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Abstract A principal challenge facing the control of floating offshore wind turbines (FOWTs) is the problem of instability, or “negative damping,” when using blade pitch feedback to control generator speed. This closed‐loop instability can be attributed to non‐minimum phase zeros in the transfer function from blade pitch to generator speed. Standard approaches to improving stability and performance include robust tuning of control gains and introducing multiple feedback loops to respond to platform motion. Combining these approaches is nontrivial because multiple control loops complicate the impact of coupling in the system dynamics. The single‐loop approach to analyzing stability robustness neglects inter‐loop coupling, while a simplistic multi‐loop approach is highly sensitive to dimensional scaling and overestimates the robustness of the single‐loop controller. This work proposes a sensitivity representation that separates some of the natural FOWT dynamic coupling into a parallel feedback loop in the sensitivity function loop to address both of these concerns. The modified robustness measure is used with a simplified linear FOWT model to optimize scheduled multi‐loop control parameters in an automated tuning procedure. This controller is implemented for the 10‐MW Ultraflexible Smart FLoating Offshore Wind Turbine (USFLOWT) and compared against conventional single‐ and multi‐loop controllers tuned using frequency‐domain analysis and high‐fidelity OpenFAST simulations. The multi‐loop robust controller shows the highest overall performance in generator speed regulation and tower load reduction, though consideration of power quality, actuator usage, and other structural loading leads to additional trade‐offs.

A new control algorithm for nonlinear plants is proposed, ensuring the controlled variable stays within a given set under conditions of parametric uncertainties, external disturbances and high-frequency noises in measurements. The problem is solved in two stages. In the first stage, a low-pass filter is applied to eliminate high-frequency components in the measured controlled signal. In the second stage, a coordinate transformation represents the initial problem with given restrictions as an input-state stability analysis problem of a new system without constraints. An output feedback algorithm has been developed for uncertain nonlinear systems under conditions of parametric uncertainties, external disturbances, and high-frequency noise in measurements. Simulations in MATLAB/Simulink are given. The simulation results show the efficiency of the proposed algorithm. The proposed algorithm can effectively solve control problems for power systems or electromechanical systems in the presence of measurement noises.

The article presents the results of the development of means for intelligent robust control of the parameters of a tribotronic rotor-support system with a tapered bearing with a removable bush. The proposed controller is implemented on the basis of deep Q-network reinforcement learning (DQN) synthesized on the basis of a numerical model of a rotor support system. The control strategy involved simultaneous control of the shaft position and friction in the lubrication layer. Methods for control synthesis are presented for both a deterministic system and a system with stochastic parameters. In the latter case, a controller synthesis technique is proposed that takes into account uncertainties in the system at the training stage. Testing of the resulting controllers shows the better ability of a controller trained to take into account uncertainties to cope with variable loads, as well as predict possible changes in the system and proactively transfer the system to more advantageous states.

Abstract This work tackles the tracking control problem of robotic manipulators where the robot dynamics contains uncertain parameters and joint velocity measurements are not available. Specifically when the robotic manipulator is required to perform a periodic task repetitively, as in most industrial applications, a repetitive learning controller is proposed that does not require joint velocity measurements and can compensate the uncertainties of the robot dynamical parameters and additive disturbances caused due to the periodic joint motion. The proposed solution is achieved via the use of a novel learning component in the controller design in conjunction with a novel model‐free joint velocity observer design. The stability of the closed‐loop system and the convergence of both the joint position tracking error and the joint velocity observation error to the origin are guaranteed via Lyapunov based arguments. Experimental results performed on a 2 degree of freedom robot manipulator are presented to demonstrate the performance of the proposed observer–controller couple.

In previous studies, structure vibration control mainly focused on integer-order systems or commensurate fractional-order (FO) dynamics systems. In this paper, we propose an adaptive radial basis function (RBF) neural network feedback (ARBF-FK) controller for FO building structure vibration systems with viscoelastic (VE) dampers, uncertain structure parameters and unknown seismic waves. Firstly, use a FO multi-order state space description for FO building structural vibration systems. Then, the design of the ideal feedback controller is based on the stability theory of FO multi-order systems. Moreover, to reduce the cost and facilitate the practical implementation of the control, unknown earthquake seismic waves is approximated by a RBF neural network, the ARBF-FK controller is proposed. In addition, to guarantee the stability of the closed-loop control system and avoid falling into local optimum, network weights are adapted by the FO Lyapunov stability theory instead of gradient descent algorithms. Finally, the convergence rate of the system is analyzed and perform various tests on the ARBF-FK controller. The simulation results demonstrate that the ARBF-FK controller has superior performance and is very robust to control FO building structure vibration systems with uncertain structure parameters and unknown external earthquake excitation.

Abstract This paper concentrates on synthesizing robust predictive controllers in uncertain models with time‐delay and saturated input. To handle the complexities simultaneously and reach the desired objective, a state‐feedback structure with unknown gains is considered the compensator. Then, to specify the instantaneous values of the regulator's gains, an optimization issue will be derived relying on linear matrix inequality. Accordingly, in uncertain continuous‐time systems with some constraints and time‐delays, the controller's coefficients would be found in an online way from such an optimization. Numerous continuous‐time simulations are numerically performed to reveal the merit and robustness of the suggested methodology over similar techniques.

Abstract Herein, the load power control of the stand‐alone photovoltaic‐battery hybrid power system (HPS) has been investigated. The underlying HPS consists of a boost DC‐DC converter, a non‐isolated bidirectional half‐bridge converter, a photovoltaic (PV) panel, and a battery pack. On the PV side, a disturbance observer‐based finite‐time terminal sliding mode control (FTSMC) is used to regulate the DC bus to the desired voltage, in the presence of irradiation variation and load changes. On the battery side, the load power control system is constructed, based on a model predictive control (MPC) algorithm, with constraints on state‐of‐charge (SOC) and maximum current value of the battery to improve the battery life cycle and high reliability of the system. To highlight the benefits of the closed‐loop system, the analytical proofs and numerical analysis are presented from a comparative viewpoint. The experimentally derived results, by implementation on TMS320F28335 digital signal processing (DSP), are also presented and discussed for practical justification.

