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This article aims at designing a robust wide-area damping controller (WADC) to address the problems associated with communication failures. The proposed controller is a state feedback-based controller, which uses generators' states as the controller inputs. The structure of the proposed controller's feedback gain matrix is a combination of two feedback gain matrices to achieve adequate robust performance over different disturbances and communication losses. These two feedback gain matrices are; full-scale feedback gain matrix and decentralized scale feedback gain matrix. Both models are designed by considering all generators' states are available at the controller input side. However, both matrices' structures are different from each other. The conventional LQR state feedback control technique is used to design the full-scale matrix. On the other hand, the decentralized scale matrix is computed using a structurally constrained H 2 -norm optimization technique to acquire the required structure. The full-scale model gives robust performance under different faults. Whereas, the decentralized scale matrix will result robust performance against communication failures. Therefore, to achieve superior performance under all conditions, the proposed controller can be used. To show the efficacy of the proposed controller, it is compared with different existing controllers and the results are illustrated. Simulation studies are conducted on well-known IEEE 39-bus and 68-bus test systems to validate the robustness of the proposed controller considering different faults and communication failures. [ABSTRACT FROM AUTHOR]

Proportional reactive power sharing among the distributed generators (DGs) is still a challenge in an islanded microgrid. The disparity in output voltage of each DG is the main cause of the error in reactive power sharing in DGs. The output voltage difference of DGs is caused by unequal line impedances and connected local loads. In order to reduce the error in reactive power sharing among the DGs, a Robust Volt-VAr control scheme (RVVCS) with an adaptive nominal voltage is proposed in this study. Since the suggested strategy is entirely decentralized, it does not need knowledge of feeder impedance or network configurations. Also, it does not require any communication link or optimization technique. The technique proposed is tested on several network configurations, including various loads, radial networks, reconfigured networks, and mesh networks, and is found satisfactory in each case. In one of the many cases presented in this study, with n q = 3 e - 4 V/VAr, β = 0.2, β add = 0.002 and local as well as common load is connected, the suggested RVVCS minimises the error in reactive power output of D G 1 , D G 2 and D G 3 to (42.68%, - 8.92%, - 33.82%), respectively, compared to the error obtained in RDCS (72.21 %, - 15.61%, - 56.6%), SADCS (72.6%, - 16%, - 56.6%) and CDCS (243.74%, - 69.87%, - 173.87%). A similar reduction pattern in reactive power output is also observed in all the case studies performed in this research. The stability of the proposed strategy is investigated in terms of eigenvalue analysis. The practicability of the proposed technique is validated in time-domain simulation in MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

The study deals with designing a multivariable centralized proportional‐integral (PI) controller for an activated sludge process (ASP) by adopting a new tuning technique. To overcome the tedious task of manual tuning by a classical method that was based on Ziegler–Nichols tuning, a novel approach is developed by formulating the optimization problem and considering it along with the theoretical results to calculate the controller parameters. The proposed method is then compared with the reported method to demonstrate the advantages of the proposed approach. A nonlinear ASP model is selected to evaluate the performance of the designed controller. The simulation carried through the proposed optimization technique exhibits a significant improvement in closed‐loop performance for tuning the centralized controller. It is observed that the proposed method minimizes the effort of tuning the controller significantly. The proposed controller achieves better load disturbance response and lower overshoot than the reported method. The proposed method could improve integral absolute error (IAE) and integral time absolute error (ITAE) performance indices, ranging between 18.69% to 53.80% and 16.42% to 52.97% for a step change in yr1 and yr2, respectively, which are better compared to the reported method. The study also assesses the control system's robustness for their uncertainties with the time constant and time delay. [ABSTRACT FROM AUTHOR]

A Wearable Robotic Knee (WRK) is a mobile device designed to assist disabled individuals in moving freely in undefined environments without external support. An advanced controller is required to track the output trajectory of a WRK device in order to resolve uncertainties that are caused by modeling errors and external disturbances. During the performance of a task, disturbances are caused by changes in the external load and dynamic work conditions, such as by holding weights while performing the task. The aim of this study is to address these issues and enhance the performance of the output trajectory tracking goal using an adaptive robust controller based on the Radial Basis Function (RBF) Neural Network (NN) system and Hamilton–Jacobi Inequality (HJI) approach. WRK dynamics are established using the Lagrange approach at the outset of the analysis. Afterwards, the L 2 gain technique is applied to enhance the control motion solutions and provide the main features of the designed WRK control systems. To prove the stability of the controlled system, the HJI approach is investigated next using optimization techniques. The synthesized RBF NN algorithm supports the easy implementation of the adaptive controller, as well as ensuring the stability of the WRK system. An analysis of the numerical simulation results is performed in order to demonstrate the robustness and effectiveness of the proposed tracking control algorithm. The results showed the ability of the suggested controller of this study to find a solution to uncertainties. [ABSTRACT FROM AUTHOR]

In this article, the issue of finite-time boundedness is investigated for a class of uncertain periodic piecewise systems subject to time-varying input/state delays and external disturbances. The main aim of this article is to design an H ∞ observer-based control protocol for guaranteeing the resulting closed-loop system to be finite-time bounded. The state dynamics of the system are not always directly measurable, so that a system states are reconstructed by constructing a periodic piecewise observer system to get the required result. Subsequently, the Lyapunov–Krasovskii functional by the decomposition of periodic positive-definite matrices is built and in conjunction with convex optimisation technique and extended Wirtinger's integral inequality, the sufficient conditions are derived to assure the finite-time boundedness of the closed-loop system. In particular, the corresponding periodic piecewise time-varying controller and observer gain matrices can be obtained as solutions to a set of derived sufficient linear matrix inequality-based conditions. Finally, the efficacy of the proposed control design is examined by utilizing two numerical examples with simulation results including a vibration system model. [ABSTRACT FROM AUTHOR]

