Parametric Robust Control Research Articles

Decoding kinematic information from beta-band motor rhythms of speech motor cortex: a methodological/analytic approach using concurrent speech movement tracking and magnetoencephalography.

Articulography and functional neuroimaging are two major tools for studying the neurobiology of speech production. Until now, however, it has generally not been feasible to use both in the same experimental setup because of technical incompatibilities between the two methodologies. Here we describe results from a novel articulography system dubbed Magneto-articulography for the Assessment of Speech Kinematics (MASK), which is technically compatible with magnetoencephalography (MEG) brain scanning systems. In the present paper we describe our methodological and analytic approach for extracting brain motor activities related to key kinematic and coordination event parameters derived from time-registered MASK tracking measurements. Data were collected from 10 healthy adults with tracking coils on the tongue, lips, and jaw. Analyses targeted the gestural landmarks of reiterated utterances/ipa/ and /api/, produced at normal and faster rates. The results show that (1) Speech sensorimotor cortex can be reliably located in peri-rolandic regions of the left hemisphere; (2) mu (8-12 Hz) and beta band (13-30 Hz) neuromotor oscillations are present in the speech signals and contain information structures that are independent of those present in higher-frequency bands; and (3) hypotheses concerning the information content of speech motor rhythms can be systematically evaluated with multivariate pattern analytic techniques. These results show that MASK provides the capability, for deriving subject-specific articulatory parameters, based on well-established and robust motor control parameters, in the same experimental setup as the MEG brain recordings and in temporal and spatial co-register with the brain data. The analytic approach described here provides new capabilities for testing hypotheses concerning the types of kinematic information that are encoded and processed within specific components of the speech neuromotor system.

Robust control design for Electric Helicopter Tail Deceleration system: Fuzzy view and Stackelberg game theory-based optimization

A novel robust controller is designed for Electric Helicopter Tail Deceleration (EHTD) system which is subject to uncertainties and nonlinearity. Then, the optimal parameters of the control are solved based on Stackelberg game theory. The fuzzy set theory is used to describe the uncertainty bound, which is distinguished from probability theory or fuzzy logic theory. Based on two-player (leader and follower) Stackelberg game, two performance indexes are proposed for novel robust controller which contains two optimal parameters. The system performance and control cost are included in performance indexes. Then, based on Stackelberg strategy, the optimal decisions are made by leader and follower, which can minimize the performance indexes. Therefore, by numerical simulation, the EHTD system is applied to prove that the design of robust control and optimal parameters based Stackelberg strategy are effective.

Robust stability region analysis of time‐delayed load frequency control systems with EVs aggregator using Kharitonov theorem

AbstractThis study focuses on analyzing the robust stability regions and robustness margin of a time‐delayed load frequency control (LFC) system with Electric Vehicles (EVs) using the Kharitonov Theorem. Communication time delays in LFC systems can jeopardize stability and reliability, leading to suboptimal controller performance. Integrating EVs into LFC systems enhances dynamical stability but introduces additional complexity. Therefore, it is crucial to employ robust analysis and controller design techniques to ensure system stability. In this study, we utilize the Kharitonov Theorem to determine the robust stability regions and stability boundaries in the parameter plane of the Proportional‐Integral (PI) controller. By considering communication time delays and parametric uncertainties in the LFC system with EVs (LFC‐EVs), the robust PI controller gains using these methods are efficiently computed. To evaluate the performance of the theoretically computed robust controller parameters, time‐domain simulations are conducted.

Current-Constrained Adaptive Robust Control for Uncertain PMSM Drive Systems: Theory and Experimentation

In this paper, we investigate the speed tracking control problem for permanent magnet synchronous motor (PMSM) drive systems. In particular, the current constraint, parametric uncertainties, and the external load disturbance are addressed simultaneously. To deal with these issues, we first propose a system transformation scheme, which equivalently transforms a PMSM system with the current constraint into the one without any constraints, thereby reducing the difficulties in the controller design caused by the current constraint. Based on the transformed system, the adaptive parameter estimation and robust control are integrated to derive a current-constrained adaptive robust controller (CCARC), which guarantees the robust tracking performance under the current constraint, parametric uncertainties and the external load disturbance. For the closed-loop system, the Lyapunov-based analysis is provided to show the convergence and stability. Finally, the effectiveness of the proposed method is experimentally validated using a 5.5 kW PMSM.