Abstract The traditional control method of doubly fed induction generators (DFIGs) has poor robustness due to the excessive use of machine parameters, and does not fully consider the control conditions of nonideal power grids, resulting in serious fluctuations in current and power under grid disturbances. To solve these problems, model‐free predictive rotor current control (MFPRCC) based on an ultra‐local model is proposed in this article. The ultra‐local model is adaptive because it can accurately emulate the overall structure of the system and estimate the total disturbance of the system in real time. Under a nonideal grid, the cascaded delayed signal cancellation (CDSC) modules are connected in series behind the outer power loop to extract the fundamental component of the rotor current reference. The proposed method is compared with the traditional model predictive rotor current control (MPRCC) method, and the effectiveness of the proposed method is verified on a 1.5 kW DFIG experimental platform.

A type of converter called Quasi-Z-source converters (QZSC) is becoming more popular due to its benefits, such as operating in a single stage, having smaller components, and maintaining continuous input current and a common ground. This converter is widely used in various applications that need a DC-DC converter. The small-signal analysis and linearization method are often used to control the QZSC. The linear model of QZSC does not provide sufficient stability control over a wide range. Sliding Mode Control (SMC) has become widely used for electronic power converters because of their variable structure .This paper presents a SMC for a QZSC with three objectives:1) to achieve stability across a wide range of QZSC; 2) to systematically select the proposed controller coefficients; and 3) to enable tracking of the reference voltage in spite of changes in input voltage, reference voltage, and output load. The simulations have been done with the help of MATLAB/Simulink and show the effectiveness of the proposed method.

This paper clarifies the theoretical similarities and differences between the exosystem-model-based disturbance observer and the model error compensator. In 1972, Johnson proposed the exosystem-model-based disturbance observer. This observer is constructed against the augmented system model, which consists of nominal plant and disturbance-generating models. Ohnishi et al. proposed to cancel out the effect of uncertainties with its estimated disturbance. Furthermore, in 2013, Okajima et al. proposed the model error compensator, which compensates for the error between the output of the nominal plant model and the measurement outputs of the actual plant. The theoretical differences among these control structures have yet to be fully understood. Therefore, this paper theoretically investigates the relation and shows the superiority of the exosystem-model-based disturbance observer to the model error compensator in its freedom to assign the behavior of the uncontrollable modes. This superiority enables the exosystem-model-based disturbance observer to suppress output fluctuations against uncertainties effectively. This effect is shown by a numerical example.

Abstract This paper studies the H∞ control problem for polynomial time‐varying systems. The H∞ control problem has been much less investigated for time‐varying systems in comparison to the time‐invariant systems. Approximate dynamic programming (ADP) is an optimal method to solve the control problems. Therefore, it is valuable to solve the polynomial time‐varying H∞ control problem with the ADP approach. Considering the time as an independent variable for sum‐of‐squares (SOS) optimization problems, an SOS‐based ADP method is proposed to solve this problem. A policy iteration algorithm is presented, where in its policy evaluation step it is sufficient to solve an optimization problem. Some constraints are added to this optimization problem to guarantee the closed‐loop exponential stability. The convergence and stability properties of the proposed algorithm are stated and proven. Moreover, in order to design an H∞ controller with a smaller disturbance attenuation coefficient, a two‐loop algorithm is suggested. Finally, the effectiveness of the proposed method is demonstrated by simulation examples.

This paper describes a new approach to control stall and surge instabilities in the compressor system by using of finite time backstepping-based adaptive sliding mode method. The most important innovation of this study is to provide a finite time adaptive control method to simultaneously prevent the stall and surge instabilities in a compressor system in the presence of disturbance and uncertainty in the compressor characteristic curve as well as the throttle valve. The disturbance and uncertainty issues are covered by a robust sliding mode control and the gain coefficients of this controller as well as the estimation of uncertain terms are obtained by using the adaptive method. The Lyapunov stability criterion is used to evaluate the finite-time stability of the closed-loop system. The performance of the proposed method is compared with other literature methods through simulation in Matlab. The simulation results show that the proposed controller is better than the existing methods in terms of surge preventing and robustness against uncertainty and disturbance.

Abstract Shared‐energy storage (SES) can break the energy interaction barrier between the demand side and the supply side, which is becoming an option for improving the flexibility of energy systems. This study designed a novel structure for the shared‐energy aggregator (SEA), incorporating a microgrid (MG), prosumer and SES. Then, a two‐level market transaction optimization model for SEA, considering the robust stochastic optimization method, is proposed; namely, at the day‐ahead stage, a robust stochastic operation optimization model is constructed to achieve the dispatching plan from the demand side under the objective of achieving the minimum energy consumption cost for the prosumer, and at the real‐time market stage, a robust stochastic operation optimization model from the supply side is constructed to arrange the optimal dispatching strategy under the objective of the maximum aggregator benefits of MG and SES. Finally, an industrial park micro‐grid in Qinghai Province, China, is selected as an example for analysis.

The extensive practice of DC-DC converter interfacing circuits to maintain DC link voltage in DC microgrid applications introduces unavoidable voltage regulation problems due to its nonlinearity. These unregulated voltages demean the performance and result in an unstable system. This brief proposes an internal model control (IMC) non-linear approach based robust proportional-integral-lead (PI-Lead) controller design for regulating a practical fifth-order DC-DC boost converter. The main innovation of this paper is the developed procedure for designing controller. The prominent feature of IMC filter coefficient is to fulfil the desired level of gain margin and phase margin to show the trade-off between performance and robustness. The proposed controller's performance is evaluated in the presence of uncertain disruptions and its effectiveness is verified by comparing it with the some conventional proportional-integral (PI) as well as with recently published IMC-proportional-integral-derivative (IMC-PID) and IMC-proportional integral derivative double derivative (IMC-PIDD2) controllers. The applicability of the proposed control methodology has been validated under varying supply and load current levels in presence of parametric uncertainty using a MATLAB/Simulink tool along with a low-cost microcontroller DSP F280379D based laboratory prototype.

