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• An AI-based approach to deal with uncertainties and complexities in urban VPP study. • Utilise the reinforcement learning's robustness and efficiency for optimal VPP policy in urban environments. • The reinforcement learning AI's capacity for optimal demand response and energy trading is validated. • Statistical analysis is provided to verify the proposed approach's performance and suitability. Virtual Power Plants (VPPs) are becoming popular for managing energy supply in urban environments with Distributed Energy Resources (DERs). However, decision-making for VPPs in such complex environments is challenging due to multiple uncertainties and complexities. This paper proposes an approach that optimizes decision-making for VPPs using Reinforcement Learning (RL) in urban environments with diverse supply-demand profiles and DERs. The approach addresses challenges related to integrating renewable energy sources and achieving energy efficiency. An RL-based VPP system is trained and tested under different scenarios, and a case study is conducted in a real-world urban environment. The proposed approach achieves multi-objective optimization by performing actions such as load-shifting, demand offsetting, and providing ancillary services in response to demand, renewable generators, and market signals. The study validates the effectiveness and robustness of the proposed approach under complex environmental conditions. Results demonstrate that the approach provides optimized decisions in various urban environments with different available resources and supply-demand profiles. This paper contributes to understanding the use of RL in optimizing VPP decision-making and provides valuable insights for policymakers and practitioners in sustainable and resilient cities. [ABSTRACT FROM AUTHOR]

This paper is concerned with the robust stabilization of quantized feedback sliding-mode control (SMC) for a class of uncertain linear systems via an event-triggered approach. By introducing an event-triggered mechanism, the event-triggered state instead of the state itself is quantized. And the generated finite event-triggered quantized state signal is sent through a digital network to the decoder and used for constructing a sliding mode controller to stabilize the uncertain linear systems. The relation among the lower bound of the quantized measurement saturating parameter, the upper bound of the event-triggered threshold parameter and the robust stabilization condition of linear uncertain systems, is first presented. Then, under the relation and by the combination of the proposed event-triggered mechanism for the system state and the discrete on-line adjustment policy for the quantizer sensitivity, the established quantized and event-triggered SMC laws are shown to ensure the reachability of the desired switching surface and global robust stabilization of uncertain linear systems is achieved. The theoretical result is verified via simulation illustration finally. [ABSTRACT FROM AUTHOR]

This paper investigates the iterative learning problem for discrete-time systems with event-triggered scheme and quantization. The event-triggered scheme is firstly considered in the iterative learning controllers to reduce the number of iteration steps to be updated. Here, the event-triggered scheme is designed depending on time t and iterative learning step k . Quantization is then introduced in the event-triggered controllers and some relaxed conditions are presented to guarantee the tracking problem by using some interval matrix properties. Finally, simulation results are given to illustrate the usefulness of the developed criteria. [ABSTRACT FROM AUTHOR]

Control of the superheated steam temperature is a significant technical challenge to coal-fired power plants due to the strong nonlinearity, large inertia and parameter uncertainties with external disturbances. This paper proposes a robust proportion–integration–differentiation (PID) control of the superheated steam temperature with proven stability and the external disturbance rejection. The design is based on the proposed data-driven feedback compensator (DFC) which is a neural network (NN) trained to isolate the nonlinearity and inertia from the PID controller, allowing the system for tuning the PID controller can be simplified as an approximate linear system with the modeling error based-feedback and feed-forward compensations. Then, the PID controller is tuned by the Nyquist stability criterion to place every closed-loop pole to a specific region in the left-half s-plane to guarantee the stability with considering the modeling error. Besides, a NN based feed-forward compensator is trained as the inverse model of the feedback compensator to further smooth the temperature fluctuation caused by the disturbance. The simulations and engineering implementation of the superheated steam temperature controls for a coal-fired power plant in Australia show the effectiveness. • A data-driven compensation based robust control with considering uncertainties. • A neural feedback compensator to compensate the nonlinearity and inertia. • Approximation errors handled by the robust PID control with proven stability. • The effectiveness verified by the implementation in a physical coal-fired plant. [ABSTRACT FROM AUTHOR]

