obtained data and theoretical models as well as alternative measurement techniques. The feasibility of using FBGs for sim-ultaneous measurement of strain and temperature under variable thermomechanical conditions is demonstrated.Based on the obtained results, a structural scheme of a closed-loop system for auto-matic monitoring and control of the stress–strain state of polymer composite structures has been developed. In the proposed system, fiber Bragg gratings perform the functions of prima-ry sensing elements, generating information signals for subsequent digital processing, tech-nical state assessment, and implementation of adaptive or predictive control algorithms. The proposed approach enables a transition from periodic inspections to continuous automated monitoring and can be applied in the design of intelligent composite structures with increased requirements for safety and reliability.

Abstract Offshore wind energy conversion systems have an inherent control challenge due to the long transmission cables necessary to deliver the generated power to an energy station. Grid-tied power system with a transmission cable has dynamics that threaten system stability. In this sense, controlling this kind of system subject to exogenous disturbances is not trivial, requiring sophisticated control algorithms whose parameter tuning is not intuitive. Therefore, this work presents a robust adaptive current control approach optimized with a new improved chaotic stochastic fractal search algorithm with fitness-distance balance (SFS-FDB). This novel optimizer uses a stochastic selective strategy of ten chaotic maps to improve the SFS-FDB performance. The modification consists in replacing the random variables in the SFS-FDB algorithm with values generated by chaotic maps, independently, at each iteration, enhancing the diversity in the solution, which avoids premature convergence, stagnation, and biased solutions. Four scenarios are considered to evaluate the resulting optimized controller: current reference changes, two grid inductance variations (including up to ten times the nominal value of grid-side inductance), and grid frequency oscillations. Simulation results demonstrate the effectiveness of the developed intelligent control solution, making the tracking errors converge to residual values in steady state when the renewable energy system operates under weak grid conditions.

In traditional vehicle trajectory tracking processes, the dynamic model of the vehicle may not accurately represent complex and nonlinear vehicle behaviors. Moreover, conventional control methods may perform poorly when dealing with system uncertainties and disturbances, facing challenges in real-time computation. To address these issues, this paper proposes an autonomous driving control method based on control-affine feedforward neural network (CAFNN) and fast tube model predictive control (tube-MPC). This method utilizes CAFNN for system dynamic identification, replacing traditional mathematical modeling with data-driven neural network pattern recognition to more accurately describe the vehicle's nonlinear dynamic characteristics. On this basis, the proposed tube-MPC structure is divided into two parts: nominal MPC and sliding mode control (SMC). The nominal MPC controller associates the MPC problem with a linear complementarity problem (LCP) using a ramp function, enabling rapid computation of the quadratic programming (QP) solution through piecewise affine (PWA) functions; the auxiliary SMC controller employs multi-power sliding mode reaching laws to enhance the system's robustness against external disturbances and model uncertainties. This control strategy demonstrates high accuracy and stability in vehicle trajectory tracking under complex road conditions, providing strong support for the advancement of autonomous driving technology.

A hybrid control strategy for path tracking of intelligent vehicles is developed to address the limitations of traditional control algorithms in coordinating the control performance of intelligent vehicles under varying working conditions. The strategy combines error-free kinematic model predictive control (MPC) and no sinusoidal terminal sliding mode control (NTSMC) to account for the distinct system characteristics of intelligent vehicles during high-speed and low-speed steering operations. The control strategy employs error-free kinematic model predictive control for low speed and large curvature conditions and non-singular terminal sliding model control for high speed and small curvature conditions. The path-tracking control mode is determined by the vehicle speed, and the switching coordination controller is designed to achieve smooth switching of the lateral control system. And the yaw stability controller (YSC) is designed to ensure yaw stability during high-speed turns. Finally, based on the hardware-in-the-loop (HIL) platform, the hybrid control strategy for intelligent vehicle path tracking was simulated and verified. The simulation and test results show that the designed hybrid control strategy can ensure the path tracking performance of the intelligent vehicle at any speed with good tracking accuracy, real-time performance, and vehicle driving stability.

