One of the major challenges in deploying demand-responsive energy management programs is the design of control-oriented models that explain how the demand is affected by shifting commands (e.g., shifts in the set points of lights, heating, ventilation and air conditioning (HVAC) units). This work proposes an averaging technique for control-oriented demand modeling: the key aspect resides in determining the average thermostatic phases emerging from coupled continuous-discrete demand dynamics. We analytically study stability, optimality, and robustness of averaging-based energy management, and we benchmark its effectiveness against state-of-the-art learning-based energy management. The proposed approach exhibits 3-4 times improved demand-responsive tracking, without compromising user’s flexibility in terms of set points and temperature deviations. Notably, the thermostatic phases corrupt the effectiveness of learning-based energy management, but not of the proposed averaging-based energy management.

The vast majority of protocols for connected automated vehicles are based onstatussharing, i.e., communication of the current vehicle state among neighboring vehicles. Only recently the idea ofintentsharing has been put forward, where not only the current state, but also the vehicle intention in the near future can be communicated. In the context of Cooperative Adaptive Cruise Control (CACC), this work provides a system-theoretic framework for intent sharing through the lens of output regulation. We present analytical results showing two fundamental aspects of CACC with intent sharing: a) when vehicle-to-vehicle communication is reliable, intent sharing provides no benefits over status sharing, as both sharing paradigms result in the same protocol; b) intent sharing becomes beneficial when vehicle-to-vehicle communication is unreliable, in which case the latest communicated intent can be used to reconstruct the missing information of the neighboring vehicle in the near future. Together with theoretical analysis, numerical validations with synthetic and real-world data are provided, where the benefits of CACC with the proposed implementation of intent sharing are shown against several state-of-the-art CACC protocols.

This work proposes a guaranteed collision avoidance framework for Cooperative Adaptive Cruise Control of a vehicular platoon characterized by unidirectional communication and heterogeneous parameters. In the proposed framework, the actual (heterogeneous) platoon is made to converge to a reference (homogeneous) platoon via adaptive laws designed using of set-theoretic model reference adaptive control. Yet, in contrast to the state-of-art that is based on ensuring collision avoidance on the reference platoon dynamics only, the approach we propose can ensure collision avoidance on the actual platoon dynamics. This result is possible thanks to the introduction of a novel concept of virtual platoon, only used for analysis, but that does not interact with the actual platoon. The stability and convergence properties of the proposed framework are established using Lyapunov-based analysis in conjunction with the aforementioned virtual platoon concept. Simulation results further demonstrate the effectiveness of the proposed framework.

Multi-modal vehicle trajectory prediction is crucial for autonomous driving in dynamic environments. Despite the significant progress in the field, the uncertainty and heterogeneity caused by the diversity in driving intentions and driving scenes still present major challenges to multi-modal prediction. Existing query-based prediction paradigms take into account the social context arising from the driving scene, but neglect priors regarding vehicle intentions that the historical trajectory may contain. We propose a Social-aware Water-wave Information Transmission Recurrent Acceleration Network, abbreviated as S-WITRAN, based on decoupling multi-modal prediction into an ego-aware and a social-aware learning stage. The ego-aware stage aims to relax the constraints from the driving scene to explore a diversity of future trajectory candidates. The social-aware stage aligns the candidates with respect to the social context arising from the driving scene. The information transmission is designed to extract from the vehicle’s historical trajectory priors about its possible intentions and dynamic states, which are integrated to form a multi-modal set of trajectories. Extensive experiments on the NGSIM, highD, and Argoverse2 datasets, as well as a benchmark evaluation in UniTraj, demonstrate the superior performance of the proposed model in both map-free and map-based datasets.

In longitudinal platooning, some key sources of uncertainty are the powertrain time constants of the vehicles. Because such time constants appear in the input matrix of the platooning dynamics, their correct estimation is either impractical with methods requiring persistence of excitation or impossible with methods requiring the input matrix to be known. This work proposes a novel adaptive longitudinal platooning method with correct estimation of the powertrain time constants. To achieve correct estimation, the composite adaptive control framework and its stability analysis are suitably modified to handle the time constant uncertainty in the design of the adaptive law. The result is a platooning protocol that guarantees convergence of the estimated time constants to their true values without the need for persistence of excitation: it is sufficient that the derivative of the acceleration is nonzero over a possibly short transient, an extremely relaxed excitation condition. Comparisons with state-of-the-art platooning solutions reveal advantages such as no required measurements of acceleration derivative or collection of past data. The robustness and practicality of the proposed design are also verified with CarSim-based platooning experiments.