To avoid the chattering problem in the reaching-law-based discrete-time sliding mode control (DSMC) and the generation of over-large control action in the equivalent-control-based DSMC, a new DSMC method based on non-smooth control is proposed in this paper. Since there is no use of any switching term in the proposed DSMC, it is a chattering-free SMC method. Meanwhile, it is shown that the newly proposed non-smooth control-based DSMC can guarantee the same level of accuracy for the sliding mode motion as that of an equivalent control-based DSMC. To demonstrate the effectiveness of the proposed approach, a simulation example is presented. [ABSTRACT FROM AUTHOR]

This paper addresses the sliding mode control problem for uncertain MIMO linear Markovian jump systems. Firstly, by using the linear matrix inequality approach, sufficient conditions are proposed to guarantee the stochastically asymptotical stability of the system on the sliding surfaces. Secondly, an equivalent control based sliding mode control is proposed, such that the closed-loop system can be driven onto the desired sliding surfaces in a finite time. Finally, combining with multi-step state transition probability, the reaching and sliding probabilities are derived for situations where the control force may not be strong enough to ensure the fully asymptotical stability. Simulation results are presented to illustrate the effectiveness of the proposed design method. [ABSTRACT FROM AUTHOR]

In this paper, an equivalent control based sliding mode control is proposed for single input linear Markovian jump systems which guarantees the asymptotical stability. Furthermore, by using the stochastic system theory, a multi-step state transition conditional probability function is introduced for the continuous Markovian process, which is used to define the reaching and sliding probabilities. Furthermore, the formulas for calculating reaching and sliding probabilities are derived for situations where the control force may not be strong enough to ensure the fully asymptotical stability. Extensive simulations are conducted to validate the theoretical results and show the relationship between the control force and reaching and sliding probabilities. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a method to overcome the singularity problem of terminal sliding-mode control systems. The system behaviors in both the reaching phase and the ideal sliding-mode are analyzed. A global nonsingular terminal sliding-mode control strategy for nonlinear systems is developed and it is shown that the proposed control strategy can eliminate the singularity, while guaranteeing the finite-time reachability of the systems to the terminal sliding-mode surface and the finite-time convergence of the systems towards the origin along the terminal sliding-mode surface. [Copyright &y& Elsevier]

Abstract: This paper proposes a terminal sliding mode observer for a class of nonlinear systems to achieve finite time convergence for all error states. Compared to standard sliding mode observers which only enable finite time convergence of the output error, the observer in this paper makes use of fractional powers to reduce other non-output errors to zero in finite time. A 2-degree-of-freedom robotic manipulator is used to demonstrate the effectiveness of the proposed observer. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, discretization behaviors of equivalent control based sliding mode control systems with matched uncertainties are studied. Upper bounds for system steady states are established. Some inherent dynamical periodic properties of the systems subject to matched constant and periodic uncertainties are explored. Simulations are presented to verify the theoretical results. [Copyright &y& Elsevier]

Abstract: In this communique, the properties of a class of discrete-time dynamic systems with power rule are studied and comparisons with their continuous-time counterparts as well as the linear discrete-time systems are made. It is shown that using power rule can be beneficial for improving dynamic behaviors of discrete-time systems. For example, a power control law may stabilize a discrete-time system which is not stabilizable by the linear control law. [Copyright &y& Elsevier]

Abstract: A continuous finite-time control scheme for rigid robotic manipulators is proposed using a new form of terminal sliding modes. The robustness of the controller is established using the Lyapunov stability theory. Theoretical analysis and simulation results show that faster and high-precision tracking performance is obtained compared with the conventional continuous sliding mode control method. [Copyright &y& Elsevier]

This paper addresses the robust learning control problem for a class of nonlinear systems with structured periodic and unstructured aperiodic uncertainties. A recursive technique is proposed which extends the backstepping idea to the robust repetitive learning control systems. A learning evaluation function instead of a Lyapunov function is formulated as a guideline for derivation of the control strategy which guarantees the asymptotic stability of the tracking system. A design example is given. [Copyright &y& Elsevier]

This paper presents a global non-singular terminal sliding mode controller for rigid manipulators. A new terminal sliding mode manifold is first proposed for the second-order system to enable the elimination of the singularity problem associated with conventional terminal sliding mode control. The time taken to reach the equilibrium point from any initial state is guaranteed to be finite time. The proposed terminal sliding mode controller is then applied to the control of n-link rigid manipulators. Simulation results are presented to validate the analysis. [Copyright &y& Elsevier]

