Robust Control Approach Research Articles

Real-time random reference tracking nonlinear model predictive control: a case study on wind turbines.

Real-time random reference tracking nonlinear model predictive control: a case study on wind turbines.

Nonlinear Output Feedback Control for Parrot Mambo UAV: Robust Complex Structure Design and Experimental Validation

This paper addresses the problem of controlling quadcopters operating in an environment characterized by unpredictable disturbances such as wind gusts. From a control point of view, this is a nonstandard, highly challenging problem. Fundamentally, these quadcopters are high-order dynamical systems characterized by an under-actuated and highly nonlinear model with coupling between several state variables. The main objective of this work is to achieve a trajectory by tracking desired altitude and attitude. The problem was tackled using a robust control approach with a multi-loop nonlinear controller combined with extended Kalman filtering (EKF). Specifically, the flight control system consists of two regulation loops. The first one is an outer loop based on the backstepping approach and allows for control of the elevation as well as the yaw of the quadcopter, while the second one is the inner loop, which allows the maintenance of the desired attitude by adjusting the roll and pitch, whose references are generated by the outer loop through a standard PID, to limit the 2D trajectory to a desired set path. The investigation integrates EKF technique for sensor signal processing to increase measurements accuracy, hence improving robustness of the flight. The proposed control system was formally developed and experimentally validated through indoor tests using the well-known Parrot Mambo unmanned aerial vehicle (UAV). The obtained results show that the proposed flight control system is efficient and robust, making it suitable for advanced UAV navigation in dynamic scenarios with disturbances.

Robust Control and Energy Management in Wind Energy Systems Using LMI-Based Fuzzy H∞ Design and Neural Network Delay Compensation

This study presents advanced control and energy management strategies for uncertain wind energy systems using a Takagi–Sugeno (T-S) fuzzy modeling framework. To address key challenges, such as system uncertainties, external disturbances, and input delays, the study integrates a fuzzy H∞ robust control approach with a neural network-based delay compensation mechanism. A fuzzy observer-based H∞ tracking controller is developed to enhance robustness and minimize the impact of disturbances. The stability conditions are rigorously derived using a quadratic Lyapunov function, H∞ performance criteria, and Young’s inequality and are expressed as Linear Matrix Inequalities (LMIs) for computational efficiency. In parallel, a neural network-based controller is employed to compensate for the input delays introduced by online learning processes. Furthermore, an energy management layer is incorporated to regulate the power flow and optimize energy utilization under varying operating conditions. The proposed framework effectively combines control and energy coordination to improve the systems’ performance. The simulation results confirm the effectiveness of the proposed strategies, demonstrating enhanced stability, robustness, delay tolerance, and energy efficiency in wind energy systems.

A Robust Adaptive Control Approach to Leader-Following Consensus of Multiple Uncertain Euler–Lagrange Systems Over Switching Networks

A Robust Adaptive Control Approach to Leader-Following Consensus of Multiple Uncertain Euler–Lagrange Systems Over Switching Networks

Observer-Based Robust Explicit Model Predictive Control for Path Following of Autonomous Electric Vehicles with Communication Delay

The existing research on the path following of the autonomous electric vehicle (AEV) mainly focuses on the path planning and the kinematic control. However, the dynamic control with the state observation and the communication delay is usually ignored, so the path following performance of the AEV cannot be ensured. This article studies the observer-based path following control strategy for the AEV with the communication delay via a robust explicit model predictive control approach. Firstly, a projected interval unscented Kalman filter is proposed to observe the vehicle sideslip angle and yaw rate. The observer considers the state constraints during the observation process, and the robustness of the observer is also considered. Secondly, an explicit model predictive control is designed to reduce the computational complexity. Thirdly, considering the efficiency of the information transmission, the influence of the communication delay is considered when designing the observer-based path following control strategy. Finally, the numerical simulation and the hardware-in-the-loop test are conducted to examine the effectiveness and practicability of the proposed strategy.