Adaptive Single-Input Recurrent WCMAC-Based Supervisory Control for De-icing Robot Manipulator

The control of any robotic system always faces many great challenges in theory and practice. Because between theory and reality, there is always a huge difference in the uncertainty components in the system. That leads to the accuracy and stability of the system not being guaranteed with the set requirements. This paper presents a novel adaptive single-input recurrent wavelet differentiable cerebellar model articulation controller (S-RWCMAC)-based supervisory control system for an m-link robot manipulator to achieve precision trajectory tracking. This adaptive S-RWCMAC-based supervisory control system consists of a main adaptive S-RWCMAC, a supervisory controller, and an adaptive robust controller. The S-RWCMAC incorporates the advantages of the wavelet decomposition property with a CMAC fast learning ability, dynamic response, and input space dimension of RWCMAC can be simplified; and it is used to control the plant. The supervisory controller is appended to the adaptive S-RWCMAC to force the system states within a predefined constraint set and the adaptive robust controller is developed to dispel the effect of the approximate error. In this scheme, if the adaptive S-RWCMAC can not maintain the system states within the constraint set. Then, the supervisory controller will work to pull the states back to the constraint set and otherwise is idle. The online tuning laws of S-RWCMAC and the robust controller parameters are derived from the gradient-descent learning method and Lyapunov function so that the stability of the system can be guaranteed. The simulation and experimental results of the novel three-link De-icing robot manipulator are provided to verify the effectiveness of the proposed control methodology. The results indicate that the proposed model has superior accuracy compared to that of the Standalone CMAC Controller. The parameters of the average squared error in the S-RWCMAC -based 3 robot joints are lower than those of the Standalone CMAC Controller by 0.023%, 0.029%, and 0.032%, respectively.

A two-stage robust optimization model for a virtual power plant considering responsiveness-based electric vehicle aggregation

With the goal of pursuing carbon neutrality, this study is aimed to investigate effectively managing distributed renewable energy. Considering the uncertainty of wind power (WP), photovoltaic power (PV), and load, a two-stage robust optimization model for virtual power plant (VPP) is proposed, with a focus on calculating the available capacity of electric vehicle (EV) aggregated virtual energy storage (VES). Time–space distribution characteristics of EVs and the responsiveness of EV users are analyzed and the available capacity calculation model is constructed. In the first stage, the bidding strategy and unit combination of the VPP are determined based on the predicted values of WP, PV, and load. Combining with the pre-decided decision from the first stage, the output of each unit is determined by considering an uncertainty set in the second stage, for achieving the best operational benefit under worst-case scenarios. Robust control parameters are introduced into the model and used to adjust conservativeness. The model is solved by applying the C&CG algorithm. Additionally, a carbon trading mechanism is leveraged to balance the system economy and environmental friendliness. The results show that the optimal scheduling solution helps to reduce fluctuations caused by uncertainties, balancing the economic benefits and operational risks of the VPP. Besides, guidance is provided for VPP operators to formulate reasonable incentives for EV users and the construction of energy storage systems. When the carbon trading price of the VPP proposed in the paper is set at 160 ¥/ton, economic and environmental sustainability can be effectively balanced.

An at-scale tailless flapping wing hummingbird robot: II. Flight control in hovering and trajectory tracking

Flight control such as stable hovering and trajectory tracking of tailless flapping-wing micro aerial vehicles is a challenging task. Given the constraint on actuation capability, flight control authority is limited beyond sufficient lift generation. In addition, the highly nonlinear and inherently unstable vehicle dynamics, unsteady aerodynamics, wing motion caused body oscillations, and mechanism asymmetries and imperfections due to fabrication process, all pose challenges to flight control. In this work, we propose a systematic onboard control method to address such challenges. In particular, with a systematic comparative study, a nonlinear flight controller incorporating parameter adaptation and robust control demonstrates the preferred performances. Such a controller is designed to address time-varying system uncertainty in flapping flight. The proposed controller is validated on a 12-g at-scale tailless hummingbird robot equipped with two actuators. Maneuver experiments have been successfully performed by the proposed hummingbird robot, including stable hovering, waypoint and trajectory tracking, and stabilization under severe wing asymmetries.