• A cutting-edge scientometric analysis is adopted in this literature review. • The considerations for urban sustainability are narrowed in previous studies. • VPP's inclusion of distributed resources are still conservative and limited. • Economic and social impacts for public and community are often neglected. • A roadmap is proposed for future study to address urban sustainability concerns. Due to rapid population growth and urbanisation, more than ever we need an urban environment with greater efficiency and fewer negative impacts. Virtual power plant (VPP) has great potential for improving urban sustainability by supplying clean energy from distributed generators. This paper provides a literature review on VPP from the perspective of urban sustainability. We apply a scientometric literature review approach, and 1225 publications were collected for data visualization and network analysis. Three focus areas, components, operation strategies and problem-solving techniques are identified by the scientometric analysis. A deeper investigation is then conducted to further reveal the role VPP can play in a sustainable urban future. It is found that the intellectual structure of this domain has been well established by previous studies in terms of optimisation, communication and control technologies. However, limitations and gaps also exist from the standpoint of urban environment, such as limitations in adopting innovative technologies, sustainability consideration, stakeholder involvement and urban environment dynamics. This paper contributes to the academic society by analysing the progress and limitations of VPP research and providing the guideline for future research to address urban sustainability issues. [ABSTRACT FROM AUTHOR]

This paper studies the distributed algorithms to obtain a solution of the linear equation A x = b in finite time (FT) over a multi-agent network. In order to guarantee the settling time without depending on the initial states, the fixed-time (FxT) distributed algorithms are also provided to obtain a solution within a globally bounded time. Specifically, three distributed nonlinear algorithms are developed. The first one is designed to achieve FT/FxT consensus on a solution with special initialization. The second is to obtain the solution in FT/FxT with free initialization by first driving the local states to satisfy the special initialization in FT/FxT time. The last one is to guarantee the FT/FxT convergence to a solution closest to specific points when multiple solutions exist. Finally, three case studies are performed to show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The paper is concerned with the stability of an uncertain system with quantization by using super-twisting controller. Both uniform and logarithmic quantization schemes are considered. Our main discovery is that the trajectories are always bounded with static uniform quantization scheme and are globally finite-time stable with logarithmic quantization scheme. Furthermore, a dynamic uniform quantization scheme is proposed to eliminate quantization errors. Simulation examples are given to shown the effectiveness of the proposed design approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose the periodic event-triggering based design of sliding mode control (SMC) for the linear time-invariant (LTI) systems. In this technique, the triggering instants are generated by a triggering mechanism which is evaluated periodically at those time instants when the state measurements are available. So, the continuous state measurement, as it is usually needed in the continuous event-triggering strategy, is no longer required in this proposed triggering strategy. The main advantages of this triggering mechanism are: (1) a uniform positive lower bound for the inter event time is guaranteed and (2) this technique is more economical and realistic than its continuous counterpart due to the relaxation of continuous measurements. In this work, we use SMC to design the periodic event-triggering condition where SMC is designed in such a way that it allows periodic evaluation of triggering rule while guaranteeing the robust performance of the system. Moreover, an upper bound of the sampling period for the periodic measurements is also obtained in this design. Finally, the simulation results are given to demonstrate the design methodology and performance of the system with the proposed event-triggering strategy. [ABSTRACT FROM AUTHOR]

In this paper, the Euler’s discretization of the second order sliding mode control system with the twisting algorithm is studied. It shows that, for all values of discretization steps, initial conditions and allowable parameter setting, the system trajectories are always bounded. It also shows that for the uncertain systems, under a mild condition, the system trajectories are also bounded. Further, the periodic behaviors and limit cycles of the system trajectories are explored with conditions for the existence of periodic orbits formulated. Extensive simulation examples are given to show typical trajectories, and comparison between the first order and second order sliding mode control systems. [ABSTRACT FROM AUTHOR]

The consensus tracking problem of multiple nonholonomic high-order chained-form systems is considered in this paper. A finite-time observer-based distributed control strategy is proposed. At the first step, a distributed finite-time convergent observer is proposed for each agent to estimate the leader’s state in a finite time. Then, a finite-time tracking controller is designed to track the estimated state and the leader’s attitude in a finite time. As an application of the proposed results, finite-time formation control of multiple wheeled mobile robots is studied and a finite-time formation control algorithm is proposed. To show effectiveness of the proposed approach, a simulation example is given. [ABSTRACT FROM AUTHOR]