Data-Driven Stabilization: A Robust Control Approach with Linear Matrix Inequalities

In this paper, we present a novel data-driven approach to stabilize discretized batch reactor systems under the influence of noisy data. By leveraging the framework of Linear Matrix Inequalities (LMIs), our method eliminates the need for explicit system identification, instead utilizing persistently exciting input-output data directly. This enables the design of robust control strategies tailored to the specific dynamics of batch reactors. We address critical control challenges, including state and output feedback stabilization, and demonstrate how data-dependent LMIs ensure stability despite significant uncertainties and measurement noise. Finally, a numerical example is provided to illustrate the efficacy and robustness of the proposed approach. It is shown that whilst the controller ​ designed using LMI satisfies the same criteria provided by the original controller is capable of achieving a faster convergence, better damping and larger robustness. Results from this evaluation show the method stabilizes unstable equilibrium in complex nonlinear environments.

Enhancing electric vehicle powertrain energy efficiency using robust nonlinear control approaches

This paper addresses the issue of controlling the drivetrain of electric vehicles. Taking into account both internal and external system disturbances, including the vehicle’s mass, rotational friction of the shafts, wind speed, vehicle aerodynamics, road type, and slope constraints, the controller’s task is to ensure robustness in vehicle behavior. The significant dynamics of these disturbances and uncertainties in vehicle parameters have a substantial impact on vehicle performance. To overcome these challenges, a nonlinear model of the entire controlled system is developed. Subsequently, a robust nonlinear controller is designed using the damping function version of the backstepping design technique to compensate for all uncertain terms. Within this framework, two primary control loops are established. Firstly, a speed control loop is implemented to achieve precise tracking of the driver’s speed reference. Secondly, the machine current is optimized to generate maximum torque. A formal analysis based on Lyapunov stability is conducted to describe the control system’s performance. Despite parameter uncertainties, it is demonstrated that all control objectives are asymptotically achieved. Ultimately, all control objectives are validated through simulation results using Matlab/Simulink, showcasing the efficiency and robustness of the proposed control technique.

Rapid whole genome sequencing for AMR surveillance in low- and middle-income countries: Oxford Nanopore Technology reveals multidrug-resistant Enterobacter cloacae complex from dairy farms in Sri Lanka

BackgroundAntimicrobial resistance (AMR) is a major global challenge that disproportionately affects low- and middle-income countries (LMICs). Environmental contamination by resistant bacteria from animal production facilities is a major driver of the spread of AMR through the food chain, requiring a robust one-health control approach. Traditional culture-based AMR surveillance is time-consuming and less sensitive, and fails to fully capture the spectrum of AMR, evolutionary trends, and epidemiological patterns of AMR spread. Whole-genome sequencing (WGS) has revolutionized AMR surveillance capabilities. Rapid WGS captures the full AMR spectrum with minimum samples, aids source attribution, and provides insights into trends in AMR spread. The portable Oxford Nanopore® Technology (ONT) platform, coupled with open-source software such as Galaxy and Konstanz Information Miner (KNIME), enables the establishment of a potentially portable, transferable workflow for low-resource settings. This study aimed to assess the AMR burden on four dairy farms in Kandy, Sri Lanka, via a resource-limited LMIC using a low-cost high-throughput screening assay and rapid WGS via ONT with Galaxy and KNIME processing to obtain full antibiotic resistomes.ResultsThe four isolates exhibiting the highest minimum inhibitory concentrations for amoxicillin were identified as Enterobacter cloacae and E. hormaechei by WGS. Chromosomes (4.8 to 4.9 Mb) carry the strain-specific resistance genes blaCMH-1, blaACT-25, fosA_7, and ramA, which are associated with diverse antibiotic classes. Plasmids, including IncFIB (pECLA), IncFII (pECLA), and IncX3, carry multiple resistance genes, including AAC(3)-IIe, AAC(6’)-Ib-cr, APH(3″)-Ib, APH(6)-Id, blaCTX-M-15, blaNDM, blaOXA-1, blaTEM-1, dfrA14, QnrB17, catII, determinant-of-bleomycin-resistance, and sul2. Novel arrangements of insertion sequences were observed in E. hormaechei plasmids. The phenotypic resistance of all the isolates matched the genotypic MDR profiles, including resistance to chloramphenicol, gentamicin, tetracycline, and cotrimoxazole.ConclusionsONT WGS with Galaxy and KNIME processing may be a feasible option for AMR surveillance in resource-limited LMICs. To the best of our knowledge, this is the first in-house whole-genome analysis workflow in the country tailored for AMR surveillance. The presence of potentially pathogenic high-MIC, MDR Enterobacter spp. with wide resistomes, including the blaNDM gene, emphasizes the urgent need to address AMR in animal production facilities within a one-health framework.