The increasing global integration of photovoltaic (PV) systems, vital for sustainable development, presents significant operational challenges in rural distribution networks. These feeders, particularly those in Kenya with high resistance-to-reactance (R/X) ratios, are prone to reverse power flow (RPF) and voltage instability, with generic smart inverter controls often proving ineffective due to insufficient voltage rise. This paper addresses this critical gap by developing and validating an adaptive, coordinated smart inverter control algorithm. Employing both Volt-VAR for reactive power support and Active Power Curtailment (APC), the strategy dynamically aligns PV output with real-time local load demand using quasi-static time-series simulations in OpenDSS for a representative Murang'a 11kV rural feeder in Kenya. This grid compliance was achieved at a quantified cost of approximately 1,500 kWh of curtailed renewable energy per day during the dry season. Critically, a comparative seasonal analysis was performed by repeating the simulations under rainy season (long rains) conditions, characterized by significantly reduced and variable solar irradiance. The rainy season results confirmed the absence of reverse power flow due to the naturally lower PV generation, which remained below local load demand throughout the day. This seasonal comparison validates that RPF is predominantly a dry-season phenomenon in this context and that the proposed adaptive control strategy is both critically necessary during high-irradiance periods and benignly non-intrusive during low-irradiance seasons. The study validates a practical methodology for high-penetration PV integration in challenging rural grids, emphasizing the necessity of feeder-specific, seasonally-informed control strategies to balance grid safety with renewable energy yield.

Power balance control algorithm for input-series-output-parallel structure LLC converters

The development of an intelligent connected monorail transit system offers an effective solution to the mismatch between passenger flow and system capacity at various time intervals within urban rail networks. As the core of such a system lies the virtual coupling (VC) technology, which dynamically adjusts train configurations in response to real-time passenger demand, thereby improving resource utilization. However, during VC operations, severe communication delays between vehicles or the sudden emergence of obstacles ahead may still result in rear-end collisions among coupled vehicles, posing significant safety risks. To address these challenges, this paper focuses on the active collision avoidance control of intelligent connected monorail vehicles operating within the VC environment. At the modeling level, a control model is developed to facilitate VC between leading and following vehicles, and the dynamic characteristics of typical operational scenarios—including station approach coupling, tracking coupling, and departure decoupling—are thoroughly analyzed. Building upon this foundation, the train’s behaviors under collision avoidance during accelerated departures, decelerated arrivals, and unexpected obstacle encounters are further investigated. In terms of control strategy, a Model Predictive Control (MPC) algorithm is introduced to enable efficient coordination and proactive collision avoidance among trains. Ultimately, a simulation platform based on Chongqing Rail Transit Line 3 is established for validating the proposed model and algorithm under representative operating scenarios. The evaluation demonstrates gains in system flexibility and safety and technical foundation for the practical implementation of intelligent rail transit systems.

Compared with rule-based and optimization energy management strategies, online optimal energy management control strategies for a dual-mode power-split hybrid electric vehicles (PSHEVs) are able to achieve better fuel economy and real-time performance. Global online optimization of a finite time domain energy management strategy based on the hybrid model predictive control (HMPC) algorithm is proposed in this study. To reduce the computing time, a linearized predictive model is built; because dual-mode PSHEVs can be considered hybrid systems that include continuous and discrete states, the hybrid states can be expressed uniformly. Therefore, a mixed logical dynamic (MLD) predictive model is built based on hybrid system theory, and an HMPC energy management strategy is proposed based on the MLD predictive model. To solve the optimal control problem online to obtain the optimal control sequence, the optimal control problem is converted into a mixed-integer linear programming (MILP) problem. The HMPC-based energy management strategy is compared with dynamic programming (DP)-based and rule-based energy management strategies over two different driving cycles. Simulation results indicate that the HMPC-based EMS achieves 80.60% and 83.79% of the fuel economy performance obtained by the DP-based EMS. In comparison, the rule-based EMS only achieves 66.46% and 70.51% of the DP-based control performance. Therefore, the HMPC-based energy management strategy is favorable for real-time control while effectively improving fuel economy.