This paper presents a modified super-twisting control algorithm that drives the state of a scalar system at the origin for a predefined-time with a low control cost. The designed control law is used for stabilizing the state of a scalar permanent-magnet synchronous motor system in three different cases: disturbance-free, with rate-bounded disturbances, and with both rate-bounded deterministic disturbances and stochastic noises. The performance of the proposed algorithm is validated by numerical simulations, and comparisons to other predefined-time convergent control laws are provided.

Abstract The wind power generation system of a 5 MW horizontal axis wind turbine has a high wind energy conversion efficiency. The proportion of installed capacity in practical production applications is increasing year on year, so that the stability of its operation becomes a central factor in determining the productivity of the wind farm in question. This paper takes a 5 MW wind turbine as the research object and proposes a parameter‐adaptive robust model predictive control method to achieve self‐optimization of controller parameters through a Bayesian optimization approach. A robust model predictive control strategy, aiming to reduce the power fluctuation while maximizing the power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A Bayesian algorithm is used in this paper to optimize the parameters of the controller. Moreover, wind speeds are simulated using TurbSim for different turbulence intensities of 5%, 10%, and 15% turbulence. Finally, the robust model predictive control toolbox in MATLAB is designed and simulated. The results show that the operational instability of the wind energy system is overcome. Meanwhile, the robustness of the wind energy system operation is improved compared to the traditional model predictive control approach.

Abstract The stabilization problem for uncertain discrete‐time singular Markovian jump systems (DSMJSs) with time‐varying delays is comprehensively covered in this paper. An updated Lyapunov–Krasovskii functional is presented via a discrete state decomposition method. With the help of this constructed Lyapunov–Krasovskii functional, some delay‐ and mode‐dependent sufficient conditions for the open‐loop DSMJSs are derived. Based on these circumstances, a memory mode‐dependent state feedback control is used to create a closed‐loop DSMJS with parameter uncertainties that is regular and causal. And then, the stochastically admissible conditions are attained. Through the exact calculation of each decomposition component for the designed memory state feedback controller, the intended memory state feedback controller settings are determined. It should be mentioned that the algorithm suggested in this article expands the controller design's feasibility and flexibility. The numerical results show how the approach is superior to previous ones, and the given results are less conservative.

This study outlines a comprehensive strategy for designing and implementing robust controllers tailored for intelligent structures. This study presents a robust control-based structural identification technique that uses the input/output data of the system to construct a state-space mode and frequency domain. To reduce vibrations, a robust controller is created using the control Simulink model. The identification and robust control of smart structures using Simulink involve a combination of system identification techniques and control design within the MATLAB Simulink environment. The key challenge is dealing with uncertainties and variations in system dynamics. Robust control methods have been employed to suppress the vibrations during dynamic disturbances. These methods are important for mechanical systems operating under stochastic loading conditions.

Abstract This paper studies the control problem of free‐floating space manipulators, and a control scheme is proposed to solve reactionless control of the end‐effector pose tracking with parameter uncertainty and input disturbance. First, based on dual modeling to treat the end‐effector as a virtual base spacecraft, the dynamics with uncertainties are established which map the joints' torque to the end‐effector pose and base spacecraft attitude, while the inverse kinematics and the derivative of the generalized Jacobi matrix can be avoided in controller design. Then, the reference acceleration stabilization schemes satisfying prescribed performance constraints are carefully designed for tracking errors, and based on these schemes the steady‐state and transient performance of the tracking control can be guaranteed. Further, the radial basis function neural network is adopted to estimate modeling errors caused by parameter uncertainty and input disturbance. In addition, a concurrent learning method is introduced in the network weights matrix update law, which allows the estimation errors to converge a neighborhood of zeros without the need for satisfying the persistent excitation condition. The simulation results verify the effectiveness of the proposed control scheme.

Abstract This paper presents a model predictive control (MPC) approach based on the extended disturbance observer (EDOB) for trajectory tracking of a coaxial octorotor unmanned aerial vehicle (UAV). First, the system dynamic model is derived using Newton–Euler relations in the presence of time‐varying centre of gravity (COG); then, a two‐loop cascade structure is presented to perform the trajectory tracking task. Both loops are controlled using MPC with feedforward compensation based on the EDOB to improve disturbance rejection abilities. When the mass changes, the moment of inertia and COG are affected. The EDOB simultaneously estimates the effects of time‐varying mass, external disturbances, and parametric uncertainties in six degrees of freedom. After obtaining virtual control inputs using designed controllers, constrained control allocation is used to obtain rotors speed in a valid range. The proposed control scheme is evaluated using simulation. The simulation results show the ability of the developed control strategy in accurate trajectory tracking and stable flight in different conditions and being robust to uncertainty and disturbance.

Summary Over the past few decades, global demand for renewable energy has been rising steadily. To meet this demand, there has been an exponential growth in size of wind turbines (WTs) to capture more energy from wind. Consequent increase in weight and flexibility of WT components has led to increased structural loading, affecting reliability of these wind energy conversion systems. Spatio‐temporal variation of rotor effective wind field acts as a disturbance to a WT system, hence, necessitating controllers that can cancel this disturbance. Additionally, assumptions made in extracting linear models for controller design lead to modeling errors resulting from changing operating conditions. Previous attempts have proposed robust controllers incorporating wind disturbance models. However, these controllers have been evaluated on smaller WTs, which experience lower structural loading than larger ones. Additionally, a majority these controllers are based on collective pitch control (CPC), hence do not address loading in the blades. To address these challenges, this contribution proposes an independent pitch‐based robust disturbance accommodating controller (IPC‐RDAC) for reducing structural loads and regulating generator speed in utility‐scale WTs. The proposed controller is designed using μ‐synthesis approach and is evaluated on the 5 MW National Renewable Energy Laboratory (NREL) reference WT. Its performance is evaluated against a gain‐scheduled proportional integral (GSPI)‐based reference open‐source controller (ROSCO) and a CPC‐based RDAC (CPC‐RDAC) controller, developed previously by the authors. Simulation results for various wind conditions show that the proposed controller offers improved performance in blade and tower load mitigation, as well a generator speed regulation.