A passivity based nonlinear controller for hybrid DC microgrid with constant power loads

This study examines the voltage stability challenge in hybrid DC microgrids that power Constant Power Loads (CPLs). Due to uncertainties in both supply and demand, traditional linear controllers are often inadequate, leading to inevitable voltage oscillations under uncertain conditions. This article introduces a robust passivity-based control (PBC) approach aimed at reducing instability issues, while considering the dynamic behaviour of the hybrid DC microgrid in the presence of CPL. The main objective is to achieve large-signal stabilization of the output voltage of a CPL powered by a hybrid DC microgrid through a series-damped passivity-based control (PBC) scheme, while considering the dynamic performance of renewable energy sources and loads. DC microgrids, which include parallel source subsystems, present additional challenges in analysing the nonlinear stability of the entire system, often requiring complex dynamical equations. Therefore, the stability analysis, based on the passivity property, can be easily conducted for each subsystem individually within the hybrid DC microgrid. The proposed controller prevails over other non-linear controllers because of its accuracy, reliability, and simplicity, while also exhibiting a more robust response compared to the control techniques previously suggested. MATLAB simulations and experimental results are provided to validate the robustness of the proposed controller.

A robust infinite-horizon optimal control approach to climate economics

An infinite-horizon optimal control paradigm is proposed to model the global energy transition to zero-net emissions when carbon dioxide removal (CDR) and electric fuel (E-Fuel) technologies become available. Infinite-horizon optimal trajectories for convex systems are often characterized by global asymptotic stability, where an attractor exists, which is defined as an extremal steady state. In our approach, this asymptotic attractor, known as the ‘turnpike’, represents a sustainable future with zero net emissions. The turnpike can be obtained by solving an “implicit” mathematical programming problem where we introduce robustness for taking into account some important uncertainties on the availability of CO2 storage. The complete mathematical description of an infinite-horizon optimal control formulation is complemented by the numerical illustration which shows results that are consistent with the goals of Paris-agreement.

VIOLATION OF PUBLISHING ETHICS. Robust tracking control for twin rotor multiple-input multiple-output system modelled by uncertain fully-actuated mechanical form with additive disturbances

VIOLATION OF PUBLISHING ETHICSTRMS finds applications across various fields, including aerospace, robotics, education, and research. In control system development field, TRMS provides an excellent platform for developing and testing control algorithms due to its nonlinear and coupled dynamics. This research aims to develop a robust tracking control approach for twin rotor multiple-input multiple-output system (TRMS). First, the model of TRMS is written in the space state of uncertain fully-actuated mechanical form with additive disturbances caused by existence of measurement errors, payload variations, and external disturbances. The robust controller is then designed using the sign function and auxiliary controller to ensure that the closed system including TRMS system and robust controller is always adjusted so that the position-tracking errors tend to the origin, the closed-loop system is globally robust and stable with the lumped system uncertainties caused by modeling errors, the aerodynamic coupling between the vertical and horizontal movements, and friction forces that affect the motions of TRMS. The advantages of this method are a fast transient period, high precision, and strong robustness, and without the adaptive mechanism, it can be applied widely to practical applications such as high nonlinear systems, robotic manipulators, and rigid spacecraft attitude control systems. Finally, to show the advantages this control method is applied to the angles tracking control problem of TRMS which has high nonlinear characteristics, input torque disturbances, and the coupling between the vertical and horizontal movements. The experimental results are presented with the choosing of parameters as nominal parameters that validate the proposed solution. The prominent advantages of this method include a zero percent overshoot, a transient response time of approximately 1s for the yaw angle and 1.5 s for the pitch angle, and superior impulse disturbance rejection compared to linear control. Specifically, the proposed controller stabilizes the pitch angle to its desired initial value within 1s, while the stabilization time for the yaw angle is also 1 s.

