Proposed Control Scheme Research Articles

Control strategies applied to distribution networks with the integration of distributed energy resources - A review

The continuous integration of distributed energy resources (DERs) in power systems has opened the need to propose control strategies that tend to mitigate the possible impacts that an unlimited integration can produce. Authors have presented studies that present different control strategies that take advantage of the devices already present in the network or of new elements added, such as DERs. Also, different control architectures that can take full advantage of the capabilities that DERs can offer are studied. Therefore, this article focuses on reviewing the recent literature related to control strategies and architectures applied to distribution networks with DERs as protagonists. The control strategies that exploit the characteristics of DERs to perform control actions are described, and the control architectures adapted to the integration of DERs in distribution networks are detailed. Device modeling approaches are also mentioned, considering that some DERs depend on random variables and can introduce uncertainty in the analyses if their behavior is not modeled correctly. Finally, current and future trends in this area are discussed. Keywords. Control architectures, control strategies, deterministic approach, Distributed Energy Resources, stochastic approach.

Adaptive Robust Control of Uncertain Euler-Lagrange Systems Using Gaussian Processes.

This article proposes a novel adaptive robust control approach based on Gaussian processes (GPs) for the high-precision tracking problem of uncertain Euler-Lagrange (EL) systems with time-varying external disturbances. Given a prior dynamic model, the GP regression (GPR) technique is employed to obtain a nonparametric data-based uncertainty model, including its probabilistic confidence intervals. Based on the adaptive sliding mode control (ASMC) framework, the posterior means of GPs are utilized for dynamic compensation, whereas the posterior variances are applied to adjust the feedback gains. This proposed control strategy is robust against significant system uncertainty with low feedback gains. A novel adaptive law for updating hyperparameters based on tracking error feedback is presented, thereby improving the performance of both tracking control and GP modeling simultaneously. Compared to existing likelihood-based optimization methods, this hyperparameter adaptive law enables data-efficient and fast uncertainty learning for control applications. The proposed control strategy guarantees the semiglobal asymptotic convergence to zero tracking error with a specified probability. Simulations using an underwater robot model demonstrate that the utilization of GPs and hyperparameter adaptive law significantly improves the performance of tracking control and uncertainty learning.

Distributed Optimal Attitude Synchronization Control of Multiple QUAVs via Adaptive Dynamic Programming.

This article proposes a distributed optimal attitude synchronization control strategy for multiple quadrotor unmanned aerial vehicles (QUAVs) through the adaptive dynamic programming (ADP) algorithm. The attitude systems of QUAVs are modeled as affine nominal systems subject to parameter uncertainties and external disturbances. Considering attitude constraints in complex flying environments, a one-to-one mapping technique is utilized to transform the constrained systems into equivalent unconstrained systems. An improved nonquadratic cost function is constructed for each QUAV, which reflects the requirements of robustness and the constraints of control input simultaneously. To overcome the issue that the persistence of excitation (PE) condition is difficult to meet, a novel tuning rule of critic neural network (NN) weights is developed via the concurrent learning (CL) technique. In terms of the Lyapunov stability theorem, the stability of the closed-loop system and the convergence of critic NN weights are proved. Finally, simulation results on multiple QUAVs show the effectiveness of the proposed control strategy.

Fault-Tolerant Control of Stochastic High-Order Fully Actuated Systems.

In recent years, high-order fully actuated (HOFA) systems, founded by Prof. GR Duan, have recorded rapid progress for deterministic systems. However, the control issue of stochastic fully actuated systems is still an open problem. This study develops a novel stochastic HOFA system model that complements the existing HOFA methodology. Notably, stochastic signals can be considered in the proposed model, different from the case in the deterministic model. By adopting a high-order operator, equivalent control and stabilization control laws are realized to guarantee the global asymptotic stability in probability of the closed-loop system. For the system with sensor gain faults, an observer-based fault-tolerant control law is designed. Finally, the simulation results validate the effectiveness of the proposed control schemes.

Decentralized Implicit Inverse Control for Large-Scale Hysteretic Nonlinear Time-Delay Systems and Its Application on Triple-Axis Giant Magnetostrictive Actuators.