Abstract Malicious attacks are often inevitable in cyber-physical systems (CPS). Accuracy in Cyber physical system for position tracking of servos is the major concern now a days. In high precision industrial automation, it is very hard to achieve accuracy in tracking especially under malicious cyber-attacks, control saturations, parametric perturbations and external disturbances. In this paper, we have designed a novel predefined time (PDT) convergence sliding mode adaptive controller (PTCSMAC) for such kind of cyber physical control system. Main key feature of our control is to cope these challenges that are posed by CPS systems such as parameter perturbation, control saturation, and cyber-attacks and the whole system then upgrade to a third-order system to facilitate adaptive control law. Then, we present an adaptive controller based on the novel PDT convergent sliding mode surface (SMS) combined with a modified weight updated Extreme Learning Machine (ELM) which is used to approximate the uncertain part of the system. Another significant advantage of our proposed control approach is that it does not require detailed model information, guaranteeing robust performance even when the system model is uncertain. Additionally, our proposed PTCSMAC controller is nonsingular regardless of initial conditions, and is capable of eradicating the possibility of singularity problems, which are frequently a concern in numerous CPS control systems. Finally, we have verified our designed PTCSMAC control law through rigorous simulations on CPS seeker servo positioning system and compared the robustness and performance of different existing techniques.

We study for a benchmark small open emerging economy an optimal robust monetary policy à la Hansen and Sargent (2003) considering additive model uncertainty. The robust control approach supposes that economic agents are not able to assign probabilities to a set of all plausible models and rather focuses on the worst possible misspecification from a benchmark model. Our findings are threefold. First, conducting a global robust optimal monetary policy can be limited since the departure from the benchmark model leads to multiple equilibria. Second, when model uncertainty arises only from the IS curve or the UIP condition, the space of unique solutions is expanded. In fact, when the central bank has a preference for robustness on the IS curve only, it should be more aggressive to demand and real exchange rate shocks but more conservative to cost-push shocks. On the other hand, when it has a preference for robustness only for the UIP, the central bank should be more aggressive to demand and cost-push shocks. Third, a sensitivity analysis suggests that conducting a global robust optimal monetary policy with the same misspecification in all equations is limited due to the persistence of inflation, the low exchange-rate pass-through and the need to anchor inflation expectations. Finally, we propose a Bayesian estimation for the Sidaoui and Ramos-Francia’s model over the period 2001–2019.

Abstract This study presents an approach for robust current and power control for a three‐phase grid‐connected inverter set up with an L‐filter that operates under an unbalanced AC grid‐voltage source. This control method consists of two paralleled current control for the positive and negative sequences to accomplish one of three selective control targets: a) supplying balanced three‐phase current; b) supplying non‐oscillated real power; and c) supplying non‐oscillated imaginary power to the AC grid for a given reference value utilizing the identical control gains. The real and imaginary power references, together with the measured grid voltages, are used in the computation of the reference current for each control target. In addition, the proposed dual‐current control system has a control law that comprises state feedback to offer stability and an integral effect to get rid of the offset error in the dq‐frame. To reduce settling time under uncertain system conditions, an optimization problem based on linear matrix inequalities is solved to determine a set of robust stabilizing gains. Simulation and experimental results using a TMS320F28377D digital signal processor validate the proposed control performance under three control targets.

This article introduces a model-based robust control framework for electrohydraulic soft robots. The methods presented herein exploit linear system control theory as it applies to a nonlinear soft robotic system. We employ dynamic mode decomposition with control (DMDc) to create appropriate linear models from real-world measurements. We build on the theory by developing linear models in various operational regions of the system to result in a collection of linear plants used in uncertainty analysis. To complement the uncertainty analyses, we utilize H∞ (“H Infinity”) synthesis techniques to determine an optimal controller to meet performance requirements for the nominal plant. Following this methodology, we demonstrate robust control over a multi-input multi-output (MIMO) hydraulically amplified self-healing electrostatic (HASEL)-actuated system. The simplifications in the proposed framework help address the inherent uncertainties and complexities of compliant robots, providing a flexible approach for real-time control of soft robotic systems in real-world applications.

Distributed energy resources (DER) are integrated into a microgrid through dc-dc power electronic converters. The bidirectional dc-dc converter regulates charging and discharging operations of ESS. Model predictive control (MPC), is a high-performance control technique for these converters, but it is limited in robustness to parameter mismatch, model uncertainties and sensor measurement noise. Therefore, in this paper, an improved hybrid cascade-parallel extended state observer (CP-ESO) is proposed, for model-free predictive control, to guarantee both robustness to parameter/model uncertainties and measurement noise suppression. A novel structurally-adaptive ESO scheme is also proposed to improve the disturbance rejection of CP-ESO during transient response. These results are supported by analysis and design guidelines for the selection of optimal sub-frequencies. Experimental results, for a bidirectional dc-dc boost converter, validate the effectiveness of the proposed methods against model uncertainties, external disturbances from variable input voltage and load, as well as measurement noise.

Based on the semi-active suspension controller of an automobile, the control law can be adjusted based on the control law reorganization idea, and the active fault-tolerant controller of the semi-active suspension is designed to make the fault closed loop system and the fault-free suspension semi-active suspension. Active suspension closed-loop systems have the same closed-loop pole or proximity system performance. Bench test and simulation results show that: the fault suspension under the control of the active fault-tolerant controller lags behind its performance level after some time and can quickly recover to the same performance as the fault-free automotive semi-active suspension level. And the simulation test and bench test results are basically consistent. Based on the concept of control law reorganization to design the active fault-tolerant control strategy of semi-active suspension, it can effectively realize the active fault-tolerant control of the semi-active suspension of the vehicle to improve the suspension control quality and reliability, and optimize the suspension design.

