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This paper proposes an optimal controller and estimator for energy dispatch to balance the power supply and demand considering communication delays. The proposed algorithm involves modeling an autonomous nanogrid (ANG) consisting of distributed energy resources, energy storage systems, loads, an $H_{\infty }$ controller with a reference power modulation technique, and a state estimator. The ANG was developed to express the dynamic supply-demand energy balance of a nanogird system. Reference power modulation was designed to generate the desired ESS power based on the imbalanced energy. Random communication delays were modeled using a stochastic variable satisfying the Bernoulli random binary distribution. The optimal $H_{\infty }$ controller and estimator were developed using a linear matrix inequality approach to exponentially stabilize the closed-loop system. Simulations were performed using real daily demand forecasts obtained from the Korea Meteorological Administration to demonstrate the effectiveness of the proposed real-time optimization algorithm.

The increase in ${R} _{DUT}$ , the on-state resistance of power semiconductor devices, is used to estimate the remaining useful life (RUL) of the devices. Conventional online measurement of $R_{DUT}$ involves on-state voltage measurement of the on-state device, which is easily polluted by switching noise to limit the measurement accuracy. This paper proposes a new noise-robust method to measure the increment of $R_{DUT}$ by inductor current and input/output DC voltage of DC-DC converters. The proposed method features higher accuracy in predicting RUL than the conventional one because the inductor current is immune to switching noise. The aging of MOSFET is emulated in this paper by adding a series resistor $R_{ext}$ . Experiments showed that the resistance increment measured by the proposed method is up to 99.2% accurate to value of $R_{ext}$ .

This paper proposes a gradient-descent-based learning (GL) gain for backstepping controller and disturbance observer (DOB) of nonlinear system. The proposed method consists of the GL gain update law, controller, and DOB. The GL gain update law is proposed to adapt the control gain and DOB gain according to the direction that minimizes the cost function. The mathematical analysis reveals that the GL gain always has a positive sign and upper bound. The controller is designed via a backstepping procedure to track the desired output with GL control gain. The DOB is designed to estimate the unknown external disturbance with the GL DOB gain. Because the control and DOB gains are simultaneously tuned to achieve improved performance, the time consumption for tuning can be reduced. In addition, the peaking phenomenon can be avoided initially by a small initial value of GL gains. The stability of the closed-loop system is guaranteed using the input-to-state stability property. The performance of the proposed method was validated via simulations and experiments using a DC motor.

Trajectory prediction is gaining attention as a form of situational awareness because it is an essential component of the support system of autonomous driving, particularly in urban areas. A promising application is cooperative driving automation, where the traffic scene is monitored by roadside sensors with undisrupted views. A critical problem is that these sensors are adversely affected by inclement weather, including drenching rain or large amounts of snow, in which case the reliability of the prediction results can be significantly compromised. To address these problems, this study proposes a framework for robust vehicle-trajectory predictions based on the Chebyshev transform. In the proposed framework, the original trajectory snippets (partial trajectories) are Chebyshev-transformed, and the resulting coefficients form new snippets. The LSTM (long-short term memory) encoder-decoder structure was trained and tested using these new coefficient snippets, which were extracted from a public vehicle trajectory dataset. The performance and robustness of the proposed framework were verified by emulating sensor data that were incomplete as a result of environmental factors. The proposed framework provides stable and accurate long-term trajectory prediction because the Chebyshev transform is robust to incomplete sensor data by virtue of its uniform nature.

This paper proposes a cascaded backstepping control method with an augmented observer for the lateral control of an autonomous vehicle. The proposed cascaded backstepping control structure consists of an inner-loop electric power steering (EPS) system and an outer-loop lane-keeping system (LKS). The outer-loop controller for LKS calculates and provides the desired steering angle to the inner-loop EPS system for maintaining the vehicle at the center of the lane. Subsequently, the inner-loop controller for the EPS system generates steering torque to track the desired steering angle from the outer loop. The proposed method can guarantee the stability of the vehicle considering both inner-loop and outer-loop dynamics. In addition, an augmented observer affords robustness against unknown model parameters and external disturbances. The stability of the closed loop of the overall system, including the lateral dynamics and the EPS system, is proven using the input-to-state stable property. Controller design with consideration of the overall system can improve the lateral control performance.