Evidences have been provided that the effect of poorly designed vehicle automation systems may propagate from the single car up to the traffic dynamics. A reported example consists of car-following driver assistance systems triggering destabilizing group phenomena like phantom traffic jams and stop-and-go waves. It then becomes fundamental to study ‘group-oriented’ vehicle shared control algorithms that are able to assist the driver while at the same time prevent the propagation of destabilizing effects in the traffic when these systems are widely deployed. A key challenge in vehicle shared control is that the heterogeneity and uncertainty of human driving characteristics require personalized adaptation: it is an open problem to realize stabilizing traffic properties from personalized adaptive vehicle shared control. The distinguishing contribution of this work is a car-following shared control method that, while adapting to the personal characteristics of each driver, contains a ‘group’ model with desirable traffic properties defined in terms of string stability and collision avoidance. It is proven analytically that the proposed shared control is able to assist each driver in approaching the group model adaptively (i.e., handling heterogeneity and uncertainties) and optimally (i.e., with minimum control authority over the driver). Numerical experiments performed in SUMO with Highway Fuel Economy Test Cycle (HWFET) data and stop-and-go wave data validate that the proposed assistance improves traffic smoothing while handling heterogeneity and uncertainties in the driver parameters.

While several methods for short-term traffic prediction have been proposed in the literature, modeling the interaction between short-term and long-term traffic dynamics remains a major challenge. To address this problem, we propose a deep learning architecture named High and Low Frequency Attention Network (HLFNet) with the capability to capture multi-scale spatio-temporal dynamics. HLFNet takes inspiration from the frequency domain analysis in signal processing, where short-term (fast) and long-term (slow) dynamics are characterized in terms of high and low frequency, respectively. Specifically, we design a multi-scale attention block considering multi-grained feature information at different frequencies. The attention block comprises: a high-frequency attention operation for extracting fine-grained information within a local window; a low-frequency attention operation for extracting coarse-grained information within average pooled features. To capture the spatial interactions within the traffic network, HLFNet further considers three spatial embeddings: distance-based, attribute-based and potential-based. Experimental results with real-world traffic datasets using different prediction horizons and granularities show that, no matter how state-of-the-art methods are good at predicting in the short term, they fail to capture long-term trends. On the other hand, HLFNet retains a consistent prediction performance in both the short and the long term, outperforming all tested state-of-the-art methods in long-term traffic prediction.

Due to the complex entanglement between distributed control and distributed estimation, adaptive multiagent dynamics over leaderless directed graphs are yet not completely understood. This happens even when the adaptive dynamics are based on established tools like model reference adaptive control (MRAC). This work starts from the observation that existing MRAC-based leaderless designs stop at asymptotic consensus, lacking of any guarantee for exponential consensus or parameter identification. The main contribution of this work is to show a new design departing from the existing ones in terms of exploiting persistence of excitation (PE): the proposed design is the first one attaining exponential consensus with exponential parameter identification over leaderless directed graphs. In the special case that the unknown parameters are homogeneous, PE can be relaxed to a weaker cooperative PE (C-PE) condition. The design is illustrated and verified alongside the state-of-the-art.

This work presents an adaptive open-source implementation of ArduPilot: the adaptive mechanisms in the autopilot are inspired by model reference stabilization (MRS) and are seamlessly embedded into the open-source ArduPilot suite. We illustrate MRS ArduPilot for the ArduPlane and ArduCopter modules (fixed-wing and rotary-wing vehicles): yet, the approach is general enough to be applicable to all aerial/surface/marine vehicles of ArduPilot, and even to PX4. Our tests show that the embedded adaptation makes the vehicle capable of handling uncertain scenarios like wind and varying payloads. The source code of MRS ArduPilot is released at https://github.com/Sunsun24/MRS.git