Background:: Efficient control of liquid levels in interconnected tank systems is a fundamental challenge in various industries, including chemical processes, wastewater treatment, and manufacturing. The traditional approach to achieving precise control has relied on Proportional–Integral–Derivative (PID) controllers, whose performance largely depends on tuned parameters. However, tuning PID controllers for complex and nonlinear systems like coupled tanks remains a challenging task. In recent years, nature-inspired optimization algorithms have gained prominence in control system design. The tree seed optimization (TSO), inspired by the dispersion of tree seeds in search of optimal growth conditions, has shown promise in solving complex optimization problems. This study seeks to explore the application of TSO in tuning PID controllers for coupled tank systems. Methodology:: This research employs the TSO to optimize PID controller parameters for enhanced liquid-level control in coupled tank systems. The optimization process involves integrating performance metrics and closed-loop gain constraints to achieve optimal control of coupled tank systems. The imposed constraints aim to ensure robustness and system stability in the face of uncertainties and disturbances. Besides, μ analysis quantifies the robustness of the system by assessing its ability to tolerate uncertainties. Findings:: Simulation studies conducted in this research verify the efficacy of the proposed TSO-based approach for tuning PID controllers in coupled tank systems. Compared with various methods, the TSO consistently yields better control performance, reduces settling time, and minimizes overshoot. The robustness of the optimized controllers is also evaluated, showing the ability to handle varying operating conditions effectively.

In this paper, a new method for robust control is used. The whole robotic system, including the robot arm and motors in control, is considered. The main purpose of this article is to obtain the best results of the control law in order to achieve the minimum tracking error, which uses congestion optimization. Also, the designers of the control law are based on the nominal model . The real model uses intelligent systems. Control to resistance is evaluated by analysis and analysis.The stability of the system is demonstrated using Lyapunov's direct method, and the simulation results show the effectiveness of the proposed methods applied to a spherical robot driven by permanent magnet dc motors. Using the simulation results, the optimal values ​​of the parameters in the torque controllers have not converged to their true values ​​due to the large modelless dynamics, while they have converged to their true values ​​in the voltage control because it has only parametric uncertainty. . Also, the torque control law requires position vector, velocity vector and acceleration vector feedback.These feedback can not be easily obtained. In contrast, the law of voltage control requires feedback from the position vector, velocity vector, current vector, and time derivative. These feedback can be easily accessed.

Active suspension systems (ASSs) have been proposed and developed for a few decades, and have now once again become a thriving topic in both academia and industry, due to the high demand for driving comfort and safety and the compatibility of ASSs with vehicle electrification and autonomy. Existing review papers on ASSs mainly cover dynamics modeling and robust control; however, the gap between academic research outcomes and industrial application requirements has not yet been bridged, hindering most ASS research knowledge from being transferred to vehicle companies. This paper comprehensively reviews advances in ASSs for road vehicles, with a focus on hardware structures and control strategies. In particular, state-of-the-art ASSs that have been recently adopted in production cars are discussed in detail, including the representative solutions of Mercedes active body control (ABC) and Audi predictive active suspension; novel concepts that could become alternative candidates are also introduced, including series active variable geometry suspension, and the active wheel-alignment system. ASSs with compact structure, small mass increment, low power consumption, high-frequency response, acceptable economic costs, and high reliability are more likely to be adopted by car manufacturers. In terms of control strategies, the development of future ASSs aims not only to stabilize the chassis attitude and attenuate the chassis vibration, but also to enable ASSs to cooperate with other modules (e.g., steering and braking) and sensors (e.g., cameras) within a car, and even with high-level decision-making (e.g., reference driving speed) in the overall transportation system—strategies that will be compatible with the rapidly developing electric and autonomous vehicles.

In this paper, the vibration control of the multivariable model of rotor bearing systems is considered for investigation. Some simply structured controllers that can suppress vibrational disturbances are tested for their robustness via the H∞ optimality criteria. Initially, intelligent optimisation techniques are used to minimize the H∞ mixed-sensitivity norm of the Linear Fractional Transformation (LFT) of the simple two-term PI controllers acting on the rotor system models. This results in some controllers that can suppress the vibration but with a slow oscillatory response. After this, an appropriate interpretation of the Bode plot singular values of the combined sensitivity and control effort matrix is used to explain the performance shortcomings of this controller. Moreover, the existing simply structured controllers in the literature exhibiting a faster performance are examined by using singular value plots. It is shown that when the maximum singular value of the control effort matrix drops below the 0 db line, the performance will be boosted. Finally, the H∞ controllers are designed by using the robust control toolbox in MATLAB. This resulted in rapid disturbance rejection, with the vibration amplitude diminishing to zero after 0.3 s due to double-step disturbances. However, these controllers in the frequency domain have an order of eight and may not be realizable to be implemented in practice. It is concluded that examining the Bode plot of the maximum singular value of the control effort matrix is a useful tool for evaluating performance in the frequency domain. However, designing robust controllers by toolboxes in the time domain can lead to superb performance with higher-order controllers.

Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of all-electric tank, it behaves more drastically in high maneuvering environments. In this work, the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved. By utilizing the smooth adaptive backlash inverse model, a nonlinear robust adaptive feedback control scheme is presented. The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term. Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation, which ensures the system stability. Meanwhile, the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality. Finally, Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash. Contrast co-simulations and experiments illustrate the advantages of the proposed approach.