Research on Coordination Planning Model of Source-Grid-Load-Storage Considering Demand Response Uncertainty

With the integration of wind power, photovoltaic, and other new energy into the grid, the growth of carbon emissions has been effectively suppressed, which greatly contributes to the realization of the “Carbon peak, Carbon neutral’’. However, the randomness and instability of new energy generation also greatly affect the stable operation of the power grid. At present, the combination of demand response and power system can give full play to the maximum potential of new energy. This is an effective method to realize stable and optimal operation of power system. Based on this, this paper first constructs the SOC output characteristic model of energy storage and considers the DLC and time-of-use price as well as different demand response types. The robust optimization method is used to deal with the uncertainty of demand response. Secondly, a multi-objective function and constraint conditions are constructed to minimize the system planning cost, operation cost, and pollution emission. Finally, the genetic algorithm is used to analyze an example in a certain area of Fujian. In the example, the economy of planning schemes under different robust control parameters is compared and analyzed. The results show that the model and calculation method in this paper are beneficial to delaying the investment in power systems, and can provide decision-making reference for mining and utilizing demand response resources, reducing system planning costs and improving operation efficiency.

MicroRNA profiling of low concentration extracellular vesicle RNA utilizing NanoString nCounter technology

AbstractExtracellular vesicles (EV) and the microRNAs that they contain are increasingly recognised as a rich source of informative biomarkers, reflecting pathological processes and fundamental biological pathways and responses. Their presence in biofluids makes them particularly attractive for biomarker identification. However, a frequent caveat in relation to clinical studies is low abundance of EV RNA content. In this study, we used NanoString nCounter technology to assess the microRNA profiles of n = 64 EV low concentration RNA samples (180–49125 pg), isolated from serum and cell culture media using precipitation reagent or sequential ultracentrifugation. Data was subjected to robust quality control parameters based on three levels of limit of detection stringency, and differential microRNA expression analysis was performed between biological subgroups. We report that RNA concentrations > 100 times lower than the current NanoString recommendations can be successfully profiled using nCounter microRNA assays, demonstrating acceptable output ranges for imaging parameters, binding density, positive/negative controls, ligation controls and normalisation quality control. Furthermore, despite low levels of input RNA, high‐level differential expression analysis between biological subgroups identified microRNAs of biological relevance. Our results demonstrate that NanoString nCounter technology offers a sensitive approach for the detection and profiling of low abundance EV‐derived microRNA, and may provide a solution for research studies that focus on limited sample material.

Novel ARC-Fuzzy Coordinated Automatic Tracking Control of Four-Wheeled Mobile Robot

Four-wheeled, individual-driven, nonholonomic structured mobile robots are widely used in industries for automated work, inspection and exploration purposes. The trajectory tracking control of the four-wheel individual-driven mobile robot is one of the most blooming research topics due to its nonholonomic structure. The wheel velocities are separately adjusted to follow the trajectory in the old-fashioned kinematic control of skid-steered mobile robots. However, there is no consideration for robot dynamics when using a kinematic controller that solely addresses the robot chassis’s motion. As a result, the mobile robot has limited performance, such as chattering during curved movement. In this research work, a three-tiered adaptive robust control with fuzzy parameter estimation, including dynamic modeling, direct torque control and wheel slip control is proposed. Fuzzy logic-based parameter estimation is a valuable tool for adjusting adaptive robust controller (ARC) parameters and tracking the trajectories with less tracking error as well as high tracking accuracy. This research considers the O type and 8 type trajectories for performance analysis of the proposed novel control technique. Our suggested approach outperforms the existing control methods such as Fuzzy, proportional–integral–derivative (PID) and adaptive robust controller with discrete projection (ARC–DP). The experimental results show that the scheduled performance index decreases by 2.77% and 4.76%. All the experimental simulations obviously proved that the proposed ARC-Fuzzy performed well in smooth groud surfaces compared to other approaches.