The publication of fake reviews by parties with vested interests has become a severe problem for consumers who use online product reviews in their decision making. To counter this problem a number of methods for detecting these fake reviews, termed opinion spam, have been proposed. However, to date, many of these methods focus on analysis of review text, making them unsuitable for many review systems where accompanying text is optional, or not possible. Moreover, these approaches are often computationally expensive, requiring extensive resources to handle text analysis over the scale of data typically involved. In this paper, we consider opinion spammers manipulation of average ratings for products, focusing on differences between spammer ratings and the majority opinion of honest reviewers. We propose a lightweight, effective method for detecting opinion spammers based on these differences. This method uses binomial regression to identify reviewers having an anomalous proportion of ratings that deviate from the majority opinion. Experiments on real-world and synthetic data show that our approach is able to successfully identify opinion spammers. Comparison with the current state-of-the-art approach, also based only on ratings, shows that our method is able to achieve similar detection accuracy while removing the need for assumptions regarding probabilities of spam and non-spam reviews and reducing the heavy computation required for learning. [ABSTRACT FROM AUTHOR]

Despite early calls to investigate the application of Vickrey auctions in electricity markets, academic literature still provides little insight into the matter. Similarly, despite general auction-theoretic literature discussing various advantages of employing revealed rather than sealed bidding procedures, publications on electricity market auctions rarely even mention differences between these opposing features. This paper marries these neglected features into a new auction model for electricity markets. We propose a novel open-bid, descending-clock auction capable of handling inter-bidder dependencies such as thermal line limits. A novel set of termination criteria ensures that our proposed auction facing markets with power balance and generator capacity constraints and thermal line limits, preserves truthful bidding as a dominant strategy, which, if employed by all bidders, allocates supply efficiently constituting an ex-post perfect equilibrium. Furthermore, we discuss known disadvantages of Vickrey–Clarke–Groves type auctions in the context of our proposed auction applied in electricity markets. • Proposition of a novel auction design considering electricity grid constraints. • Given quasi-linear utility, truthful bidding is weakly dominant utilising open bids. • Truthful bidding by all bidders results in efficient supply allocation. • Discussion of real-world electricity market design impacting auction properties. [ABSTRACT FROM AUTHOR]

Abstract: In conventional sliding-mode control systems, the sliding-mode motion is of reduced order. Two main problems hindering the application of the sliding-mode control are the singularity in terminal sliding-mode control systems and the chattering in both the conventional linear sliding-mode and the terminal sliding-mode control systems. This paper proposes a chattering-free full-order terminal-sliding-mode control scheme. Since the derivatives of terms with fractional powers do not appear in the control law, the control singularities are avoided. A continuous control strategy is developed to achieve the chattering free sliding-mode control. During the ideal sliding-mode motion, the systems behave as a desirable full-order dynamics rather than a desirable reduced-order dynamics. A systematic design method of full-order sliding-mode control for nonlinear systems is presented, which allows both the chattering and singularity problems to be resolved. Simulations validate the proposed chattering free full-order sliding-mode control. [Copyright &y& Elsevier]

Abstract: This paper proposes a chaotic fingerprint images encryption approach. An image of a fingerprint is encrypted via a two-dimensional (2D) chaotic sequence obtained from multi-scroll chaotic attractors. Initial values of the chaotic attractors are served as the private key, which can be generated from the pixel distribution of the binary images of the captured fingerprints. Due to the dynamic uncertainties in the acquisition process of fingerprint images, the keys generated from the pixel value distribution are virtually random. With the elaborately designed 2D chaotic sequence, the encrypted fingerprint images have balanced 0–1 ratio and ideal nonlinearity. Only with the valid private key can the images of fingerprint be recovered. Simulation results and 2D-DFT validate this chaotic encryption approach. [Copyright &y& Elsevier]

This paper introduces a systematic method—a hysteresis series switching approach—for generating multi-scroll chaotic attractors from a three-dimensional linear autonomous system, including 1-D n-scroll, 2-D n×m-grid scroll, and 3-D n×m×l-grid scroll chaotic attractors. The chaos generation mechanism is studied by analyzing the system trajectories and the hysteresis switching dynamics of the controlled chaotic systems are explored. Moreover, a two-dimensional Poincaré return map is rigorously derived. This map and its maximum Lyapunov exponent are applied to verifying the chaotic behaviors of the generated 3-D multi-scroll chaotic attractors. [Copyright &y& Elsevier]

