Efficient operation of integrated energy system (IES) is hampered by multicoupled uncertainties across the source-load-price. Existing studies often model isolated or partial uncertainties, lacking full-dimensional, systematic characterization. Model predictive control (MPC) and deep reinforcement learning (DRL) exhibit significant complementary potential, and their combination holds promise for revealing deep interconnections among multiuncertainties. This article is the first to explore the collaborative application of MPC and DRL in the IES field and proposes a novel TD3MPC framework. In this framework, MPC is embedded as an expert actor within the twin delayed deep deterministic policy gradient (TD3), providing safe, physics-compliant baseline actions. TD3 then employs data-driven exploration around this baseline to compensate for model mismatch and improve multiuncertainty responsiveness. To balance the learning and exploration of the intelligent agent, a hybrid prioritized experience replay mechanism is designed. Additionally, the conventional single-timestep state is extended to an augmented state space incorporating historical and future information, with state transitions modeled via probabilistic scenario techniques to reinforce TD3’s generalization ability for complex energy problems. Simulation results demonstrate that the proposed strategy exhibits strong robustness and reduces economic costs under multiuncertainty environments.

This paper presents a novel snake robot, JiAo, equipped with elliptical wheels that enable both wheeled and body-based locomotion. First, the design of each module of the snake robot is described, which consists of the body link and the transmission system of the elliptical wheels. Second, distinct control systems for wheeled and body-based locomotion are proposed. Finally, the prototype has been successfully developed and various experiments have been conducted, including crossing grasslands, crossing gaps, climbing slopes, navigating pipelines and climbing cylinders. In conclusion, JiAo demonstrates its versatility by effectively performing a wide range of tasks in various challenging scenarios.

With the aim to solve the secondary control problems for DC microgrids with cyber-attacks and limited bandwidth in the communication network, this article proposed a unified distributed resilient control strategy to achieve simultaneously appropriate voltage restoration and optimal power allocation (economic dispatch) by integrating dynamic event-triggered mechanism (DETM). First, based on dynamic average consensus protocol (DACP) and taking the DETM into consideration, the voltage regulator with distributed average voltage estimator and the PI controller is designed. Meanwhile, in order to maintain the economic operation of the microgrids under cyber-attacks, the power regulator with distributed optimal power controller and the PI controller is designed. Then, the sufficient conditions of convergence and optimality for the proposed distributed resilient average voltage estimator and optimal power controller are demonstrated by using the Lyapunov stability principle and convex optimization analysis, respectively. To this end, several case studies are conducted to validate the effectiveness of the proposed control strategy for enhancing the security, robustness, adaptability and economy of DC microgrids.

The fundamental cause of severe output fluctuations in photovoltaic (PV) power plants is the abrupt change in ground irradiance due to cloud cover. To enhance the accuracy of PV power forecasting under cloud cover conditions, this article proposes a second level power forecasting method for PV power plants based on an improved Pix2PixHD image restoration algorithm. First, the PV plant model is constructed based on the actual layout by applying Newton Raphson method. Second, the data characteristics of PV power output from the inverters are deeply explored to analyze the mapping relationship between PV power and irradiance. A virtual cloud image is constructed to represent the cloud cover (power loss) situation by describing the shape, thickness, and movement direction of the clouds. Subsequently, the virtual cloud images were preprocessed using the Canny edge detector, followed by restoration of the processed defective virtual cloud images using the Improved Pix2PixHD image restoration algorithm. Finally, a high-precision PV power forecasting at second level is achieved based on the linear relationship between PV power, irradiance, and pixel values of the virtual cloud image.