Reconfigurable robots provide an attractive option for cleaning tasks, thanks to their better area coverage and adaptability to changing environment. However, the ability to change morphology creates drastic changes in the reconfigurable robot dynamics, and existing control design techniques do not take this into account. Neglecting configuration changes can lead to performance degradation and, in the worst scenarios, instability. This paper proposes to embed the changes arising from reconfiguration in the control design, via a switched uncertain Euler-Lagrangian model. Accordingly, a novel switched adaptive design is proposed for trajectory tracking. Closed-loop stability is assured using the multiple Lyapunov function framework, and the design is implemented and validated on a self-reconfigurable pavement cleaning mobile robot (PANTHERA). Note to Practitioners—Self-reconfigurable mobile cleaning robots, which can change their configurations as per the application requirements, are now predominantly used for cleaning and maintenance operations because of their better area coverage, less manpower requirement and consistent performance. However, the state-of-the-art control strategies for conventional robots cannot always ensure stability and performance under the simultaneous effects of configuration changes and uncertainties. The switched Euler-Lagrange model formulated in this work can capture the configuration changes of the robot and the proposed switched adaptive controller can tackle uncertainties of each configurations of the robot. The simulation and experimental results clearly show the potential issues of the state-of-the-art methods and the remarkable benefits of the proposed approach.

Presents corrections to the paper, (Correction for "Consensus in High-Power Multiagent Systems With Mixed Unknown Control Directions via Hybrid Nussbaum-Based Control").

Cooperative adaptive cruise control (CACC) is one of the most studied platooning algorithms for connected vehicles. Despite its popularity, available studies neglect an important practical aspect of CACC: because the control input is a desired acceleration, existing CACC algorithms require an acceleration-hold loop, fed by accelerometer measurements that are noisy in practice. This work proposes new classes of CACC strategies that, while avoiding any feedback from the accelerometer, guarantee the same properties of existing designs. Theoretical properties are proven in terms of stability and string stability. Numerical tests, also performed in the open-source CARLA platooning toolbox named OpenCDA (cooperative driving automation), are provided to validate the theoretical properties of the design and the improved performance against noisy measurements.

Due to the use of inter-vehicle wireless communication, vehicular platooning can be prone to attacks with corrupted data, as in false data injection (FDI) attacks. It is crucial to develop platooning protocols promoting resilience to injected false data. In this work we show that resilience can be attained by making use of a system-theoretic property known as disturbance decoupling. We first show how disturbance decoupling is obtained in nominal platooning protocols without attacks: then, in the presence of FDI attacks, we propose compensation strategies that guarantee to recover the nominal performance of the platoon. The proposed compensation strategies can cope with platoons of heterogeneous vehicles and are designed towards string stability. Numerical experiments, also performed in a SUMO-Veins co-simulation environment with different platooning scenarios under FDI attacks, validate the effectiveness of the proposed protocols in handling cyber-attacks and platoon heterogeneity.

The vector field method was originally proposed to guide a single fixed-wing Unmanned Aerial Vehicle (UAV) towards a desired path. In this work, a non-uniform vector field method is proposed that changes in both magnitude and direction, for the purpose of achieving formations of UAVs. As compared to related work in the literature, the proposed formation control law does not need to assume absence of wind. That is, due to the effect of the wind on the UAV, one can handle the UAV air speed being different from its ground speed, and the UAV heading angle being different from its course angle. Stability of the proposed formation method is analyzed via Lyapunov stability theory, and validations are carried out in software-in-the-loop and hardware-in-the-loop comparative experiments. Note to Practitioners—The software-in-the-loop and hardware-in-the-loop experiments, which are done with PX4 autopilot software and hardware, show that the proposed method can be implemented on board of UAVs and integrated with the control architecture of existing autopilot suites. Comparisons with standard formation algorithms show that the proposed method is effective in achieving formation in different path scenarios.

This article proposes an output-feedback control protocol for hypersonic flight vehicle (HFV) swarms considering dynamic event-triggered communication. The peculiarities of the proposed method over existing ones consist in the following: 1) While carrying out scheduled maneuvers, the outputs of follower HFVs converge inside the convex hull spanned by leader HFVs whose task is to maintain a geometric space configuration; 2) a simple nonrecursive output-feedback design is established without involving any intermediate control laws or requiring full-state information; 3) an error-dependent monotonically decreasing exponential term is incorporated into the dynamic event-triggered threshold to reduce the communication bandwidth while preserving the desired track performance and excluding Zeno behavior. Comparative simulation results validate the effectiveness of the proposed methodology.