Robust PID optimal tuning of a Delta parallel robot based on a hybrid optimization algorithm of particle swarm optimization and differential evolution

AbstractIn this paper, we propose an approach to tune optimal parameters of a robust PID controller by means of computed torque control (CTC) strategy for trajectory tracking of a Delta parallel robot, using a hybrid optimization algorithm of Particle Swarm Optimization (PSO) and differential evolution (DE). It differs from previous works that they propose robust PID controller parameters tuning based on conventional gradient-based optimization algorithms and apply them to process control. First, we reduce the tuning problem of a robust PID controller with CTC strategy satisfying requirements including robustness and disturbance attenuation to an optimization problem with nonlinear constraints by considering the nonlinear dynamic model of a Delta parallel robot. Second, we set up the design characteristics for the trajectory tracking of a Delta parallel robot. Then, we propose a robust PID controller in a way of obtaining the global optimization solution of the nonlinear optimization problem by running a PSO-DE hybrid optimization algorithm of finding the global optimal solution by maintaining the diversity of swarm during evolution based on the evolution of cognitive experience. Simulation and experimental results demonstrate that the proposed controller outperforms previous works with respect to robust performance and active disturbance attenuation when it is applied to tracking control of a Delta parallel robot.

Passenger Evacuation Path Planning in Subway Station Under Multiple Fires Based on Multiobjective Robust Optimization

The optimization of passenger evacuation path in subway station under emergencies directly affects the evacuation efficiency. Aiming at the evacuation scenario of passengers in subway station under multiple fires, a path planning method based on multi-objective robust optimization is established in this paper. The shortest total evacuation time, the minimum total risk and the minimum total congestion cost are taken as the objectives where the robustness of travel time and risk is also considered. NSGA-II algorithm is used to solve the model, the optimal Pareto solution under a certain robust control parameter is obtained according to the principle of minimizing the total cost function, and the overall optimization degree of route is obtained by using the evaluation index. Taking Qingdao May 4th Square subway station of China as a verification example, a fire diffusion model is built by using Pyrosim, and a passenger evacuation system is built by using Pathfinder. The multi-objective robust path optimization model of passenger evacuation is applied to the built passenger evacuation system under fires. The simulation results show that the overall optimization degree of route can reach 12.8%, when the time and risk robust control parameters are 30 and 30, respectively. The method can be used to guide the passenger evacuation in subway stations under multiple hazard sources and improve the safety index of passenger travel.

Neural network based adaptive sliding mode tracking control of autonomous surface vehicles with input quantization and saturation

The autonomous surface vehicle (ASV) is supposed to be able to adapt unknown environments without human interference. This paper investigates the problem of trajectory tracking control for ASVs in the present of parameter uncertainties, disturbances, input quantization and actuator saturation. The neural network (NN) approximation and an adaptive sliding mode control (SMC) strategy are combined for the ASVs with a priori unknown upper bound of parameter uncertainties, external disturbances and saturation errors. The NN is adopted to approximate the unknown continuous uncertainties and disturbances in the system. The adaptive law for the SMC parameter can guarantee no overestimation of the robust control parameter with respect to unknown upper bounds of the NN approximation errors and the saturation error. By adding the NN approximation into the adaptive SMC laws, the robust control parameter and the chattering can be reduced. Uniformly ultimately bounded (UUB) for sliding variables and tracking errors can be guaranteed by Lyapunov proof. Simulation examples are given to illustrate the improved performance with the proposed approach. The comparisons on the proposed control method with other algorithms are given by using the integrated absolute error (IAE) and integrated time absolute error (ITAE).

Inserting of heuristic techniques into the stability regions for multiarea load frequency control systems with time delays

The design and optimization of robust controller parameters are required to improve the controller performances and to keep the stability of load frequency control (LFC) system. In addition, reducing the number of iterations and computational time is very important for swiftly tuning of the controller parameters and the system to reach stability rapidly. For this purpose, this study presents the inserting of heuristic optimization techniques into stability regions method identified in proportional-integral (PI) controllers space for multiarea LFC systems with communication time delays (CTDs). This method consists of two steps: determination of stability region for the system and application of heuristics. Stability region for the system is found via stability boundary locus (SBL) and moth-flame optimization (MFO), particle swarm optimization (PSO), sine cosine algorithm (SCA), slime mould algorithm (SMA) and whale optimization algorithm (WOA) are inserted and applied to this region. In addition, a cost function having time domain specifications is developed for improving the performances of LFC and it is compared with the well-known integral error functions. Also, the robust stability region, which tolerates any system parameter and any time delay variation, is identified and the significance of this region is given for robustness analysis. It is observed from the analyses that better system outputs have been obtained with developed cost function. Steady state errors are minimized and transient state performances are improved with the proposed method. Moreover, desired system performances have been achieved with lower computational time and iteration number (approximately more than about 89% reduced according to classical approach) without deteriorating the stable structure of the system by the proposed method.