In this article, an optimized model predictive control (MPC) method with fixed switching frequency is proposed to eliminate the common-mode voltage (CMV) and balance the capacitor voltage. First, the zero CMV vector diagram is divided by finite control set MPC (FCS-MPC) to obtain the candidate vectors effectively. Then, in order to control the capacitor voltage and avoid the great computational burden originated from the redundant switching combinations, a piecewise control scheme is proposed to determine the required ones directly. The sequence generated by the selected vectors is further optimized for the purpose of saving switching transitions according to the internal and external parts of the vector diagram. Finally, the duty ratios are evaluated based on the results of the FCS-MPC in dividing sectors so as to avoid any extra complex calculation. The experimental evaluation has been carried out to validate the feasibility and effectiveness of the proposed method.

This article addresses the common-mode (CM) resonance problem in parallel inverters with separate dc inputs and improvedLCLfilters. Unlike single inverter system, parallel system presents a more challenging picture. Especially when the grid impedance and parasitic capacitance are considered, the parallel inverter interactions introduce more complex coupling relationships. In this situation, intricate CM resonances are excited at various frequencies, which is different from the resonance characteristics of a single inverter. Therefore, the control methods based on the single inverter system is insufficient in the parallel system and cannot behave as expected. To solve this issue, the CM resonance suppression problem in parallel system with separate inputs and parasitic parameters is being considered and analyzed for the first time. Firstly, the Thevenin's CM equivalent model of the parallel system is established. Compared with a single inverter model, the increase of CM loops triggers another two CM resonance peaks, which further deteriorates the system stability. Then, the internal and external CM excitations between each inverter are analyzed. The results show that the CM current of each inverter is coupled with the CM excitation sources of all inverters and the resonance characteristics are affected by the number of inverters. Furthermore, the influence of grid impedance and parasitic capacitance on the CM resonance characteristics of the system is analyzed. Accordingly, a CM resonance controller and a control parameter design method are proposed. By damping the CM excitation sources, the CM resonance suppression and CM current attenuation can be achieved simultaneously.

In parallel T-type three-level rectifiers system (PT23LRs), when the open-circuit fault (OCF) of neutral-point (NP) switches occurs, the existing zero-sequence circulating current (ZSCC) suppression methods are not capable of suppressing the ZSCC any more since the ZSCC model of PT23LRs is changed. Significantly, the ZSCC suppression issue of PT23LRs under the OCF of NP switches has not been researched in previous literatures. To overcome this limitation, a novel ZSCC suppression method for PT23LRs with the OCF of NP switches is proposed. First of all, according to the principle of area equivalence, the fault-tolerant control (FTC) is accomplished by reconstructing the switching sequence. Second, a novel ZSCC model with FTC is established. Then, according to the novel ZSCC model, the ZSCC control method based on “PI + feedforward” is proposed. This method is realized by modifying the turn-ontime of three-phase of fault rectifier in PT23LRs. Consequently, the method proposed in this article effectively restrains the ZSCC of PT23LRs with the OCF of NP switches. Finally, the simulation and experiment results verified the correctness of proposed method.

This article proposes a double vector model predictive control (MPC) method for the$LC$filtered three-level T-type inverter to gain superior voltage performance, which is current sensorless and computationally efficient. In the proposed scheme, inverter voltage increment is induced in the multiobjectives cost function, and the desired voltage is derived based on the simplified discrete model, which optimizes the output voltage control performance. The prepositive prediction calculation is shifted out from the evaluation circle, and the computation is lessened simultaneously. Then, aiming to estimate the capacitor current of the simplified discrete model with voltage feedback only, the strong-tracking Kalman filter-based observer is designed, which gets rid of the dependence on current sensors and lowers hardware cost. Furthermore, to improve the control accuracy of tracking the desired voltage, a two-layer decision algorithm based on the search tree is developed to find the optimal double vector easily, which obtains a low-complexity selection process using the univariate cost function. Finally, the simulation and experimental results are given to demonstrate the effectiveness of the theoretical analyses and the proposed method.