Control inputs of fully constrained cable-driven parallel robots (CDPRs) are constrained by the positiveness of the cables tension, as cables merely apply tensile forces. The positive tension distribution (PTD) in CDPRs is usually guaranteed with iterative optimization techniques, utilizing the redundant actuation of the CDPR. The iterative nature of the conventional PTD limits their real-time application since the worst-case computation time of the iterative methods is not predictable. In addition, optimization methods are prone to model uncertainties. This article addresses the PTD problem in the fully constrained CDPRs with a control viewpoint. In the proposed approach, the PTD algorithm is an integral part of the controller, which explicitly generates positive values for the cables tension. To this aim, a saturation-type function is coupled with the controller, and its effect is compensated using a nonlinear disturbance observer. The stability of the proposed control scheme is also investigated in detail through Lyapunov’s second method, considering a nonsingular terminal sliding mode controller. Furthermore, the performance of the proposed methodology is compared with the conventional method for a six-degrees-of-freedom CDPR, in the presence of uncertainties. Finally, the effectiveness of the proposed control scheme is investigated through experiments. [ABSTRACT FROM AUTHOR]

• A novel nonlinear impulsive time-delay system in fed-batch process is proposed. • The robust parameter identification problem is presented to determine the optimal parameters. • A parallel particle swarm optimization algorithm is developed to solve the identification problem. In this paper, taking the feeding process as an impulsive form and considering the time-delay in fermentation process, we propose a nonlinear impulsive time-delay system to formulate the fed-batch culture in microbial 1,3-propanediol production. For this system, we discuss some important properties of the solution. To determine the optimal time-delay and kinetic parameters in the system, we then present a robust parameter identification model subject to continuous state inequality constraints. By the constraint transcription technique and a penalty method, we transform the robust parameter identification problem to a sequence of optimization problems with only box constraints. On this basis, we propose a parallel Particle Swarm Optimization algorithm to solve the resulting problem. Finally, the numerical results show that the impulsive time-delay system can well describe the fed-batch process and the developed algorithm is highly effective. [ABSTRACT FROM AUTHOR]

This paper aims to determine a time-varying dilution rate of the feed medium which maximizes mean productivity while minimizing both system sensitivity and control cost in glycerol continuous culture. A multi-objective optimal control problem subjected to a nonlinear control system and its auxiliary system is proposed and transformed into a finite-dimensional large-scale multi-objective optimization problem via discretization and time-scaling transformation. A multi-objective competitive swarm optimization algorithm is constructed to solve this transformed problem based on pairwise competition, mutation operation and environment selection. The Pareto front obtained shows the distribution of the optimal target value which indicates the rationality and feasibility of the control system under the approximate optimal strategy. Numerical simulations verify the robustness of the optimal control system and the effectiveness of our numerical solution method. The value of IGD shows the good accuracy and distribution of the proposed multi-objective optimization algorithm. • We propose a multi-objective optimal control problem of glycerol continuous culture. • The multi-objective optimal control problem is approximated by a parameter optimization problem. • A multi-objective competitive swarm optimization algorithm is developed. • Numerical experiments show the effectiveness and applicability of the approach proposed. [ABSTRACT FROM AUTHOR]

The hydraulic turbine governing system (HTGS) is a complex nonlinear system that regulates the rotational speed and power of a hydro-generator set. In this work, an incremental form of an HTGS nonlinear model was established and the Takagi–Sugeno (T-S) fuzzy linearization and mixed H2/H∞ robust control theory was applied to the design of an HTGS controller. A T-S fuzzy H2/H∞ controller for an HTGS based on modified hybrid particle swarm optimization and gravitational search algorithm integrated with chaotic maps (CPSOGSA) is proposed in this paper. The T-S fuzzy model of an HTGS that integrates multiple-state space equations was established by linearizing numerous equilibrium points. The linear matrix inequality (LMI) toolbox in MATLAB was used to solve the mixed H2/H∞ feedback coefficients using the CPSOGSA intelligent algorithm to optimize the weighting matrix in the process so that each mixed H2/H∞ feedback coefficients in the fuzzy control were optimized under the constraints to improve the performance of the controller. The simulation results show that this method allows the HTGS to perform well in suppressing system frequency deviations. In addition, the robustness of the method to system parameter variations is also verified. [ABSTRACT FROM AUTHOR]

In this article, a risk-aware motion control scheme is considered for mobile robots to avoid randomly moving obstacles when the true probability distribution of uncertainty is unknown. We propose a novel model-predictive control (MPC) method for limiting the risk of unsafety even when the true distribution of the obstacles’ movements deviates, within an ambiguity set, from the empirical distribution obtained using a limited amount of sample data. By choosing the ambiguity set as a statistical ball with its radius measured by the Wasserstein metric, we achieve a probabilistic guarantee of the out-of-sample risk, evaluated using new sample data generated independently of the training data. To resolve the infinite-dimensionality issue inherent in the distributionally robust MPC problem, we reformulate it as a finite-dimensional nonlinear program using modern distributionally robust optimization techniques based on the Kantorovich duality principle. To find a globally optimal solution in the case of affine dynamics and output equations, a spatial branch-and-bound algorithm is designed using McCormick relaxation. The performance of the proposed method is demonstrated and analyzed through simulation studies using nonlinear dynamic and kinematic vehicle models and a linearized quadrotor model. The simulation results indicate that, even when the sample size is small, the proposed method can successfully avoid randomly moving obstacles with a guarantee of out-of-sample risk, while its sample average approximation counterpart fails to do so. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to develop a quadrotor with a robust control system against weight variations. A Proportional-Integral-Derivative (PID) controller based on Particle Swarm Optimization and Differential Evaluation to tune the parameters of PID has been implemented with real-time simulations of the quadrotor. Design/methodology/approach: The optimization algorithms are combined with the PID control mechanism of the quadrotor to increase the performance of the trajectory tracking for a quadrotor. The dynamical model of the quadrotor is derived by using Newton-Euler equations. Findings: In this study, the most efficient control parameters of the quadrotor are selected using evolutionary optimization algorithms in real-time simulations. The control parameters of PID directly affect the controller's performance that position error and stability improved by tuning the parameters. Therefore, the optimization algorithms can be used to improve the trajectory tracking performance of the quadrotor. Practical implications: The online optimization result showed that evolutionary algorithms improve the performance of the trajectory tracking of the quadrotor. Originality/value: This study states the design of an optimized controller compared with manually tuned controller methods. Fitness functions are defined as a custom fitness function (overshoot, rise-time, settling-time and steady-state error), mean-square-error, root-mean-square-error and sum-square-error. In addition, all the simulations are performed based on a realistic simulation environment. Furthermore, the optimization process of the parameters is implemented in real-time that the proposed controller searches better parameters with real-time simulations and finds the optimal parameter online. [ABSTRACT FROM AUTHOR]