This article proposes fuzzy-logic systems (FLSs)-based decentralized adaptive implicit inverse control scheme for a class of large-scale nonlinear systems with time delays and multihysteretic loops. Our novel algorithms feature hysteretic implicit inverse compensators designed to effectively mitigate multihysteretic loops in large-scale systems. In this article, hysteretic implicit inverse compensators can replace the traditional hysteretic inverse models, which are exceedingly difficult to construct, and no longer necessary. The authors provide three contributions: 1) a searching mechanism to obtain the approximate value of the practical input signal from the so-called hysteretic temporary control law; 2) the arbitrarily small L∞ norm of the tracking error attained by utilizing the proposed initializing technique, which applies the combination of FLSs and a finite covering lemma to deal with time delays; and 3) the construction of a triple-axis giant magnetostrictive motion control platform, which validates the effectiveness of the proposed control scheme and algorithms.

Resilient Output Containment Control of Heterogeneous Multiagent Systems Against Composite Attacks: A Digital Twin Approach.

This article delves into the distributed resilient output containment control of heterogeneous multiagent systems against composite attacks, including Denial-of-Service (DoS) attacks, false-data injection (FDI) attacks, camouflage attacks, and actuation attacks. Inspired by digital twin technology, a twin layer (TL) with higher security and privacy is employed to decouple the above problem into two tasks: 1) defense protocols against DoS attacks on TL and 2) defense protocols against actuation attacks on the cyber-physical layer (CPL). Initially, considering modeling errors of leader dynamics, distributed observers are introduced to reconstruct the leader dynamics for each follower on TL under DoS attacks. Subsequently, distributed estimators are utilized to estimate follower states based on the reconstructed leader dynamics on the TL. Then, decentralized solvers are designed to calculate the output regulator equations on CPL by using the reconstructed leader dynamics. Simultaneously, decentralized adaptive attack-resilient control schemes are proposed to resist unbounded actuation attacks on the CPL. Furthermore, the aforementioned control protocols are applied to demonstrate that the followers can achieve uniformly ultimately bounded (UUB) convergence, with the upper bound of the UUB convergence being explicitly determined. Finally, we present a simulation example and an experiment to show the effectiveness of the proposed control scheme.

Event-Triggered Output-Feedback Adaptive Control of Interconnected Nonlinear Systems: A Cyclic-Small-Gain Approach.

This article proposes a novel output-feedback event-triggered control protocol for a class of interconnected parametric nonlinear systems. Different from most existing works where event-triggering mechanisms are considered for only the controller-to-actuator channel or the sensor-to-controller channel, this work adopts event-triggering mechanisms for both channels as well as adaptive laws. An adaptive states observer is designed to estimate the unavailable system state and then a novel adaptive event-triggered controller is proposed based on the celebrated backstepping technique. By utilizing the cyclic small-gain theorem and Lyapunov theory, it is shown that the proposed control scheme ensures that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), and the Zeno behavior is successfully excluded. Finally, a practical example is provided to illustrate the effectiveness and advantages of the proposed approach.

Harmonic State-Space Modeling and Closed-Loop Control of Single-Stage High-Frequency Isolated DC–AC Converter

The port harmonic features of the single-phase single-stage high-frequency isolated dc-ac converter is not analyzed so far and the corresponding theoretical basis for improving the current quality is still a research gap. In this paper, a linear model in frequency domain of the single-stage high-frequency isolated dc-ac converter is originally derived based on the combined harmonic state space and extended description function modeling technique. The systematic modeling process is analyzed and the accuracy of the modeling is verified by the simulation results. Combined with the model analysis, the harmonic coupling characteristics between the ac side and the dc side are clearly presented. On this basis, this paper proposes a closed-loop control strategy to reduce the decrease of the dc-side and ac-side current quality caused by the harmonic coupling at the control level. Furthermore, this paper constructs the compound controllers based on the P-type iterative learning to suppress the harmonic currents of the dc side and the ac side, respectively. The composite controller does not distinguish the harmonic frequency. Hence, the proposed controller achieves the effective suppression of the harmonic currents in real time. The effectiveness and feasibility of the proposed control strategy are verified by the experiments.