In transformerless three-level photovoltaic inverter systems, the modifiedLCfilter, which directly connects the dc-side neutral point to the common point of filter capacitors, is considered to be an ideal scheme to suppress the troublesome leakage current. However, a new common-mode (CM) resonant current is emerged, which leads to inverter-side current distortion and even system instability. Therefore, a second-order passivity-based controller in the time domain featuring excellent system stability is proposed for the CM resonant current mitigation. First, the reason for the CM resonant current generation with the modifiedLCfilter is analyzed in detail. Then, the CM current model is derived, which reveals that it is a second-order system. The passivity of this second-order model is strictly proved. Moreover, based on the passive control theory, zero state detectable theory, and Lyapunov stability theorem, a second-order passivity-based controller is designed step by step to suppress the CM resonant current, and the system global asymptotic stability is also proved. The proposed controller is based on the time domain, which is more intuitive and avoids the error amplification in the conversion from frequency domain to time domain. Finally, the effectiveness and the correctness of the theoretical derivation of the passivity-based control method are validated by experiments. Compared with the passive damping resistor method, the proposed method not only has better performance but also avoids the use of real resistors and reduces the cost by using software instead of hardware.

Alzheimer's disease (AD) is the main cause of dementia in the elderly. To date, it remains largely unknown whether and how dynamic characteristics of the functional networks differ from cognitively normal (CN) to AD. Here, we propose an AD dynamic network complexity intelligent detecting algorithm based on visibility graph. The focal regions that caused the dynamic abnormality of the connection mode were intelligently detected by creating a dynamic complexity network on the basis of the dynamic functional network. The results showed that the brain areas with different dynamic complexity gradually shifted from the frontal lobe to the temporal lobe and the occipital lobe. This was significantly related to the disorder of clinical patients from mood to memory and language. The increased dynamic complexity illustrates the compensatory effect of the brain area of AD lesions. In addition, the small-world topological properties of the dynamic complexity network have significant differences from CN to AD. To the best of our knowledge, this is the first time that such a concept is proposed. Our method of intelligently detecting the complexity of AD dynamic network provides new insights for understanding the internal dynamic mechanism of AD brain.

This article proposes a dynamic gain approach for investigating the leader-follower consensus problem of nonlinear multiagent systems with both distributed delays and discrete delays. It is assumed that each agent is in the strict-feedback form and satisfies the Lipschitz condition with an unknown constant. A novel dynamic gain observer is first constructed for each follower by only utilizing the output information of the follower and its neighbors. By introducing an observer, a distributed output-feedback control protocol is designed for each follower agent under a directed communication topology. It is proved by the Lyapunov-Razumikhin theorem that the consensus of the multiagent system is achieved with the proposed consensus protocol. Different from the existing results, the dynamic gain is constructed for each follower to propose the consensus protocol, without resorting to the celebrated backstepping technique. Moreover, the time-varying gain is designed in a uniform framework. The effectiveness of the proposed approaches is illustrated by a simulation example.

Modified LCL-type filter is adopted in parallel three-level inverters for high-frequency zero circulating current mitigation. However, it will generate internal resonance circulating current (IRCC) and external resonance circulating current (ERCC), which causes the output current unstable unavoidably. To address this issue, in this article, the model of IRCC and ERCC is established and analyzed. It reveals that the IRCC and ERCC are highly related to inverter-side zero sequence circulating current (ISZSCC) and common-mode voltage. Therefore, IRCC and ERCC can be suppressed by controlling the ISZSCC. To suppress the resonance currents, the ISZSCC model is first set up. Furthermore, a controller of ISZSCC is proposed and the parameters designing process of closed-loop controller are elaborated to stabilize the system based on the established model. Moreover, the resonance current suppression is achieved by adjusting the duty times of common-mode small vectors in space vector modulation. Thus, the current waveform quality and system stability are greatly improved after applying the proposed method. Finally, simulation and experiments results prove the effectiveness of theoretical analyses and proposed control strategy.