We present a convex solution for the design of generalized accelerated gradient algorithms for strongly convex objective functions with Lipschitz continuous gradients. We utilize integral quadratic constraints and the Youla parameterization from robust control theory to formulate a solution of the algorithm design problem as a convex semidefinite program. We establish explicit formulas for the optimal convergence rates and extend the proposed synthesis solution to extremum control problems. [ABSTRACT FROM AUTHOR]

This article explores a successive convex optimization method for solving a class of nonconvex programming problems subject to bilinear matrix inequality (BMI) constraints. In particular, many control issues can be boiled down to BMI problems, which are typically NP‐hard. To get out of the predicament, we propose a more relaxed feasible set to approximate the original one, based on which a local optimization algorithm is developed and its convergence is analyzed. As a case of application, we consider static output‐feedback control for uncertain systems with disturbances in restricted frequency intervals. In order to strengthen the disturbance‐rejection capability over the given frequency range, we establish sufficient and necessary analysis conditions via the generalized Kalman‐Yakubovich‐Popov lemma, under which the homogeneous polynomially parameter‐dependent technique is adopted to facilitate the design. Finally, several examples are given to demonstrate the efficiency of the results. [ABSTRACT FROM AUTHOR]

Solving nonlinear model predictive control problems in real time is still an important challenge despite of recent advances in computing hardware, optimization algorithms and tailored implementations. This challenge is even greater when uncertainty is present due to disturbances, unknown parameters or measurement and estimation errors. To enable the application of advanced control schemes to fast systems and on low‐cost embedded hardware, we propose to approximate a robust nonlinear model controller using deep learning and to verify its quality using probabilistic validation techniques. We propose a probabilistic validation technique based on finite families, combined with the idea of generalized maximum and constraint backoff to enable statistically valid conclusions related to general performance indicators. The potential of the proposed approach is demonstrated with simulation results of an uncertain nonlinear system. [ABSTRACT FROM AUTHOR]

This paper presents an optimal decentralized control system for an isolated, networked dc microgrid with multiple sources and composite loads. The key feature of the proposed controller is that it requires only locally measurable states for controlling the local generation while achieving global stability. The controller is designed to minimize a performance index accounting for voltage regulation and branch current regulation with improved dynamics and stability. Decentralized control guarantees high reliability and modularity of the microgrid enabling plug-and-play operations of new units with little modifications. The solution of optimal decentralized control is obtained through constrained parameter optimization technique. The designed controller ensures stability over extended operating conditions of load and droop changes. This leads to a unique robust control law for the dc microgrid that obviates the need for frequent change of controller settings for any change in system parameters. Modal analysis and time-domain simulation of the controlled system substantiate robustness and effectiveness of the proposed decentralized control system in improving the system dynamic performance and optimal set point tracking with a unique controller configuration. [ABSTRACT FROM AUTHOR]

In general, unexpected failures in sensorless brushless DC (BLDC) motors can result in production downtime, costly repairs, and safety concerns. BLDC motors are commonly used in home appliances, the medical sector, aerospace, small‐scale, and large‐scale industries under uncertain operating conditions. Therefore, the fault detection and diagnosis (FDD) of BLDC motor drives can play a very important role in increasing their performance, reliability, robustness control, and operational safety under uncertain operating conditions in critical real‐time applications. To satisfy these issues of hall effect sensor, misplacement of a hall‐effect sensor, inverter IGBT open‐switch fault diagnosis, failure of hall effect sensor, lack of robustness speed control of BLDC motor, which has received substantial interest in academic and industry sectors to establish the proposed work optimization techniques approach FDD strategy for speed control of sensorless BLDC motor under uncertain operating conditions. The proposed optimization techniques such as Bat Algorithm (BA), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA) approach FDD strategies for BLDC motor drives. These FDD strategies simulated by the above optimization techniques on a sensorless BLDC motor with numerical Matlab/Simulink 2020a simulation results are verified. From the simulation results, out of three optimization techniques, the WOA‐based FDD strategies are very effective for both bearing and stator winding faults detection and diagnosis in sensorless BLDC motor drives. [ABSTRACT FROM AUTHOR]

This article presents a systematic robust current control design approach for three-phase voltage-source converters. Robustness is guaranteed by combining intrinsic passive properties of the impedance uncertainty at the point of common coupling together with stability results from a passivity-based control theory. This approach ensures stability against typical uncertainty sources at mid and high frequencies, such as cable resonances or other converters interaction, with significant less conservative performances than the obtained with the traditional robust control theory. The approach uses multiobjective controller synthesis formulation that allows us to logically combine robustness requirements with performance objectives avoiding heuristic iteration over the control structure and parameters. The controller synthesis is performed by means of a nonsmooth $\mathcal {H}_{\infty }$ optimization technique that tunes all free parameters of a vector-based controller function, which constraints its structure. This results in a synthesized controller with lower order than those obtained with convex optimization definitions of the $\mathcal {H}_{\infty }$ control problem. The design methodology is validated in time and frequency domain by means of theoretical analysis and experimental results with three usual grid filters: L, LCL, and LLCL. [ABSTRACT FROM AUTHOR]