As the motion of pedestrians is largely unpredictable, situational awareness presents a challenge for safe autonomous driving in urban areas. In particular, conventional sensor information about the dynamic states involved in determining and predicting pedestrian motion, including the walking speed, is significantly affected by latency when pedestrians suddenly increase their pace. In this paper, we propose a framework for predicting the steady-state walking speed of sudden pedestrian movement at the early stage of walking after heel-off. Based on the analysis that some motion cues during gait initiation are related to the steady-state walking speed, a fuzzy inference framework for predicting the steady-state walking speed, where the related motion cues are input to the inference model, is developed. The proposed framework can accurately predict the steady-state walking speed, even at the end of the first gait cycle. Moreover, the future trajectory of the pedestrian can be predicted using the piecewise linear speed model. Using the proposed framework, installed on the edge server of the cooperative-intelligent transportation system (C-ITS), this study aims to ensure the safety of autonomous vehicles by enabling them to successfully navigate the danger caused by sudden pedestrian movement. Experimental results obtained from testing the system at a real urban intersection verify the value offered by the proposed framework.

Low-voltage ride-through (LVRT) is important for the grid integration of wind power systems (WPSs). Each grid operator defines a grid code, including the LVRT requirement, considering their power system characteristics. During grid faults, LVRT is needed for the protection of WPS because the over current can induce damage to the power converter system. An important issue in the LVRT method of WPS is the energy capacity because sufficient reserve energy capacities of wind turbine inertia and an energy storage system (ESS) are needed for a successful LVRT operation. Although ESS can store excess energy of the WPS during a grid fault, it is physically limited considering its energy capacity. Moreover, WPS has a low reserve energy capacity, especially when the WPS operates in high wind speed conditions. Thus, we propose the deloaded operation of WPS for the preparation of LVRT by reducing the power generation of WPS. The effectiveness of the proposed method is validated by modeling a WT and an ESS topologically and performing simulations using the MATLAB/Simulink SimPowerSystems toolbox.

This paper proposes a super twisting-based nonlinear gain sliding mode controller (STNGSMC) to achieve the position control of permanent-magnet synchronous motors (PMSMs). Nonlinear gain is developed to improve the position tracking performance of a super twisting sliding mode controller (STSMC). The inclusion of nonlinear gain in the STSMC reduces chattering, and the stability of the closed loop is mathematically proven using the Lyapunov theorem considering load torque. In the proposed method, chattering is analyzed using the describing function method under unmodeled dynamics, such as that corresponding to the quantization effect of the digital sensor, sensor resolution, and pulse-width modulation (PWM) switching noise, in PMSM position control systems. Consequently, the STNGSMC can improve the position tracking performance in steady-state responses. The performance of the proposed method is verified using simulations. The experimental results demonstrate that chattering can be reduced by the STNGSMC, consequently improving the position tracking performance.

In this study, a velocity control method is proposed for minimizing velocity ripples in permanent-magnet synchronous motors with low-performance current sensing. The proposed method involves the use of desired currents, peak estimation, and a proposed tracking controller. The desired currents are used to achieve velocity control with only the current sensing. Peak estimation is performed to estimate the peak of the measured distorted current that occurs due to low-performance sensing. The proposed tracking controller is developed based on a proportional-integral controller using the estimated peak obtained from the measured currents. The proposed method aids in reducing velocity ripples and improving velocity tracking performance of permanent-magnet synchronous motors. The primary contribution of this method is the improvement of velocity-control performance with low-performance current sensing. The stability of the closed loop is proven by employing the input-to-state stable property. To verify the effectiveness of the proposed method, we performed simulations and experiments with different three cases.

We propose an extended-state-observer (ESO)-based robust position tracking control method using nonlinear damping gain to improve the control performance under external disturbances and parameter uncertainties for quadrotors. The proposed method consists of an ESO and a nonlinear damping controller (NDC). The ESO is designed to estimate full state and disturbance. The external disturbance, velocity dynamics, and the uncertainty of the input parameter are lumped in the disturbance. The NDC is developed via backstepping procedure to suppress the output tracking error according to the disturbance estimation error. The proposed method is simple and robust against external disturbance and parameter uncertainties. In addition, only the nominal value of the input gain parameters are required. The closed-loop stability is proven by using the input-to-state stability property. The position tracking performance of proposed method was verified by performing hardware-in-the-loop simulations using a quadrotor platform.