The constraints of communication channel resources and control energies always lie in real networks, which bring large error and cause adverse affects on system performance, ultimately. To address these limitations, in this article, a distributed event-triggered impulsive control strategy for the consensus problem of fractional-order multiagent networks (FOMANs) is investigated. By collecting the local instantaneous access information, the distributed tracking algorithm with a novel triggering condition is proposed, which is available for both linear and nonlinear systems. Combining with general quadratic Lyapunov stability method and discontinuous control theory, the sufficient conditions of consensus in terms of gain parameter and impulsive interval are derived. Furthermore, the minimal length of interevent is proven to be strictly greater than zero, which guarantees the nonexistence of Zeno-behavior in the closed-loop system. In addition, the implementation of the fractional order algorithm, the quantitative relationship between fractional order$\alpha $or parameter$a$in the triggering function, and the number of event-triggered impulsive instants are discussed. At the end of this article, two illustrative examples with numerical and circuit simulations are provided to validate the effectiveness of above results.

This article is concerned with the problem of distributed prescribed finite-time observer design for a strictfeedback nonlinear system with external disturbance. The purpose is to reconstruct the unavailable system state based on a group of distributed observers, where each of them can only receive at most 1-D output measurement from the system. First, in the absence of disturbance, a new distributed prescribed finite-time observer featuring time-varying gains is constructed and designed under the assumption of joint observability. It is analytically proved that for any prescribed instant independent of system initial conditions and other design parameters, the obtained distributed observer can guarantee not only the asymptotic convergence to zero of the state error between each observer state and the system state at this prescribed instant but also the definite zero-state error after this prescribed instant. Second, a distributed prescribed finite-time bounded observer is delicately proposed to account for the presence of external disturbance in the system dynamics. It is shown that the state error can be bounded by an arbitrarily positive constant after a prescribed instant. Finally, a numerical example and an electromechanical system are presented to demonstrate the effectiveness of the proposed results.

The three-level inverter has been widely researched and adopted in industry. This article further proposes a carrier-based modulation method to reduce the common-mode voltage (CMV) and obtain high-quality output currents for three-level inverter with unbalanced neutral-point voltage conditions. In this method, the zero-sequence voltage is calculated by three-phase modulation waves, three-phase output currents, and voltages across the dc-link capacitors. The limitations of the zero-sequence component are carefully considered in order not to generate the basic voltage vector with high CMV magnitudes. In addition, a deadbeat control scheme for controlling the dc-link voltages asymmetrically with the proposed pulsewidth modulation method is presented, and asymmetrical control of dc-link voltages can be realized with fast response. Compared with the conventional space vector modulation (SVM) and the virtual SVM methods, the proposed scheme can reduce the CMV by half. Simulated and experimental results validate the effectiveness of the proposed method.

Three-phase isolated matrix rectifier (IMR) is a kind of single-stage converters without dc-bus. IMR has the advantages of high transmission efficiency and flexible adjusta-bility of output voltage, which is very suitable for electrical vehicle (EV) chargers. But IMR generates harmonic pollution to the power grid because of its nonlinearity property, which in turn brings disturbances to the output side of IMR. This paper proposes an improved plug-in repetitive controller for the harmonic compensation to three-phase IMR. The proposed controller, which consists of a repetitive controller with a parallel forward path and a conventional PI controller can compensate the harmonic in power grid and attenuate the harmonic currents. For IMR operating at the power frequency, a fractional delay(FD) filter is utilized to approximate the characteristic of the delay and improve the steady-state tracking performance of the proposed controller. Simulation results verify that the proposed control scheme can compensate the harmonics of IMR effectively.

Self-heating is of extreme importance for improving the available capacity and lifetime of lithium-ion batteries in cold climates. However, few attempts have been done to achieve effective onboard self-heating for the batteries in electric vehicles. This paper derives a high-frequency sine-wave (SW) heater based on resonant LC converters to self-heat the automotive batteries at low-temperatures without the need of external heaters. To be specific, an interleaved-parallel topology is introduced to double the heating speed without extra damages to batteries compared to the single heater. Further, a corresponding thermoelectric model is developed to provide guidance for the optimal design of the parameters in the proposed SW heater. Experimental results show that with a high-frequency sinusoidal current motivated by the proposed heater, lithium-ion batteries could be effectively self-heated by the ohmic-loss and electrochemical heat. Moreover, the heating time could be significantly shortened through decreasing the characteristic impedance √(L/C) or increasing the ac-heating frequency.