Summary: The sector stability theorem is an intuitive condition for the stability of feedback loops that unifies many lines of research including robust control and the theory of passive and dissipative systems. Applying this theorem typically requires checking or enforcing sector bounds on linear time‐invariant systems. This article discusses practical and efficient numerical methods for performing these tasks. We derive a frequency‐domain test for sector boundedness and use it to develop O(n3) numerical algorithms for computing the relative or directional indices of sector bounds. We also discuss how such algorithms can be combined with nonsmooth optimization techniques to enforce sector bounds while designing and tuning controllers. Several application examples illustrate the potential of these tools and techniques. [ABSTRACT FROM AUTHOR]

A two-wheeled single seat Segway robot is a special kind of wheeled mobile robot, using it as a human transporter system needs applying a robust control system to overcome its inherent unstable problem. The mathematical model of the system dynamics is derived and then state space formulation for the system is presented to enable design state feedback controller scheme. In this research, an optimal control system based on linear quadratic regulator (LQR) technique is proposed to stabilize the mobile robot. The LQR controller is designed to control the position and yaw rotation of the two-wheeled vehicle. The proposed balancing robot system is validated by simulating the LQR using Matlab software. Two tuning methods, genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) are used to obtain optimal values for controller parameters. A comparison between the performance of both controllers GA-LQR and BFO-LQR is achieved based on the standard control criteria which includes rise time, maximum overshoot, settling time and control input of the system. Simulation results suggest that the BFOA-LQR controller can be adopted to balance the Segway robot with minimal overshoot and oscillation frequency. [ABSTRACT FROM AUTHOR]

This paper presents a robust tracking controller for the automatic voltage regulator (AVR) system, where both system uncertainties and external disturbances are taken into account. The proposed control scheme combines optimal feedback control with robust control. The parameter gains of the proposed controller have been selected based on Lyapunov theorem to ensure the stability of the closed-loop control system. Moreover, the parameters of the feedback control term are optimized using the Harris Hawks Optimization (HHO) algorithm. The performance of the proposed controller is compared with several Proportional Integral Derivative (PID) controllers optimized with different optimization algorithms. In addition, the fractional order PID (FOPID) controller is used for comparison. The validity of the proposed control scheme is illustrated through the simulation of the AVR system with three cases of simulation have been discussed. [ABSTRACT FROM AUTHOR]

Proportional–integral–derivative is one of the most applicable control methods in industry. Although it is simple and effective in most cases, it does not provide robustness against disturbances and may not perform well in cases with uncertainties and nonlinearities. In this study, a fuzzy adaptive robust proportional–integral–derivative controller is used to control a nonlinear 4 degree-of-freedom quadrotor. An adaptation mechanism is submitted to the proportional–integral–derivative controller for updating the proportional, derivative, and integral gains of proportional–integral–derivative control. Furthermore, a sliding surface is generated and submitted to the adaptation mechanism for better regulation of proportional–integral–derivative gains. Afterward, a fuzzy engine is applied to regulate the sliding surface for better performance of the adaptive proportional–integral–derivative when there are disturbance and uncertainties. The multi-objective grasshopper optimization algorithm is implemented on the control system for the regulation of the control system parameters to minimize the error and control effort of the proposed hybrid control system. Finally, the obtained results are presented for a nonlinear 4 degree-of-freedom multi-purpose (for marine, ground, and aerial maneuvers) quadrotor system designed and built in Sirjan University of Technology, Sirjan, Iran, to assure the effectiveness of this technique. [ABSTRACT FROM AUTHOR]

The current work describes the optimum design of a tuned mass damper inerter (TMDI) to control a benchmark 10-story base-excited linear shear building under seismic excitations. Mass and inertance ratios are preselected, and optimum free vibration parameters of the TMDI (i.e., natural frequency and damping ratios) are calculated for single degree-of-freedom (SDOF) and multi degree-of-freedom (MDOF) models with different configurations of single and double inerter TMDIs at different locations using the colliding bodies optimization technique. Four different inherent damping values are considered for each analysis. Minimizing the H ∞ norm of the roof displacement transfer function is considered as the objective function for robust control of the building. Additionally, the optimum designed damper performance and its robustness is assessed in both the frequency and time domains. Results show that while being robust, the SDOF-based optimized TMDI approach should be used with caution and it is recommended to optimize and employ the TMDI using the MDOF model. Results indicate the superior performance of the proposed well-tuned damper with proper configuration in comparison to a same weight/mass classical TMD. [ABSTRACT FROM AUTHOR]

The proportional-integral-derivative (PID) controller is a widely used controller in automation industries. Several advanced PID tuning/design methods, such as response-based design, internal model control, and controller optimization by stochastic algorithms, have been proposed in the literature. However, regardless of the advantages and accuracy of developed tuning methods, due to the process modeling error and parametric uncertainties, the experimental response always differs from the theoretical response, which required online fine-tuning of the PID parameter. Manual adjustment of three PID parameters disturbs the robustness of the controller from its desired value; also, it does not guarantee the stability of the process. Therefore, this paper focuses on online PID controller tuning with the guaranteed robustness of the controller. A new single variable tuning method is developed for the online robustness and performance adjustment. Moreover, the proposed rules only depend upon the previously optimized PID parameters. The proposed method is an additional feature to all existing PID tuning methods, including optimal PID controller with stochastic optimization algorithms. The proposed method is validated with the help of optimal PID controller design by existing optimal tuning rules, optimized PID with particle swarm optimization and experimental validation on the canonical tank system. [ABSTRACT FROM AUTHOR]