Recent advancements in deep neural network (DNN) technology are enhancing the utilities of machine learning and meeting potential demands for real-world applications. Deep reinforcement learning (DRL) is a key component in realizing complex decision-making and control by machine learning. However, training DRL-based policies is challenging due to the high costs and risks associated with interactions with real environments. Frameworks to avoid adversarial vulnerability and to achieve robustness are promising solutions to this problem, as they consider worst-case inputs during the evaluation and training phases. In the field of adversarial DRL, regularization methods are known to be effective in maintaining output consistency even when suffering from adversarial perturbations. However, these methods often introduce an undesired bias that conflicts with the original DRL objective, that is, to maximize accumulated rewards. In this study, we propose a new technique that mitigates the undesirable bias effect by orthogonalizing the regularization term with the DRL objective. This approach allows for the use of relatively large adversarial perturbations during training, resulting in more robust policies without significant computational overhead. We evaluate our technique on benchmarks for both discrete and continuous action domains, including Atari 2600 and PyBullet, by integrating it into popular DRL methods such as deep Q-network (DQN), deep deterministic policy gradient (DDPG), and soft actor-critic (SAC). The results demonstrate that our proposed technique is either superior to or competitive with baseline and state-of-the-art methods across all settings, particularly improving robustness by at least twice scores in DQN and DDPG under the adversarial perturbations.

Based on adaptive dynamic programming (ADP) method and the event-triggered mechanism, this paper proposes a robust control method for the cable-driven parallel robots (CDPRs) with external disturbances and cable tension constraint. First, by introducing external disturbances into the performance index function, the robust control problem of CDPRs is transformed into the design of an optimal controller. Then, based on the event-triggered mechanism, the critic network, actor network, and disturbance network are constructed to approximate the performance index function, optimal controller, and external disturbance of the system respectively. Additionally, the null-space redistribution method is utilized to optimize the cable tension. Finally, the Lyapunov method is employed to demonstrate the stability of the closed-loop system, and the effectiveness and superiority of the event-triggered ADP control algorithm are verified through simulations.

In this study, we experimentally verified a robust control law based on variable structure control and the computed torque method on a laboratory motion system. Variable structure control is characterized by high-frequency switching control, which does not have to be applicable to real motion systems. Therefore, we used a suitable reaching law to create a continuous substitution for the discontinuous nonlinear control law. This continuous substitution caused the loss of control invariance to parametric and signal disturbances, but the control loop remained robust, with bounded position errors. We augmented the variable structure control with the computed torque method, which ensured the feedback linearization of the dynamic system. The proposed design of the control law also considered the velocity constraints of the system. Both components of the control law needed to be considered in the velocity limitation, otherwise, the system constraints would be violated. We designed a velocity limitation algorithm for variable structure control to be compatible with the computed torque method, with regard to the desired performance, with exponential convergence and no overshoot. The control law is shown to satisfy the control objectives, and the results are discussed.

In this paper, the computation of all robustly stabilizing Proportional-Integral-Derivative (PID) controllers for Single-Input-Single-Output (SISO) Linear Time Invariant (LTI) systems with/without time delay and unstructured uncertainty is presented. The study proposes a graphical technique to plot the regions of all robustly stabilizing PID controllers that satisfy the H- $\infty $ based robust stability condition. These regions are formed by the Real Root Boundary (RRB), the Complex Root Boundary (CRB), and the Infinite Root Boundary (IRB), which represent the transitions of the roots of the characteristic equation from the left half-plane to the right half-plane (or vice versa), based on Hurwitz stability criteria. The regions are depicted in the ( $k_{i}$ - $k_{d}$ ), ( $k_{p}$ - $k_{i}$ ), and ( $k_{p}$ - $k_{d}$ ) 2-D planes for fixed values of $k_{p}$ , $k_{d}$ , and $k_{i}$ , respectively. The methodology is detailed step by step and demonstrated through various examples. Additionally, stability analyses are visually performed using Nyquist envelopes and uncertainty discs.

This study proposes a robust $H_{\infty }$ guaranteed cost controller (RHGCC) and its design method for set-point tracking control of quadrotor unmanned aerial vehicle (UAV) considering both model uncertainties and external disturbances. To guarantee a certain level of optimal performance against the effects of model uncertainties and external disturbances such as wind gusts, the proposed RHGCC integrates the $H_{\infty }$ control method with guaranteed cost control. Furthermore, a linear matrix inequality (LMI)-based controller design condition is proposed, guaranteeing not only a certain level of linear quadratic (LQ) cost but also the upper bound of the disturbance attenuation level in terms of $H_{\infty }$ norm. The proposed RHGCC is designed for the position and attitude control of the quadrotor UAV, enabling both the position and attitude of quadrotor UAV to be robust against external disturbances. Simulation results validate the effectiveness of the proposed RHGCC and demonstrate that the proposed method outperforms conventional methods in reducing tracking errors against disturbances. Especially, the proposed method significantly reduces the $H_{\infty }$ norm and suppresses disturbances compared to conventional methods.

This paper presents an investigation into the problem of controlling a heterogeneous vehicle platoon, focusing on two aspects: the impact of noise in sensor measurements and the effect of road slope, wind, and rolling resistance on the longitudinal dynamics of each vehicle. To maintain an adequate separation between each follower, a static output feedback (SOF) controller is designed within the predecessor-leader-follower vehicle platoon topology. In order to mitigate the impact of sensor measurement noise and external disturbances on the system, robust control theory is incorporated into the controller design. The engine’s control input is adjusted to compensate for external road disturbances affecting the longitudinal dynamics of each vehicle. This estimation is achieved through the use of a Kalman filter. Closed-loop stability of the heterogeneous vehicle platoon is ensured through a Lyapunov functional analysis. Simulations have shown that the proposed methodology achieves smoother platoon following than a strategy that does not consider or compensate the effect of disturbances on the longitudinal dynamics of the vehicle.

The objective of this article is to design an uncomplicated wide-area damping controller (WADC) to damp out the inter-area oscillations and to tolerate different communication failures. To design a WADC, remote signals are required and these can be transferred to the control centre through phasor measurement units. In reality, each and every time, it is not possible to receive the required data without loss or disturbance. This affected data can creates unwanted WADC output signals when combining with controller feedback gain matrix. Therefore, to address this issue, a detection & correction matrix (DCM) based WADC is proposed in this work by eliminating these unwanted output signals. It can be accomplished through by modifying the feedback gain matrix as per the loss of signals. This proposed controller can be designed by means of a state feedback control technique with suitable modifications in the feedback gain matrix as per the communication path failures. The required modification of feedback gain matrix can be done in real-time by making changes in respective columns and rows of a bad data detected generator. The IEEE-68 bus test system is considered to validate the proposed controller’s effectiveness by simulating all kinds of communication failures and disturbances.