An Adaptive Extended Kalman Filter With Passivity-Based Control for DC-DC Converter in DC Microgrids Supplying Constant Power Loads

This paper proposes an adaptive nonlinear control strategy to eliminate the instability problem of buck converters in DC microgrids (DCMG) considering constant power load (CPL). The strategy includes an Adaptive Passivity-Based Controller (PBC) and an Adaptive Extended Kalman Filter (AEKF). The proposed AEKF performs faster and more accurately than the conventional EKF and adjusts the covariance matrices adaptively. Furthermore, this AEKF directly estimates the unknown CPL power supplied by the converter and prevents the number of measurement sensors from increasing. Hence, PBC ensures system stability alongside AEKF. The controller guarantees Lyapunov stability and fast dynamic response in the presence of uncertainties and perturbations compared to other nonlinear methods. MATLAB simulations and laboratory implementation are provided to confirm the performance and robustness of the proposed control strategy and validate the analysis.

Practical Finite-Time Command-Filtered Adaptive Backstepping With Its Applications to Quadrotor Hovers.

In this article, a practical finite-time command-filtered adaptive backstepping (PFTCFAB) control method is presented for a class of uncertain nonlinear systems with nonparametric unknown nonlinearities and external disturbances. Unlike PFTCFAB control techniques that use neural networks (NNs) or fuzzy-logic systems (FLSs) to deal with system uncertainties, the proposed method is capable of handling such uncertainties without the need for NNs or FLSs, thus reducing complexity and increasing reliability. In the proposed approach, novel function adaptive laws are designed to directly estimate unknown nonparametric nonlinearities and external disturbances by means of command filter techniques, and a type of practical finite-time command filters is proposed to obtain such laws. Moreover, the PFTCFAB controllers and finite-time command filters are designed with practical finite-time Lyapunov stability, which ensures finite-time stability of system tracking and filter estimation errors. Experimental results with a quadrotor hover system are presented and discussed to demonstrate the advantages and effectiveness of the proposed control strategy.

A Reinforcement Learning-Based Pantograph Control Strategy for Improving Current Collection Quality in High-Speed Railways.

In high-speed railways, the pantograph-catenary system (PCS) is a critical subsystem of the train power supply system. In particular, when the double-PCS (DPCS) is in operation, the passing of the leading pantograph (LP) causes the contact force of the trailing pantograph (TP) to fluctuate violently, affecting the power collection quality of the electric multiple units (EMUs). The actively controlled pantograph is the most promising technique for reducing the pantograph-catenary contact force (PCCF) fluctuation and improving the current collection quality. Based on the Nash equilibrium framework, this study proposes a multiagent reinforcement learning (MARL) algorithm for active pantograph control called cooperative proximity policy optimization (Coo-PPO). In the algorithm implementation, the heterogeneous agents play a unique role in a cooperative environment guided by the global value function. Then, a novel reward propagation channel is proposed to reveal implicit associations between agents. Furthermore, a curriculum learning approach is adopted to strike a balance between reward maximization and rational movement patterns. An existing MARL algorithm and a traditional control strategy are compared in the same scenario to validate the proposed control strategy's performance. The experimental results show that the Coo-PPO algorithm obtains more rewards, significantly suppresses the fluctuation in PCCF (up to 41.55%), and dramatically decreases the TP's offline rate (up to 10.77%). This study adopts MARL technology for the first time to address the coordinated control of double pantographs in DPCS.

Robust Adaptive Control Based on Incremental Nonlinear Dynamic Inversion for a Quadrotor in Presence of Partial Actuator Fault

This paper presents a novel nonlinear robust adaptive trajectory tracking control architecture for stabilizing and controlling a quadrotor in the presence of actuator partial faults. The proposed control strategy utilizes an Incremental Nonlinear Dynamic Inversion (INDI) algorithm as the baseline controller in the inner loop and augments a nonlinear model reference adaptive controller in the outer loop to ensure robustness against unmodeled faults. Additionally, a modified PID controller is introduced in the most outer-loop to track the desired path. The effects of actuator faults are modeled by considering sudden variations in motor thrust and torques. To enhance the control algorithm's robustness, a projection operator is employed in the robust adaptive structure. Comparative performance evaluations with a previous successful algorithm implemented on a quadrotor model demonstrate that the proposed controller achieves full controllability of the faulty quadrotor in pitch, roll, and yaw channels in the presence of actuator partial faults up to 50%.

Sliding Mode Control for Uncertain 2-D FMII Systems Under Stochastic Scheduling.