The high penetrations of distributed energy resources (DERs) leads to severe problems such as reducing system inertia and increasing the frequency deviations from the nominal value. The main target of this study is to enable modern photovoltaics (PVs) with large penetration amounts to participate effectively in the load frequency control in the interconnected power systems, in which frequency and tie-line power sharing deviations exist. In this research, a model for the solar PV is developed to help study the heavy penetration of the solar PVs within interconnected power systems. Secondly, a time domain objective function based on the norm of the area control error is formulated. Thirdly, in order to tune the PI controllers, a combined meta-heuristic algorithm based on particle swarm optimization and whale optimization algorithm (PSO-WOA) is developed and compared with the individual PSO and WOA controllers. From the studied scenarios, the developed combined scheme outperforms the other algorithms in terms of system performance indices. Therefore, the developed PSO-WOA approach is plausible and straightforward to solve many engineering problems as it benefits from the exploitation characteristics of the conventional PSO and the exploration features of the WOA. [ABSTRACT FROM AUTHOR]

Summary: The loss of measurements used for controller scheduling or envelope protection in modern flight control systems due to sensor failures leads to a challenging fault‐tolerant control law design problem. In this article, an approach to design such a robust fault‐tolerant control system, including full envelope protections using multiobjective optimization techniques, is proposed. The generic controller design and controller verification problems are derived and solved using novel multiobjective hybrid genetic optimization algorithms. These algorithms combine the multiobjective genetic search strategy with local, single‐objective optimization to improve convergence speed. The proposed strategies are applied to the design of a fault‐tolerant flight control system for a modern civil aircraft. The results of an industrial controller verification and validation campaign using an industrial benchmark simulator are reported. [ABSTRACT FROM AUTHOR]

Semi-supervised classification is a hot topic in pattern recognition and machine learning. However, in presence of heavy noise and outliers, the unlabeled training data could be very challenging or even misleading for the semi-supervised classifier. In this paper, we propose a novel structure regularized self-paced learning method for semi-supervised classification problems, which can efficiently learn partially labeled training data sequentially from the simple to the complex ones. The proposed formulation consists of three components: a cost function defined by a mixture of losses, a functional complexity regularizer, and a self-paced regularizer; and the corresponding optimization algorithm involves three iterative steps: classifier updating, sample importance calculating, and pseudo-labeling. In the proposed method, the cost function for classifier updating and sample importance calculating is defined as a combination of the label fitting loss and manifold smoothness loss. Then, the importance of the pseudo-labeled and unlabeled samples is adaptively calculated by the novel cost. Unlabeled samples with high importance values are pseudo-labeled with their current predictions. In this way, labels are efficiently propagated from the labeled samples to the unlabeled ones in the robust self-paced manner. Experimental results on several benchmark data sets are provided to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Objective: The goal of this paper was to optimize robust PID control for propofol anesthesia in children aged 5–10 years to improve performance, particularly to decrease the time of induction of anesthesia while maintaining robustness. Methods: We analyzed results of a previous study conducted by our group to identify opportunities for system improvement. Allometric scaling was introduced to reduce the interpatient variability and a new robust PID controller was designed using an optimization-based method. We evaluated this optimized design in a clinical study involving 16 new cases. Results: The optimized controller design achieved the performance predicted in simulation studies in the design stage. Time of induction of anesthesia was median [Q1, Q3] 3.7 [2.3, 4.1] min and the achieved global score was 13.4 [9.9, 16.8]. Conclusion: Allometric scaling reduces the interpatient variability in this age group and allows for improved closed-loop performance. The uncertainty described by the model set, the predicted closed-loop responses, and the predicted robustness margins are realistic. The system meets the design objectives of improved speed of induction of anesthesia while maintaining robustness and improving clinically relevant system behavior. Significance: Control system optimization and ongoing system improvements are essential to the development of a clinically relevant commercial device. This paper demonstrates the validity of our approach, including system modeling, controller optimization, and pre-clinical testing in simulation. [ABSTRACT FROM AUTHOR]

• A novel concept of power-to-gas (P2G)-based virtual power plant (GVPP) is designed. • Power-gas-power effect of GVPP brought by P2G and gas storage tank is discussed. • A multi-objective flexible risk aversion model for GVPP operation is proposed. • Payoff table of optimization model is applied for multiple iterative weighting method. • Different operation cases for verifying the proposed operation strategy are defined. Power-to-gas (P2G) provides a new means for accommodating abandoned new energy that will support the optimal operation of virtual power plants (VPPs) in the future. In this study, a novel structure of a P2G-based virtual power plant (GVPP) is designed. A flexible risk aversion model for GVPP operation is proposed with two objectives—maximum operation profit and minimum operation risk. In the model, the conditional value at risk method and robust optimization theory are utilized to reflect uncertainty risks. To solve the multi-objective model, a solution algorithm was constructed for iteratively obtaining the optimal weight coefficient for transforming the multi-objective model into a single-objective model based on a payoff table and a rough set method. Four simulation cases were set for comparative analysis based on a nine-node energy hub system. The results show the following outcomes: (1) GVPP realizes the complementary utilization of distributed energy and forms a power-gas-power recycling mode, (2) the proposed model can provide an effective decision-making tool for different risk-attitude decision makers by setting a reasonable confidence level and robust coefficient, (3) P2G and price-based demand response (PBDR) improve the grid-connected space of clean energy, in particular, PBDR improves the flexibility of system operation and reduces operation risk, and (4) if the maximum emission trade allowance (META) is considered, P2G preferentially converts carbon dioxide into methane when the META is lower, while clean energy is preferentially used to satisfy load demand and the methane produced by P2G can be sold to the natural gas network when the META is higher. Overall, the proposed optimal decision model achieves the maximum utilization of clean energy to obtain higher economic benefits while rationally controlling operation risks; hence, providing reliable support for decision makers. [ABSTRACT FROM AUTHOR]