A composite adaptive robust control approach for ball screw feed system with mismatched uncertainty

In practical operation, time-varying bounded uncertainties such as dynamic parameter variations and external disturbances significantly affect the performance of the ball screw feed system. An innovative composite adaptive robust control approach is proposed to handle these uncertainties and improve the system’s trajectory tracking performance. Firstly, the system dynamics are modeled and decomposed into nominal and uncertain parts for separate control design. Then, a composite adaptive law incorporating the dead-zone and an exponential leakage term is proposed to estimate the unknown uncertainty boundaries. This method only requires the knowledge that a boundary exists, without needing any additional information about the boundary itself. Furthermore, a controller is designed that can uniformly handle both matched and mismatched uncertainties. Crucially, to address the challenge of determining the dead-zone size, a dual-layer adaptive hierarchy is introduced: the superior-layer adaptive law determines the value of the dead-zone in real-time, providing parameters for the foundational-layer, which in turn provides parameters for the controller. This strategy achieves parameter adaptation, replacing the traditional approach of engineers determining values based on personal experience. Finally, simulations and experiments are conducted, and the results verified that the proposed control method significantly improves the system’s tracking performance, proving its effectiveness.

An ADP-based robust control scheme for nonaffine nonlinear systems with uncertainties and input constraints

Abstract The paper develops a robust control approach for nonaffine nonlinear continuous systems with input constraints and unknown uncertainties. Firstly, this paper constructs an affine augmented system (AAS) within a pre-compensation technique for converting the original nonaffine dynamics into affine dynamics. Secondly, the paper derives a stability criterion linking the original nonaffine system and the auxiliary system, demonstrating that the obtained optimal policies from the auxiliary system can achieve the robust controller of the nonaffine system. Thirdly, an online adaptive dynamic programming (ADP) algorithm is designed for approximating the optimal solution of the Hamilton–Jacobi–Bellman (HJB) equation. Moreover, the gradient descent approach and projection approach are employed for updating the actor-critic neural network (NN) weights, with the algorithm’s convergence being proven. Then, the uniformly ultimately bounded stability of state is guaranteed. Finally, in simulation, some examples are offered for validating the effectiveness of this presented approach.

Robust energy management system for electric vehicle

Abstract The Energy Management System (EMS) is critical for electric vehicle (EV) in order to optimize energy consumption, improve efficiency, and enhance vehicle performance. The EMS provides the optimization of energy distribution among various vehicle components, reduces energy losses and maximizes the vehicle's efficacy. The EMS reduces battery stress to prevent excessive charging and discharging cycles; thereby, decreases the necessity for premature battery replacement which, in turn, contributes to the battery's life time. The goal of this research is to develop robust control technique to maximize the use of energy storage systems, renewable energy sources and the bidirectional power flow associated with EVs. The proposed robust control approach is based on combination of flatness theory with artificial neural network. The controller is responsible for maintaining the voltage DC bus stabilized and enhancing the quality of the power fed to the EV side. The performance of controlled EMS is verified via computer simulation within MATLAB/SIMULINK environment. As compared to classical proportional-integral (PI) control, the computer results show the proposed controller (FEMS-ANN) gives higher power quality of EV, lower overshot level in the DC voltage, faster response to abnormal conditions, and less steady state error.