The problem of robust decentralized stabilization for a class of large-scale, time delay, and uncertain impulsive dynamical systems is introduced and studied. Some explicit criteria of robust exponential stabilization in the large for such systems are established. A simple approach to designing a robust decentralized controller is presented. A numerical example is given for illustrating and interpreting the theoretical results. [Copyright &y& Elsevier]

The increasing share of renewable energy sources (RESs) in electricity generation leads to increased uncertainty of generation, frequency and voltage regulation as well as difficulties in energy management. A virtual power plant (VPP), as a combination of dispersed generator units, controllable load and energy storage system (ESS), provides an efficient solution for energy management and scheduling, so as to reduce the cost and network impact caused by the load spikes. This paper proposes a multi-objective optimization (MOO) of battery energy storage system (BESS) for VPP applications. A low-voltage (LV) network in Alice Springs (Northern Territory, Australia) is considered as the test network for this study. The BESS for each customer is used to store and release the energy when required to maintain the voltage regulation performance of the LV network and reduce the cost. To optimize the charge/discharge schedule in each battery, a multi-objective optimization tool (MOOT) is developed, where MOO can directly communicate with DIgSILENT PowerFactory platform to perform multi-time scale simulation. MOO mainly focuses on generating a set of trade-off schedule solutions for each customer over 24 h by considering the customer's cost and network impact. Then according to the stakeholder's prior preference, one of the solutions is selected and verified in DIgSILENT PowerFactory with a realistic LV network. We also design several scenarios with different penetrations of photovoltaics (PVs) and batteries to verify how they influence customer's cost and LV network. With the increasing penetrations of PVs and batteries, the experimental results over the selected solution according to the customer preference show that the customers' cost can be largely reduced by maintaining the network voltage regulation performance. • A generalized VPP BESS optimization framework is proposed. • Optimization of BESS in a VPP setting is modeled as an MOO problem. • MOOT facilitates the two-way communication between MOO and DIgSILENT PowerFactory directly without manual operations. • Large PV and battery penetration can largely reduce the customers' cost while maintaining the voltage level. [ABSTRACT FROM AUTHOR]

Highlights • Optimization model developed for PV–battery systems in P2P energy trading market. • P2P energy trading implements real-world constraints and market signals. • Analysis shows household energy savings sensitive to multiple parameters. • Maximal savings up to 28% achieved when equipped with large PV-battery on weekdays. • Battery-only households achieve fewer savings compared to those without any DG. Abstract Distributed generation (DG) based on rooftop photovoltaic (PV) systems with battery storages is a promising alternative energy generation technology to reduce global greenhouse gas emissions. As regulatory tariff-based incentives are diminishing, innovative solutions are required to sustain this renewable energy generation. An optimization model is proposed to maximize the economic benefits for rooftop PV-battery DG in a peer-to-peer (P2P) energy trading environment. The goal of the proposed model is to investigate the feasibility of such renewable source participated P2P energy trading by examining the economic benefits. The model is illustrated in a simulation framework for a local community with 500 households under real-world constraints encompassing PV systems, battery storage, customer demand profiles and market signals including the retail price, feed-in tariff and P2P energy trading mechanism. Interactions among peer-to-peer trading stakeholders are examined, quantifying household savings for different scenarios of this P2P-based DG. Household energy savings are identified to be sensitive to many factors including the scale of PV systems, the PV penetration, the P2P trading margins, the presence of battery storage and energy trading time. The model shows that maximal savings up to 28% can be achieved by households equipped with larger PV systems and battery storages during weekdays from an exemplified case. The sensitivity analysis demonstrates that households with PV systems have lower savings when PV penetration is high owing to excessive energy traded on the P2P market, pushing down the clearing price and the savings gain. Households with a battery-only configuration are shown to achieve fewer savings in contrast to households without any renewable resources in a P2P trading community. The model’s energy insights are the beginning of understanding the actual impact of policy, market and technical signals on economic benefits for household distributed renewable generation in a P2P energy trading market. [ABSTRACT FROM AUTHOR]