Summary: We introduce a new robust stability measure for systems with multiple pointwise delays, which is called smoothed spectral abscissa, consistently with the existing measure for delay‐free systems. Its main characteristics are that it is smooth with respect to the system parameters and it provides a trade‐off between the decay rate of the system solution and the H2 norm of a transfer matrix related with the system. The smoothed spectral abscissa is implicitly defined in terms of the H2 norm of an auxiliary system, and its computation is based on the so‐called delay Lyapunov matrix. We show that these features make the smoothed spectral abscissa suitable for the design of robust controllers by using standard gradient‐based optimization techniques and exploiting a novel characterization of the derivatives of the delay Lyapunov matrix with respect to the system parameters. [ABSTRACT FROM AUTHOR]

This paper is focused on a dual-loop current control method (grid current loop and fundamental current loop) for digitally controlled LCL-type shunt active power filters (APFs). Normally, a proportional-resonant (PR) unit is used as the fundamental current controller, where the proportional part of PR unit actually serves as the proportional feedback of inverter-side current and provides a certain degree of damping for LCL resonance. However, its valid damping region is only up to one-sixth of the sampling frequency (fs/6), which leads to less robustness to the grid impedance. To address this issue, a delay-compensation control link is proposed in this paper to replace the proportional part of the PR fundamental current controller and widen the effective damping region. As a result, the system robustness is improved without adding extra sensors or introducing an extra active damping loop. Theoretical analysis proves that it can obtain a wider damping region of (0, fs/4). Moreover, a systematic controller parameter design criterion is studied. In particular, a numerical optimization algorithm is developed to optimize damping property, and a design method based on root locus plot and Bode diagram is presented to enhance harmonic compensation accuracy. Finally, experimental results implemented on a 30 kVA APF prototype have validated the feasibility of the proposed current controller and parameter design criterion. [ABSTRACT FROM AUTHOR]

This paper proposes a new robustification strategy of a primary H ∞ controller based on a systematic selection of adequate fractional weights. Its robust performance (RP) margin is well enhanced with respect to unstructured multiplicative uncertainties presented in linear feedback systems. The proposed robustification strategy provides a robustified H ∞ controller when following proposed hierarchical control is well respected. First, the primary H ∞ controller is synthesized from solving a weighted-mixed sensitivity H ∞ problem using initial integer weights. Thus, an initial RP margin is obtained. Second, an automatic selection of adjustable fractional weights is performed by the particle swarm optimization algorithm, in which some proposed tuning rules are accordingly well satisfied. Third, frequency response data of these weights are computed and then fitted by corresponding approximated integer weights using a frequency identification technique. Finally, these weights reformulate a new weighted-mixed sensitivity problem. The optimal solution to this problem updates the previous initial RP margin. These last three steps are repeated as the updated RP margin is diminished. Otherwise, the proposed hierarchical control is achieved by selecting the best adjustable fractional weights, providing, therefore, the best approximated integer weights and leading, therefore, to the robustified H ∞ controller. In order to confirm the effectiveness of our proposed hierarchical control, primary and robustified H ∞ controllers are applied on a permanent magnet synchronous motor where its actual behavior is modeled by an unstructured multiplicative uncertain model. The results obtained are compared in frequency domains using the singular value plots of their sensitivity functions. Otherwise, the same results are compared in time domains using the Powersim ® software. [ABSTRACT FROM AUTHOR]

PID controller is the most popular controller in many applications because of many advantages such as its high efficiency, low cost, and simple structure. But the main challenge is how the user can find the optimal values for its parameters. There are many intelligent methods are proposed to find the optimal values for the PID parameters, like neural networks, genetic algorithm, Ant colony and so on. In this work, the PID controllers are used in three different layers for generating suitable control signals for controlling the position of the UAV (x,y and z), the orientation of UAV (θ, Ø and ψ) and for the motors of the quadrotor to make it more stable and efficient for doing its mission. The particle swarm optimization (PSO) algorithm is proposed in this work. The PSO algorithm is applied to tune the parameters of proposed PID controllers for the three layers to optimize the performances of the controlled system with and without existences of disturbance to show how the designed controller will be robust. The proposed controllers are used to control UAV, and the MATLAB 2018b is used to simulate the controlled system. The simulation results show that, the proposed controllers structure for the quadrotor improve the performance of the UAV and enhance its stability. [ABSTRACT FROM AUTHOR]

• Studying service network design considering multiple types of services. • Proposing both deterministic and two-stage robust optimization formulations. • Implementing a column-and-constraint generation approach to solve the robust model. • The proposed algorithm outperforms the Benders decomposition method. • The diversification of service types reduces total cost and the number of vehicles. Integrated transportation systems providing multiple types of services are attracting increasing attention due to the diversification of demands. In this paper, we propose both deterministic and two-stage robust optimization formulations for the service network design problem considering multiple types of services. A probability-free uncertainty set is employed to deal with demand uncertainty in the proposed robust model. We implement a column-and-constraint generation approach to solve the robust model, which outperforms the Benders decomposition method. Comparative results indicate that it helps reduce the total cost and the number of vehicles to consider multiple service types in the network. [ABSTRACT FROM AUTHOR]

Highlights • A robust structured sparse sensing matrix consisting of a sparse matrix with a few non-zero entries per row and a base matrix with fast implementation. • Designed a robust structured sparse sensing matrix by minimizing the mutual coherence and a surrogate function for the sparse representation error. • Proposed an alternating minimization algorithm for solving the corresponding optimization problem. • Provided global sequence convergence analysis for the proposed algorithm. Abstract We consider designing a robust structured sparse sensing matrix consisting of a sparse matrix with a few non-zero entries per row and a dense base matrix for capturing signals efficiently. We design the robust structured sparse sensing matrix through minimizing the distance between the Gram matrix of the equivalent dictionary and the target Gram of matrix holding small mutual coherence. Moreover, a regularization is added to enforce the robustness of the optimized structured sparse sensing matrix to the sparse representation error (SRE) of signals of interests. An alternating minimization algorithm with global sequence convergence is proposed for solving the corresponding optimization problem. Numerical experiments on synthetic data and natural images show that the obtained structured sensing matrix results in a higher signal reconstruction than a random dense sensing matrix. [ABSTRACT FROM AUTHOR]