Novel Approach for Robust Control of Axial Piston Pump

The article is devoted to designing novel multivariable robust μ-control of an open-circuit axial piston pump. In contrast with classical solutions of displacement volume control, in our case, the hydro-mechanical controller (by pressure, flow rate, or power) is replaced by an electro-hydraulic proportional valve which receives a control signal from an industrial microcontroller. The valve is used as the actuator of the pump swash plate. The pump swash plate swivel angle determines the displacement volume and the flow rate of the pump. The μ-controller design is performed on the basis of a one-input, two-output model with multiplicative output uncertainty. This model is estimated and validated from experimental data at various loads by multivariable identification. The designed control system achieves robust stability and robust performance for the wide working mode of an axial piston pump. To conduct this experimental study, the authors have developed a laboratory test bench, enabling a real-time function of the control system via USB/CAN communication. The designed controller is implemented in a rapid prototyping system, and real-time experiments are performed. They show the advantages of μ-control and confirm the possibility of its implementation in the case of the real-time control of an axial piston pump.

Sliding mode sensorless control model of hub motor based on digital twin technology

Abstract This paper introduces a novel integration of sliding mode control and digital twin technology to develop a sensorless control model for hub motors. The proposed approach overcomes the limitations of traditional methods, such as the inability to pre-evaluate parameter changes and the lack of multidimensional monitoring and interaction analysis. The constructed model enables real-time simulation that closely reflects actual motor behavior, allowing precise tuning and validation of control parameters. Simulation results show that the system achieves rapid response, robust performance under varying load and speed conditions, and superior adaptability compared to existing methods. These advancements highlight the potential of this approach for precise and robust motor control in robotics and autonomous vehicles.

Robust mean-variance precommitment strategies of DC pension plans with ambiguity under stochastic interest rate and stochastic volatility

. This article studies a robust optimal investment problem with an ambiguity-averse manager (AAM) for a defined contribution (DC) plan with multiple risks under the mean-variance criterion. In the pension accumulation stage, the interest rate, the volatility, and the salary level are considered to be stochastic. The financial market consists of a risk-free asset, a risky asset, and a rolling bond. We assume that the term structure of interest rates is driven by an affine interest rate model, while the stock price and the salary level are modeled by the stochastic volatility model with stochastic interest rate. The goal of an AAM is to find a robust optimal strategy to maximize the expectation of terminal wealth and minimize the variance of terminal wealth in the worst-case scenario. By applying the Lagrange dual theory and the robust optimal control approach, we obtain closed-form expressions of the robust precommitment strategy and the efficient frontier, and subsequently some special cases are derived. Finally, a numerical example is given to illustrate the results obtained.

Enhancing Solar Furnace Performance by a Robust QFT-Based Control Approach

Enhancing Solar Furnace Performance by a Robust QFT-Based Control Approach

Finite-time extended state observer-based robust control approach of RTG cranes

In this paper, a robust control method based on finite-time extended state observer is developed for the rubber-tired gantry crane under the influence of wind disturbance. Firstly, an extended state observer is proposed to estimate not only the wind disturbance from the marine environment but also to observe the velocity to address the difficulty in sensor installation. The settling time of the observer is analyzed and calculated to serve as the basis for evaluating the stability of the whole control system. Subsequently, a hierarchical sliding mode controller is presented based on the observer's estimation results. The stability of the control system is demonstrated using the Lyapunov’s stability theory to ensure that the estimated error converges to a bounded domain and the state error converges to zero. Finally, the effectiveness of the proposed method is validated through numerical simulations and compared with a standard sliding mode controller in the case of wind disturbance.

A Less Conservative Robust Control Approach and Its Application to Truck-Trailer System

A Less Conservative Robust Control Approach and Its Application to Truck-Trailer System