Coordinated control methods involving a wind turbine (WT) and an energy storage system (ESS) have been proposed to meet several objectives, such as smoothing wind power (WP) fluctuations, shaving peaks, enabling power scheduling, and allowing low-voltage ride-through (LVRT). LVRT requirement is defined by grid operators, and it should be satisfied whenever grid faults occur. Several methodologies have been proposed for the LVRT both with or without the use of an ESS. Furthermore, using an ESS is more advantageous for several WP applications. By using an ESS, WTs can be operated in a more economic and reliable way. However, the installation cost of an ESS is high and it has operation range constraints for charging and discharging. Moreover, the WT operation condition and ESS state-of-charge (SoC) can be different when a grid fault occurs. Therefore, it is necessary to coordinate both units, WT and ESS, for reliable and economic operation during a grid fault. Thus, we propose a coordinated fuzzy-based LVRT method that considers the different operation conditions of a WT and an ESS. From the proposed method, the effective reference powers of a WT and an ESS are evaluated by considering the rotor speed and SoC in the fuzzy control algorithm. The effectiveness of the proposed method is validated by considering two case studies on ESS SoC and WT rotor speed violations.

Low-voltage ride-through (LVRT) requirements are defined by grid operators, and they vary based on power system characteristics. Coordinated LVRT control methods have been proposed for wind turbines (WTs) and energy storage systems (ESSs). ESSs can successfully help achieve LVRT by regulating DC-link voltage during a grid fault. During LVRT, WTs cannot transfer power to a grid because of their low voltage and current limit. Moreover, as grid operators typically require reactive power support during a grid fault, active power cannot be properly transferred to the grid. This results in fluctuation in the DC-link voltage in wind power generators and it can induce significant damage in the systems. ESS have been used for achieving better LVRT response to protect WT systems and meet LVRT grid requirements. Previous coordinated control methods have mainly focused on DC-link voltage regulation based on ESS charging and discharging control. As ESSs have high installation cost and limited charging capacity, it is better to coordinate the LVRT response properly considering the state of charge of ESSs and the rotor speed and pitch angle of WTs. In this work, an enhanced coordinated LVRT control method is proposed based on a fuzzy-logic algorithm. Fixed torque of the rotor control and a fixed ramp rate of the pitch control are employed for power analysis which is used in formulating fuzzy-logic controller. The effectiveness of the proposed method is validated by modeling a WT and an ESS topologically and performing simulations using the MATLAB/Simulink SimPowerSystems toolbox.

An adaptive nonlinear output tracking control method is proposed to reject unmatched biased sinusoidal disturbances for nonlinear systems that are in the strict feedback form. The proposed method consists of a disturbance observer (DOB) and an adaptive nonlinear controller. The DOB is in the form of a high-pass filter, and it estimates biased sinusoidal disturbances with unknown frequencies. The performance of the DOB is investigated in the time and frequency domains. As the DOB cannot accurately estimate biased sinusoidal disturbances with unknown frequencies, an adaptive nonlinear controller is designed to compensate for the disturbance estimation errors and ensure output tracking. The adaptive nonlinear controller is developed via the backstepping procedure. Thus, matching conditions are not significant in this design. The discontinuous “sgn” function is applied to the controller to compensate for the disturbance estimation errors. An update law of the control gain of the discontinuous “sgn” function is proposed to suppress the disturbance estimation errors without the knowledge of their upper bounds. Low-pass filters are embedded in the controller to smooth the derivative of the discontinuous “sgn” function. Consequently, multiple unmatched biased sinusoidal disturbances are compensated by the proposed method without any knowledge of the disturbances. The proposed method can reject multiple biased sinusoidal disturbances while it guarantees the constraint of the tracking error in steady-state response. Furthermore, the proposed method can reject multiple biased sinusoidal disturbances.