Highlights • We introduced an additional objective function of the ability of peak load regulation to deal with the reverse peak load regulation characteristic of wind power; • The novelty of the paper is to propose a novel multi-objective robust optimization model for unit commitment considering peak load regulation ability and temporal correlation of wind powers. • This paper combined Constraint-and-Column Generation and Normalized Normal Constraint to deal with the proposed multi-objective robust optimization problem. • The widely-used IEEE 118-bus test system is used to verify the proposed robust optimization model. Abstract With the increasing applications of wind energy & power technologies, the stochastic fluctuation and reverse peak load regulation characteristic of wind power brings great challenges to unit commitment of power systems. It is difficult to meet the demand of the peak load regulation with the wind power integration for the existing robust unit commitment models when only considering the cost. In order to solve this problem, an additional objective function of the ability of peak load regulation is constructed for unit commitment solutions. As a result, a novel multi-objective robust unit commitment model considering both the cost and peak load regulation is proposed. Moreover, probabilistic scenario analysis is applied to constraint the model of the fluctuant wind power, and temporal correlation of wind powers is also considered to further improve the economy in the long run. In order to deal with multi-objective problem for the robust optimization solutions, the above-mentioned problem is decomposed into two stages, i.e., a main problem and a subproblem. The main problem is to determine unit commitment and the subproblem is a "min–max optimization" one to obtain the unit outputs. Finally, the simulation analysis based on IEEE118 node system verified the robustness and the validity of the proposed model. [ABSTRACT FROM AUTHOR]

We develop search algorithms based on local search, and a matheuristic that solves a set of mixed integer programming models to improve the robustness of a set of solutions for an academic timetabling problem. The matheuristic uses the solution pool feature of CPLEX while solving two related MIP models iteratively. The solutions form a network (Akkan et al. in Eur J Oper Res 249(2):560-576, 2016. doi:10.1016/j.ejor.2015.08.047), in which edges are defined by the Hamming distance between pairs of solutions. This network is used to calculate a robustness measure, where disruption of a solution is assumed to occur when the time slot to which a team had been assigned is no longer feasible for that team and the heuristic response to this disruption is choosing one of the neighbors of the disrupted solution. Considering the objective function of the timetabling problem and this robustness measure results in a bi-criteria optimization problem where the goal is to improve the Pareto front by enlarging the network. We compare the performance of the heuristics on a set of random instances and seven semesters' actual data. These results show that some of the proposed local search algorithms and the matheuristic find high quality approximate Pareto fronts. Besides being one of the few timetabling algorithms in the literature addressing robustness, a key contribution of this research is the demonstration of the effectiveness of the matheuristic approach. By using this matheuristic approach, for any discrete optimization model that can be solved optimally or near-optimally in an acceptable time, researchers can develop a robustness improvement algorithm. [ABSTRACT FROM AUTHOR]

Abstract An extension of the well-known delayed resonator concept is proposed with the objective to enhance its robustness in vibration suppression under guaranteeing stability of the overall set up. The novel resonator feedback is based on an acceleration measurements passing through a delay of polynomial distribution. The feedback design is formulated as an optimization problem over the parameter set formed by the polynomial coefficients and the gain of the delay free part. The first objective is to minimize the sensitivity of the resonator performance with respect to variations of the excitation frequency, i.e. to widen its frequency stop-band centered at the nominal excitation frequency. The second, equally significant objective is to guarantee that this does not lead to instability of the overall mechanical system coupled with the absorber and to ensure sufficient stability margin. The arising non-convex and non-smooth optimization problem is widely discussed and a penalty method is developed, where the unconstrained problem in every iteration step is solved using available software tools for optimization and spectral design of time delay systems. The proposed design technique is validated by simulations for two case study examples. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a novel fault detection method is developed based on robust characteristic dimensionality reduction (RCDR). The time-constrained sparse representation (TCSR) method is firstly introduced by considering the space and time characteristics of industrial process monitoring data simultaneously. It can remove space-related outliers, time-related outliers and noises by solving an optimization problem. Then, a new RCDR method is proposed, which fully utilizes the constructed robust adjacency graph and considers the data characteristics. Its scatter matrices are specially designed by consideration of the data characteristics of fault detection. The within-class scatter matrix only characterizes normal data set with a classic covariance matrix, while the inter-class scatter matrix characterizes the separability between normal data and fault data through a pre-defined scatter matrix. It is worth mentioning that our method does not make Gaussian assumptions about the distribution of the fault data, and the number of projection directions is not limited as well. The TCSR is also embedded into our proposed dimensionality reduction method, enabling it to handle the fault detection problem under strong disturbances. Simulations on Tennessee Eastman process (TEP) and a case study of electric multiple unit (EMU) braking system of high-speed trains fully demonstrate the effectiveness and applicability of our proposed fault detection method. Highlights • New time-constrained sparse representation method is put forward. • Novel robust characteristic dimensionality reduction method is proposed. • Online fault detection algorithm is developed and fault detectability is analyzed. • Experimental studies on TEP and EMU braking system validate the proposed methods. [ABSTRACT FROM AUTHOR]