In this article, the sliding mode control (SMC) problem is addressed for two-dimensional (2-D) systems depicted by the second Fornasini-Marchesini (FMII) model. The communication from the controller to actuators is scheduled via a stochastic protocol modeled as Markov chain, by which only one controller node is permitted to transmit its data at each instant. A compensator for other unavailable controller nodes is introduced by means of previous transmitted signals at two most adjacent points. To characterize the features of 2-D FMII systems state recursion and stochastic scheduling protocol, a sliding function associated with the states at both the present and previous positions is constructed, and a scheduling signal-dependent SMC law is designed. By constructing token- and parameter-dependent Lyapunov functionals, both the reachability of the specified sliding surface and the uniform ultimate boundedness in the mean-square sense of the closed-loop system are analyzed and the corresponding sufficient conditions are derived. Furthermore, an optimization problem is formulated to minimize the convergent bound via searching desirable sliding matrices, meanwhile, a feasible solving procedure is provided by using the differential evolution algorithm. Finally, the proposed control scheme is further demonstrated via simulation results.

Neuro-Inspired Motion Control of a Soft Myriapod Robot

Centipedes exploit soft structures to move efficiently in complex environments. These abilities have promoted scientists to develop centipede-like robots with soft robot technologies. In this paper, a centipede-like robot constructed by multiple body segments connected in series is designed. Each body segment features a pair of antagonistic artificial muscles and four electro-adhesive feet, which plays the role of the basic motion unit of the centipede-like robot and can be regarded as a soft myriapod robot. To accomplish the desired movement tasks for the soft myriapod robot, this paper proposes a neuro-inspired hierarchical motion control scheme according to the functions of the centipede brain. In the proposed control scheme, a mushroom-body-inspired controller with an error-based learning mechanism is adopted for the soft actuator motion control, and a central-complex-inspired deep reinforcement learning algorithm is designed for the robot motion selection. At last, the effectiveness of the proposed motion control scheme is verified via both numerical studies and experiments.

Uncalibrated and Unmodeled Image-Based Visual Servoing of Robot Manipulators Using Zeroing Neural Networks.

Neural networks have been widely investigated for the control of robot manipulators and recurrent neural network (RNN) is accepted as a powerful tool for visual servoing. Different from existing control schemes for robot-camera systems, this article proposes a novel image-based visual servoing (IBVS) control scheme for both the regulation and tracking control of robot manipulators in the framework of a special class of RNN, termed zeroing neural network (ZNN), which does not require prior knowledge about camera configuration and kinematic model parameters. The proposed control scheme is composed of a data-driven mapping estimator and a controller, both of which are designed based on ZNN. To facilitate the deployment of the proposed IBVS control scheme, a discrete-time version of the proposed control scheme is developed. Theoretical analysis for the proposed method is presented in terms of convergence, stability, and robustness. In addition, simulations and experiments are carried out based on different types of robot-camera systems to verify the efficacy and portability of the proposed control scheme for solving regulation and trajectory IBVS problems. Moreover, comparative studies are performed to reveal the merits of the proposed control scheme.

Performance Evaluation of Single-Phase Boost-Type Cascaded H-Bridge Inverter in the Applications of Grid-Tied Photovoltaic Systems

In this paper, a new cascaded-H-bridge (CHB) inverter is proposed to enhance the input voltage of a renewable energy source to a desired level without any necessity of a dc-dc converter. The proposed topology consists of a pair of inductor and capacitor that serves as an input impedance network for the two full bridge inverters to boost the source voltage. An interleaved control scheme is proposed for the grid-tied inverter that independently supervises the duty cycle and the modulation index. The maximum power point tracking system commands the duty cycle whereas the dc-link voltage regulator maintains a constant voltage across the capacitors irrespective of the disturbances in the system. The proposed control scheme is primarily investigated in the MATLAB/SIMULINK platform. Different test cases are analysed to explore the performance of the proposed inverter in the applications of photovoltaic systems. An experimental prototype is designed in the laboratory to validate the simulation results.