In this paper, we present a distributed robust controller for current sharing and voltage balancing in uncertain DC microgrids. DC microgrids considered in this paper are composed of distributed generation units (DGUs), power lines, and ZI (constant impedance and constant current) loads. It is assumed that the parameters of electric components and those associated to electric and communication network are unknown but belong to known bounded sets. The proposed controller is composed of a robust primary controller and consensus-based secondary controller. The primary controller is a disturbance observer-based voltage stabilizer. It provides robustness against parameter uncertainties and ensures that DGUs under this controller have a desired behavior or dynamics. The secondary controller is a consensus-based controller that generates a reference for the primary controller. It has a distributed structure in the sense that they make decisions by communicating with local neighbors. In addition, it provides a stability condition for the primary control loop to ensure the stability of the whole closed-loop system. A rigorous stability analysis and a constructive design procedure for control parameters are provided. Finally, experiments on a lab-scale microgrid are conducted to validate the proposed controller.

This paper presents a new approach to approximate the AC optimal power flow (ACOPF). By eliminating the need to solve the ACOPF every few minutes, the paper showcases how a realtime feedback controller can be utilized in lieu of ACOPF and its variants. By i) forming the grid dynamics as a system of differential-algebraic equations (DAE) that naturally encode the non-convex OPF power flow constraints, ii) utilizing DAE-Lyapunov theory, and iii) designing a feedback controller that captures realtime uncertainty while being uncertainty-unaware, the presented approach demonstrates promises of obtaining solutions that are close to the OPF ones without needing to solve the OPF. The proposed controller responds in realtime to deviations in renewables generation and loads, guaranteeing improvements in system transient stability, while always yielding approximate solutions of the ACOPF with no constraint violations. As the studied approach herein yields slightly more expensive realtime generator controls, the corresponding price of realtime control and regulation is examined. Cost comparisons with the traditional ACOPF are also showcased—all via case studies on standard power networks.

We introduce an approach for automated driving in highway scenarios based on a two-level hierarchical architecture. The high-level consists of a path planner implemented through a Model Predictive Control algorithm that, using a simple kinematic model of the vehicle, effectively manages the relevant maneuvers of highway driving, such as lane keeping, lane change, velocity, and distance tracking, by means of suitable combinations of artificial potential field functions. Parameters of such functions are dynamically tuned according to the acquired scenario. A switching logic described by a finite state machine, based on acquired sensor data, selects the most appropriate maneuver to realize in the present driving scenario. At the low-level, the motion controller regulates the longitudinal and lateral dynamics through an original decentralized architecture to track the generated reference trajectory. Robustness issues in the presence of plant uncertainty are handled by $H_{\infty } $ synthesis. Extensive simulation tests show the effectiveness of the proposed approach.

This paper presents a control strategy for real-time trajectory optimization and robust path tracking for unmanned off-road vehicles to ensure both stability and performance. The approach takes advantage of a two-degree-of-freedom control framework that combines predictive driving control through perceptual information and feedback control for robust stability. Trajectory generation leverages model predictive control where the particle swarm optimization is used as an optimizer to address problems of the non-smoothness of the traversability information and nonlinear nature of vehicle dynamics. By using the exteroceptive perception, the vehicle could estimate traversability and adapt its motion to achieve fast and smooth driving. For the feedback controller, a system level synthesis is used to faithfully track the planned path despite uncertainty and unknown disturbance. Specifically, we focus on realizing the proposed method as a practical means for the real-time control system. The effectiveness of this method is validated through extensive numerical simulations and experimental tests, demonstrating its practical applicability in uncertain environments for autonomous vehicle navigation.

This paper formally develops robust optimal predictive control solutions that can accommodate disturbances and stabilize periodic legged locomotion. To this end, we build upon existing optimization-based control paradigms, particularly quadratic programming (QP)-based model predictive controllers (MPCs). We present conditions under which the closed-loop reduced-order systems (i.e., template models) with MPC have the continuous differentiability property on an open neighborhood of gaits. We then linearize the resulting discrete-time, closed-loop nonlinear template system around the gait to obtain a linear time-varying (LTV) system. This periodic LTV system is further transformed into a linear system with a constant state-transition matrix using discrete-time Floquet transform. The system is then analyzed to accommodate parametric uncertainties and to synthesize robust optimal $\mathcal {H}_{2}$ and $\mathcal {H}_\infty$ feedback controllers via linear matrix inequalities (LMIs). The paper then extends the theoretical results to the single rigid body (SRB) template dynamics and numerically verifies them. The proposed robust optimal predictive controllers are used in a layered control structure, where the optimal reduced-order trajectories are provided to a full-order nonlinear whole-body controller (WBC) for tracking at the low level. The developed layered controllers are numerically and experimentally validated for the robust locomotion of the A1 quadrupedal robot subject to various disturbances and uneven terrains. Our numerical results suggest that the $\mathcal {H}_{2}$- and $\mathcal {H}_\infty$-optimal MPC controllers significantly improve the robust stability of the gaits compared to the normal MPC.

Chemotherapy, a vital cancer treatment, operates by delivering drugs to target and eliminate cancer cells in the patient body. Mathematical models like the log-kill, Norton-Simon, and $E_{\max }$ hypotheses describe the growth/shrinking of the cancerous tumor due to the interaction and administration of the drugs with the tumor. This paper proposes a robust control approach based on artificial time-delayed theory to track the desired rate of change in tumor volume under model uncertainties and disturbances. The proposed algorithm relaxes the assumption on a priori knowledge of disturbance bound and its derivative. Unlike traditional methods, the control structure is simple, and the total disturbance is estimated by analyzing the previous input and output of the feedback state and control variables. Thus, robustness is ensured without relying on high-frequency switching or high gain. The stability analysis of the proposed scheme is investigated based on the Lyapunov theory. Moreover, extensive simulation results with comparative analysis affirm the efficacy of the proposed approach.