Highlights • A robust PTC RD model based on equilibrium stage was developed for the first time. • Optimization of RD systems using the RD PTC model was robust and fast. • An RDWC was optimized using equation-oriented algorithm for the first time. • The bypass efficiency method was applied to optimize RD systems for the first time. • Three intrinsic properties of bypass efficiency method were introduced in this work. Abstract Reactive distillation has significant economic and environmental advantages compared to conventional processes. However, the optimal design of a reactive distillation system is far from mature. This work proposed a novel equation-oriented optimization framework based on a rigorous equilibrium stage model for the optimization of reactive distillation systems. A pseudo-transient continuation (PTC) reactive distillation model was developed to solve the convergence problem of simulations. The bypass efficiency method was incorporated into the PTC model to avoid the intractable mixed integer optimization. The developed model can be integrated well with a steady-state optimization algorithm assisted by PTC models. Two cases demonstrated that the proposed model and optimization framework were robust and fast. However, the bypass efficiency method caused physically meaningless solutions, but they were found to be convertible to physically meaningful solutions without influencing the accurate optimization of number of stages according to the intrinsic properties of the bypass efficiency method introduced in the current work. [ABSTRACT FROM AUTHOR]

Nature is rife with networks that are functionally optimized to propagate inputs to perform specific tasks. Whether via genetic evolution or dynamic adaptation, many networks create functionality by locally tuning interactions between nodes. Here we explore this behavior in two contexts: strain propagation in mechanical networks and pressure redistribution in flow networks. By adding and removing links, we are able to optimize both types of networks to perform specific functions. We define a single function as a tuned response of a single "target" link when another, predetermined part of the network is activated. Using network structures generated via such optimization, we investigate how many simultaneous functions such networks can be programed to fulfill. We find that both flow and mechanical networks display qualitatively similar phase transitions in the number of targets that can be tuned, along with the same robust finite-size scaling behavior. We discuss how these properties can be understood in the context of constraint-satisfaction problems. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A preventive crude oil scheduling method to handle ±10% demand fluctuation is developed. • The scheduling is performed to maximize the profit in single objective optimization. • Inter-period fluctuations in feed rate of crude distillation unit is additionally minimized in multi-objective optimization. • Four industrial-scale examples with time horizon of 3, 7, 14 and 20 days are solved. Abstract Crude oil supply about 33% of the total energy consumption worldwide and is predominantly (>50%) processed in marine access refineries. Therefore, crude oil scheduling which enables the efficient processing of the crude to increase the profitability is an important problem. It often becomes challenging due to the presence of combinatorial constraints, discrete variables and uncertainties. This study addresses the crude oil scheduling under commonly present demand uncertainty. A proactive two-stage approach has been developed to solve such optimization problems. A discrete-time model is used with structure adapted genetic algorithm (SAGA) for solving single- and multi-objective scheduling optimizations. In the first stage, an initial schedule is generated with the nominal parameters provided a priory. The same schedule is then checked and accepted in the second stage only if it is robust with respect to demand uncertainty. This method is applied to four different industrial size problems with scheduling horizon of 3, 7, 14 and 20 days. The objective function in the single objective formulation is the maximization of profit. However, in multi-objective optimization, an additional objective of minimization of fluctuation in crude oil supply to crude distillation units is used as it provides a better control and operability of the plant. The proposed method is successfully implemented for the above four different crude oil scheduling problems to generate the robust schedule. [ABSTRACT FROM AUTHOR]

Abstract A new model-reference data-driven approach is presented which uses the frequency response data of a system in order to avoid the problem of unmodeled dynamics associated with low-order parametric models. It is shown that a convex optimization problem can be formulated (in either the H ∞ , H 2 or H 1 sense) to shape the closed-loop sensitivity functions while guaranteeing the closed-loop stability. The effectiveness of the method is illustrated by considering several case studies where the proposed design scheme is applied in both simulation and to a power converter control system for a specific accelerator requirement at CERN. [ABSTRACT FROM AUTHOR]

This paper considers the dynamic output feedback robust model predictive control (MPC) for a system with both polytopic model parametric uncertainty and bounded disturbance. For this topic, the techniques for handling the unknown true state are crucial, and the strict guarantee of the input/output/state constraints requires replacing the true state by its bounds in the optimisation problems. Previously, in the separate works, we (i) gave the general polyhedral bound; (ii) proposed the general ellipsoidal bound; (iii) applied some special polyhedral bounds to tighten the ellipsoidal bound since the latter is crucial for guaranteeing recursive feasibility. In this paper, (i)-(iii) are unified, and the up-to-date least conservative treatment of the true state bound is given, so the control performance can be greatly improved. The contribution mainly lies in overcoming the difficulties in developing technical details for the unification. A numerical example is given to illustrate the effectiveness of the new method. [ABSTRACT FROM AUTHOR]

Many global optimization (GO) algorithms have been introduced in recent decades to deal with the Computationally Expensive Black-Box (CEBB) optimization problems. The high number of objective function evaluations, required by conventional GO methods, is prohibitive or at least inconvenient for practical design applications. In this work, a new Kriging-Bat algorithm (K-BA) is introduced for solving CEBB problems with further improved search efficiency and robustness. A Kriging surrogate model (SM) is integrated with the Bat Algorithm (BA) to find the global optimum using substantially reduced number of evaluations of the computationally expensive objective function. The new K-BA algorithm is tested and compared with other well-known GO algorithms, using a set of standard benchmark problems with 2 to 16 design variables, as well as a real-life engineering optimization application, to determine its search capability, efficiency and robustness. Results of the comprehensive tests demonstrated the suitability and superior capability of the new K-BA. [ABSTRACT FROM AUTHOR]

Summary: This paper presents the design of a new adaptive optimization‐based second‐order sliding mode control algorithm for uncertain nonlinear systems. It is designed on the basis of a second‐order sliding mode control with optimal reaching, with the aim of reducing the control effort while maintaining all the positive aspects in terms of finite‐time convergence and robustness in front of matched uncertainties. These features are beneficial to guarantee good performance in case of vehicle dynamics control, a crucial topic in the light of the increasing demand of semiautonomous and autonomous driving capabilities in commercial vehicles. The new proposal is theoretically analyzed, as well as verified relying on an extensive comparative study, carried out on a realistic simulator of a 4‐wheeled vehicle, in the case of a lateral stability control system. [ABSTRACT FROM AUTHOR]