This paper addresses the finite-time H∞ filtering for a class of nonlinear singular nonhomogeneous Markov jump systems by T-S fuzzy approximation approach, where the transition probabilities (TPs) are time-varying and unknown. First, by considering a stochastic Lyapunov functional and rendering the time-varying TPs inside a polytope, a sufficient condition on singular stochastic H∞ finite-time boundedness (SSH∞FTB) for the filtering error systems is given. Then, by using the matrix inequality decoupling technique, a novel linear matrix inequality (LMI) condition on the existence of the finite-time H∞fuzzy filter is presented. The fuzzy filter is developed in terms of LMIs ensuring the filtering error system is SSH∞FTB. Compared with the previous ones, the proposed design method in this paper has more freedom, leading to less conservative results. A tunnel diode circuit is provided to illustrate the effectiveness and advantage of the design approach proposed in this paper.

Dead-time effect of dual active bridge (DAB) DC-DC converter is usually out of consideration in traditional optimized strategies, causing the deviation of the theoretical and actual results. In this paper, the current stress model of DAB with dead-time effect is given, and combining the transmission power model, a current-stress-optimized strategy is proposed. Meanwhile, the corresponding control scheme is designed and implemented. Different from traditional schemes, in the current-stress-optimized strategy, dead time is various with the transmission power and the voltage conversion ratio, which will enhance the control freedom and improve the performances of the system. Moreover, the optimized strategy proposed in this paper can be implemented in real-time digital microcontrollers without extra memory of microcontroller occupation, thus the control scheme can be simplified and the operation speed can be improved. Finally, experimental results verify the performance of the proposed strategy.

The Z-source neutral point clamped (NPC) inverters can realize buck-boost energy conversion with all satisfying advantages of the three-level inverters. A significant problem related to this topology is the fluctuation of the neutral-point potential (NP). However, the previous modulation techniques for this topology cannot achieve the considerate stable voltage. In this paper, based on the analysis of the relationship among the shoot-through duty cycle and the NP voltage, the average NP current, switching frequency capacitor value, a new feed-forward compensator by modifying the shoot-through duty cycle which can improve to balance the NP voltage balancing is proposed. Meanwhile, in consideration of a low voltage gain caused by the NP voltage balance method, a new control method based on a maximum constant boost control method is proposed, which brings the benefits in both high voltage gain and low-frequency ripple. The considerate stable NP voltage and the high voltage gain have been proven in simulation and through experimental results from a prototype converter.

This paper is devoted to the problem of global stabilization by output-feedback for a class of nonlinear systems with uncertain control coefficients, stable zero-dynamics and linearly unmeasured states dependent growth. By first introducing two kinds of appropriate state transformations, the original system is converted into the new system with deterministic virtual control coefficients and the separated zero-dynamics. Then, a suitable observer based on high-gain K-filters is constructed for the new system, and the backstepping design approach is successfully proposed to the output-feedback controller. It is shown that the global asymptotic stability of the closed-loop system can be guaranteed by the appropriate choice of the design parameters.

A new parameter estimation method for time-varying systems is proposed in this paper. First, by introducing 'a priori' knowledge into the performance index of least square (LS) algorithm, a modified LS algorithm is developed. Then, a number of modifications have been proposed to improve some existing LS algorithms, such as the real-time recursive LS algorithm and the instrumental variable (IV) algorithm. Finally the newly proposed method is applied to a cement blending process. The test results show that the new LS algorithm has remarkably improved the estimation accuracy and the convergent rate of tracking to the true time-varying system parameters.