A Sinusoidal Doubly Salient Electromagnetic Machine Drive Fed by Third-Harmonic Injection Two-Stage Matrix Converter With Integrated Injection Inductor

This manuscript studies a sinusoidal doubly salient electromagnetic machine (SDSEM) drive fed by third-harmonic injection two-stage matrix converter (3TSMC) with integrated injection inductor to improve the power density of the whole system. First, compared with the conventional matrix converter (MC), the 3TSMC suffers from larger volume due to the inductor in the harmonic current injection circuit. In the proposed system, the harmonic injection current is synthesized by the field current of SDSEM and the injection inductor is removed. Then, the control strategy is proposed to decouple the current injection control and field current control, which means that the harmonic current and the field current can be regulated flexibly. Finally, a prototype is built to evaluate the proposed system and control strategy. Furthermore, the proposed 3TSMC-fed drive with the corresponding control strategy is also suitable for the other DC-excited three-phase sinusoidal AC machines, such as wound rotor synchronous machines.

Reinforcement Learning-Based Switching Controller for a Milliscale Robot in a Constrained Environment

This work presents a reinforcement learning-based switching control mechanism to autonomously move a ferromagnetic object (representing a milliscale robot) around obstacles within a constrained environment in the presence of disturbances. This mechanism can be used to navigate objects (e.g., capsule endoscopy, swarms of drug particles) through complex environments when active control is a necessity but where direct manipulation can be hazardous. The proposed control scheme consists of a switching control architecture implemented by two sub-controllers. The first sub-controller is designed to employs the robot's inverse kinematic solutions to do an environment search of the to-be-carried ferromagnetic particle while being robust to disturbances. The second sub-controller uses a customized rainbow algorithm to control a robotic arm, i.e., the UR5 robot, to carry a ferromagnetic particle to a desired position through a constrained environment. For the customized Rainbow algorithm, Quantile Huber loss from the Implicit Quantile Networks (IQN) algorithm and ResNet are employed. The proposed controller is first trained and tested in a real-time physics simulation engine (PyBullet). Afterward, the trained controller is transferred to a UR5 robot to remotely transport a ferromagnetic particle in a real-world scenario to demonstrate the applicability of the proposed approach. The experimental results show an average success rate of 98.86\% calculated over 30 episodes for randomly generated trajectories.

Maximum power point tracking and space vector modulation control of quasi-z-source inverter for grid-connected photovoltaic systems

The quasi-Z-source inverter (qZSI) become one of the most promising power electronic converters for photovoltaic (PV) applications, due to its capability to perform a buck-boost conversion of the input voltage in a single stage. The control strategy based maximum power point tracking (MPPT) and proportional integral (PI) controller are well known in grid-connected with traditional configuration but not in qZSI. This paper presents a control strategy for qZSI grid-connected based on the MPPT algorithm and the linear control by PI controllers. This is complemented by the capability to efficiently transfer the harvested power to the grid, ensuring a unity power factor. The proposed control strategy effectively separates the control mechanisms for the direct current (DC) and alternating current (AC) sides by utilizing the two control variables, the shoot-through duty ratio and the modulation index. An adapted space vector modulation technique is then utilized to generate the switching pulse width modulation (PWM) signals, using these two control variables as inputs. The proposed approach was tested and validated under MATLAB/Simulink and PLECS software.

Disturbance Observer-Based Fixed-Time Tracking Control for a Tilt Trirotor Unmanned Aerial Vehicle

This paper proposed a new fixed-time trajectory tracking control strategy based on the disturbance observer (DOB) for a tilt trirotor unmanned aerial vehicle (TTUAV) which is subject to unknown external disturbances. To facilitate the TTUAV's trajectory tracking controller design, the dynamic model of the TTUAV is divided into two subsystems, the position subsystem (outer-loop) and the attitude subsystem (inner-loop). For the dynamics of the TTUAV's outer-loop, the unknown external disturbances consist of the unknown wind disturbances, the unknown aerodynamic frictions forces, the lateral force generated by the rear servo and the states coupling between the outer-loop and the inner-loop. Then, a nonlinear DOB is designed to estimate the unknown external disturbances, and a nonsingular terminal sliding-mode controller (NTSMC) is further developed to drive the TTUAV's position tracking errors to zero in a fixed time. For the dynamics of the TTUAV's inner-loop, the unknown external disturbances are mainly introduced by the aerodynamic frictions torques. Then, a modified sliding mode controller is designed to reject the unknown external disturbances, and to ensure that the attitude tracking errors converge to zero exponentially. In addition, a composite stability analysis based on Lyapunov stability theory is provided to guarantee the fixed-time convergence of the closed-loop dynamics of the TTUAV. Finally, the results of the real-time experiments are presented to verify the effectiveness of the proposed control strategy.