Due to the intermittent and uncertain nature of photovoltaic systems, their incorporation within a DC microgrid presents a challenge to primary control, which directly interfaces with the generation converter. By utilizing a weighted mixed sensitivity control, this study aims to amplify the robustness of a noninverting buck-boost converter integrated photovoltaic system in addressing the uncertainties and disturbances arising from simultaneous fluctuations in irradiance, temperature, and load. The robust control algorithm was formulated by employing frequency-weighting functions and imposing a requirement for the minimum norm of the transformation matrix to achieve robust performance and robust stability. Furthermore, the involvement of reference models in a robust control synthesis offered additional advantages in enhancing the damping of the system. Consequently, the entire design configuration could effectively establish the converter robustness against both generation and load intermittencies occurring simultaneously. The simulation and experiment results are demonstrated to illustrate the efficiency of the designed algorithm.

The development of autonomous vehicles has recently received substantial impetus, fueled by researchers and industry personnel. The need for powerful steering control in autonomous vehicles is critical for assuring the vehicle’s safety and reliability. Robust steering control allows for precise and accurate maneuvering, allowing the vehicle to traverse complicated road conditions. Comparative research on the certification of a robust steering system for autonomous vehicles is presented in this paper. Traditional controllers (PD and PID) are compared with a modern Model Predictive Control (MPC) controller that uses a multi-turn potentiometer and incremental encoder for position feedback. The controllers are designed in MATLAB Simulink and deployed for real-time testing on a Speedgoat performance real-time target Hardware-in-the-Loop (HIL) machine. The study focuses on evaluating the steering system’s real-time performance in terms of accuracy and robustness. The novelty is that this work is carried out in a real experimental modified electric vehicle and presents real-time results obtained using the HIL machine and Rapid Control Prototyping (RCP) technique. The research covers a thorough examination of the experimental hardware configuration, system identification, controller design, and data-gathering technologies. A significant contribution of this research is the use of the HIL machine for real-time performance testing of different controllers with different velocities and sample times, specifically in a speed breaker scenario. To analyze each controller’s response, real-time data is logged at a high sampling rate of 0.1 milliseconds. The research contributes to the advancement of driverless vehicles by providing insights into the optimal performance of steering systems. It also emphasizes the importance of real-time testing of the robust performance of different controllers to ensure human safety in driverless cars.

Dual Excitation Generators (DESGs) are a potential improvement in high-efficiency generator technology, but optimizing performance and control techniques remains a difficulty. This work focuses on a detailed examination of a 1.5 MW DESG, with the goal of identifying important electrical features for optimal control implementation using an analytical sizing method. The project also intends to present and evaluate a novel control mechanism, the Fractional Order Proportional Integral (FOPI) controller. The inquiry begins with a comprehensive literature analysis that categorizes DESGs and describes their many uses. Using experimental data, a custom mathematical model integrating spatial harmonics is created to improve model precision. The FOPI controller is then introduced and tested using simulations with the NREL lab’s Fatigue Aerodynamic Structure and Turbulence (FAST) program. The study’s findings show significant development in wind energy control systems, notably in terms of improving power extraction and velocity tracking. The FOPI controller appears as a revolutionary technology, providing greater stability and robustness in DESG control over its operating range.

As the size of wind turbines (WTs) increase, an additional increase in the structural load on the WT components is to be expected. This will have an impact on operational safety in terms of damage and service life. Spatial and temporal fluctuations in wind speed are responsible for the fatigue load during power generation. To minimize the effects of varying stresses, advanced control systems that incorporate appropriate models of the disturbances are proposed. These controllers are usually developed based on reduced-order models of nonlinear WTs, hence are affected by uncertainties such as modeling errors. Although robust controllers are able to deal with uncertainties, they are still only developed for design situations. Therefore, their performance can deteriorate significantly under very uncertain operating conditions. On the other hand, adaptive controllers are designed to consider multiple operating points in the design. However, most of these methods do not consider the optimization of different objectives in the design for structural load reduction or speed control of WTs. In this paper, a novel adaptive robust observer-based control strategy for structural load reduction and rotor speed regulation in commercial WTs operating at high wind speed regime is proposed. To achieve this, a robust disturbance accommodating controller (RDAC) is combined with an adaptive pitch controller (aIPC), which adapts to changing operating points. The proposed control method is tested on a 1.5 MW reference WT (RWT) developed by the National Renewable Energy Laboratory (NREL). The simulation results show that, compared with the state of the art presented on a gain-scheduled proportional integral (GSPI) and RDAC controllers, the proposed control method reduces the structural load on the rotor blades by 10.7 % and 9.2 %, respectively, and on the tower by 36.2 % and 8.4 %, respectively. Therefore, it makes a key contribution to mitigating the structural dynamic loads on WTs by reducing the load on multiple components. This is achieved without any significant impact on the rotor speed and power regulation performance or the generated power under changing operating conditions.

This paper presents the design of a smooth starter for a DC motor that uses a full-bridge Buck inverter powered by renewable energy. For this aim, a renewable energy power supply is considered in the mathematical model of the “full-bridge Buck inverter–DC motor” system. Whereas, for the design of the smooth starter, a control based on sliding-mode and proportional-integral control is proposed. The performance of the developed control scheme is experimentally validated using the system prototype, the TDK-Lambda G100-17 renewable energy emulator source, the DS1104 board, and the Matlab-Simulink software. Experimental tests are conducted with two different power supply scenarios: 1) a time-varying input voltage and 2) a commercial photovoltaic panel emulator. The obtained experimental results show the robustness of the controller proposed here to abrupt disturbances in the system parameters and torque.