Target encirclement of moving ride-hailing vehicle under uncertain environment: A multi-vehicle mutual rescue model

To solve the emergency rescue of driver-hijacking-passengers event, this paper proposes a multi-vehicle mutual rescue (MMR) model for ride-hailing platform to solve the emergency rescue of driver-hijacking-passengers event. Firstly, the upper model is constructed minimize the movable link lengths of the target vehicle, the path risk of rescue vehicles according to robust optimization theory. Secondly, the lower model is formed by the user equilibrium (UE) model for traffic assignment problems by using mutual and positioning information. Thirdly, the strength Pareto evolutionary algorithm (SPEA Ⅱ), the Frank-Wolfe algorithm, and the method of two-stage coding are comprehensively applied in this paper for the NP-hard problem of the MMR model. Finally, the mutual information and O-D flows of links on the 4th Ring Road of Beijing are utilized to examine the MMR model, and analyzed the utility of the hybrid algorithm and the MMR model. The experimental result indicates that when the robust control parameters are set to 0, 30, and 60, then 3, 2, and 3 groups of the MMR schemes with different sensitivity to the uncertain environment can be obtained. The algorithm utility analysis results indicate that the hybrid algorithm with intimacy calculation can significantly enhance the convergence of the algorithm. Compared with the traditional convex hull algorithm, the model utility analysis results indicate that, in addition to effectively making up for the application defects of the traditional algorithm, the MMR model adopts the mutual rescue strategy that is superior to the traditional algorithm in the Target encirclement nodes selection of target vehicle, the paths planning of rescue vehicles, the collaborative scheduling of ride-hailing platform, and can well complete the mutual rescue task under an uncertain environment for driver-hijacking-passengers event.

Fault Diagnostics and Tolerance Analysis of a Microgrid System Using Hamilton-Jacobi-Isaacs Equation and Game Theoretic Estimations in Sliding Mode Observers.

This paper focuses on robustness and sensitivity analysis for sensor fault diagnosis of a voltage source converter based microgrid model. It uses robust control parameters such as minimum sensitivity parameter , maximum robustness parameter , and compromised both , being incorporated in the sliding mode observer theory using the game theoretic saddle point estimation achieved through convex optimization of constrained LMIs. The approach used works in a way that the mentioned robust control parameters are embedded in Hamilton–Jacobi–Isaacs-Equation (HJIE) and are also used to determine the inequality version of HJIE, which is, in terms of the Lyapunov function, faults/disturbances and augmented state/output estimation error as its variables. The stability analysis is also presented by negative definiteness of the same inequality version of HJIE, and additionally, it also gives linear matrix inequalities (LMIs), which are optimized using iterative convex optimization algorithms to give optimal sliding mode observer gains enhanced with robustness to maximal preset values of disturbances and sensitivity to minimal preset values of faults. The enhanced sliding mode observer is used to estimate states, faults, and disturbances using sliding mode observer theory. The optimality of sliding mode observer gains for sensitivity of the observer to minimal faults and robustness to maximal disturbance is a game theoretic saddle point estimation achieved through convex optimization of LMIs. The paper includes results for state estimation errors, faults’ estimation/reconstruction, fault estimation errors, and fault-tolerant-control performance for current and potential transformer faults. The considered faults and disturbances in current and potential transformers are sinusoidal nature composite of magnitude/phase/harmonics at the same time.

Parametric Robust Generalized Minimum Variance Control

This work presents a hybridization of the Generalized Minimum Variance control (GMVC) with Parametric Robust Control (RPC) for Linear Time Invariant (LTI) Uncertain Systems. A linear parametric uncertainties model is used to describe the system dynamic behavior. The controller synthesis is based on PID controller with a low-pass filter and formulated as a convex optimization problem which takes in account the desired closed-loop performance and the uncertainties on the model parameters. Then, the robust controller parameters, that represent the best solution for the all possible models and assign the closed-loop poles within the desired region in the s-plane, are transferred to GMVC by Tustin’s method, resulting in a parametric robust generalized minimum variance (PRGMV) controller, which was compared to two other approaches, carrying out several simulation essays in Matlab environment. In order to emphasize its performances and its efficiency, the performance index and sensitivity analysis were assessed. The results confirmed that the proposed controller ensures better robustness and performance for reference tracking and disturbance rejection.

High-Frequency Vibration Control of Rotating Beam Based on Trailing-edge Flap

This study is to investigate an active control method, based on trailing-edge flap, in high-frequency vibration control of rotating blade beam. The structure is modeled as composite blade beam with circumferentially asymmetric stiffness configuration, which is based on analysis of the elastic flap-wise/twist displacements and incorporates the angle control of trailing-edge flap driven by a stepping motor. Aerodynamic expressions are based on a novel quasi-steady model suitable for trailing-edge flap. Based on a strip theory, the multi-body dynamic aeroelastic system is analyzed and solved. The active control is realized by Hœ algorithm using linear matrix inequality (LMI) design. This study is to investigate time-domain stability analysis and robust control method, realize displacement response analysis and robust performance analysis, and input signal display of trailing-edge flap angle. The optimization mechanism of LMI is to optimize uncertain robust performance parameters based on the selection of robust control parameters, so that the controlled displacement and control input are kept within reasonable ranges. The reliability and robustness of H» control algorithm based on LMI are verified by comparisons of the results of high-frequency vibration control using different robust performance parameters.

Comprehensive small-signal stability analysis for master hybrid MMC connected with strong and weak AC systems

The small-signal stability problem of power electronic converters has always been a critical concern for safe operation. This paper conducts a comprehensive analysis of the small-signal stability for the hybrid modular multilevel converter (MMC), which is promising in the application of future over-head-line high-voltage-direct-current transmission. The impacts of the strong and weak AC system connection are comparatively researched through eigenvalue-based analysis. The newly emerging factors that hamper the stability are identified for the weak AC system integration. The work in this paper provides valuable guidance for robust control parameter configuration of the hybrid MMC.

Improved hybrid inexact optimal scheduling of virtual powerplant (VPP) for zero-carbon multi-energy system (ZCMES) incorporating Electric Vehicle (EV) multi-flexible approach

This study presents a novel day-ahead optimal scheduling for a Virtual Power Plant (VPP) to enable optimal dispatch of Zero-Carbon Multi-Energy System (ZCMES) and EV multi-flexible potentials and the influence of uncertainties under various scenarios. The Latin Hypercube Sampling (LHS) method is used to quantify the uncertainties, while the multi-energy demand randomness is further analyzed using a robust approach, and a robust-stochastic optimization approach is developed to solve the mathematical problem. The obtained simulation results indicate that for a specified EV flexibility (e.g vehicle-to-electricity use V2EU, vehicle-to-heat use V2HU, or vehicle-to-cooling use V2CU), the VPP optimally dispatches the EV flexibility to offset part of the specified energy demand. With the proposed approach, the EV flexibility is scheduled to be used for electricity and cooling purpose without grid interaction while the charging-discharging behavior of the EVs in the clusters is optimally scheduled. Also, the preferred charging station standard is either IEC61851 Mode 2 or IEC62196 Mode 3 with 45A current rating for full EV multi-flexible usage. Moreover, the multi-flexible approach provides both economic and technical benefits by reducing the overall cost by 8.5%, high EV flexibility ratio (0.54), and reduced the technical stress on energy storage by smoothing the state of charge (SOC) level through discharge behavior regulation. Finally, it was observed that the multi-flexible performance is influenced by the magnitude of multi-energy demand and generation, charging station standards, EV parameters, and robust control parameters. Thus, such a great feasibility analysis of EV multi-flexible potential provides valuable policy and planning reference tools for the energy stakeholders towards energy carbon neutrality in urban areas.