The interaction between automated and human-driven vehicles in mixed (human/automated) platoons is far from understood. To study this interaction, the notion of head-to-tail string stability was proposed in the literature. Head-to-tail string stability is an extension of the standard string stability concept where, instead of asking every vehicle to achieve string stability, a lack of string stability is allowed due to human drivers, provided it can be suitably compensated by automated vehicles sparsely inserted in the platoon. This work introduces a theoretical framework for the problem of head-to-tail string stability of mixed platoons: it discusses a suitable vehicle-following human driver model to study mixed platoons, and it gives a reduced-order design strategy for head-to-tail string stability only depending on three gains. The work further discusses the safety limitations of the head-to-tail string stability notion, and it shows that safety improvements can be attained by an appropriate reduced-order design strategy only depending on two additional gains. To validate the effectiveness of the design, linear and nonlinear simulations show that the string stability/safety trade-offs of the proposed reduced-order design are comparable with those resulting from full-order designs.

Actor-critic (AC) cooperative multiagent reinforcement learning (MARL) over directed graphs is studied in this article. The goal of the agents in MARL is to maximize the globally averaged return in a distributed way, i.e., each agent can only exchange information with its neighboring agents. AC methods proposed in the literature require the communication graphs to be undirected and the weight matrices to be doubly stochastic (more precisely, the weight matrices are row stochastic and their expectation are column stochastic). Differently from these methods, we propose a distributed AC algorithm for MARL over directed graph with fixed topology that only requires the weight matrix to be row stochastic. Then, we also study the MARL over directed graphs (possibly not connected) with changing topologies, proposing a different distributed AC algorithm based on the push-sum protocol that only requires the weight matrices to be column stochastic. Convergence of the proposed algorithms is proven for linear function approximation of the action value function. Simulations are presented to demonstrate the effectiveness of the proposed algorithms.

This article investigates the finite-time distributed adaptive consensus for nonlinear uncertain multiagent systems (MASs). The performance of the consensus errors can be prespecified in a funnel sense. Three achievements make this work depart from available results on prespecified performance and funnel control: first, a novel error transformation is constructed to prespecify the performance, which avoids the singularity problem pointed out in the literature in the differentiation of the control law; second, the funnel control method is extended in a MAS setting by suitably modifying the backstepping technique in a power integrator sense; and third, it is shown that finite-time stability notions can be attained without extra complexity being involved in the design. Stability analysis and comparative simulations demonstrate the method.

This work studies the formation control problem for mobile robots. The distinguishing feature of this work is considering the leader and the followers dynamics to be non-ideal, i.e. subject to disturbances/unmodelled terms. Distributed joint disturbance-and-leader estimators are designed to solve the problem, allowing to reconstruct the leader’s signals in a distributed way. The estimates of the leader and the follower disturbances provided by the estimators are embedded in the control law to reject such disturbances and achieve the formation asymptotically. Lyapunov technique is employed to analyze stability of the overall distributed formation control algorithm. Simulations are conducted to illustrate the theoretical results.

Switch-based adaptive dynamic programming (ADP) is an optimal control problem in which a cost must be minimized by switching among a family of dynamical modes. When the system dimension increases, the solution to switch-based ADP is made prohibitive by the exponentially increasing structure of the value function approximator and by the exponentially increasing modes. This technical correspondence proposes a distributed computational method for solving switch-based ADP. The method relies on partitioning the system into agents, each one dealing with a lower dimensional state and a few local modes. Each agent aims to minimize a local version of the global cost while avoiding that its local switching strategy has conflicts with the switching strategies of the neighboring agents. A heuristic algorithm based on the consensus dynamics and Nash equilibrium is proposed to avoid such conflicts. The effectiveness of the proposed method is verified via traffic and building test cases.

This article investigates the distributed time-varying optimization problem for second-order multiagent systems (MASs) under limited interaction ranges. The goal is to seek the minimum of the sum of local time-varying cost functions (CFs), where each CF is only available to the corresponding agent. Limited communication range refers to the scenario where the agents have limited sensing and communication capabilities, that is, a pair of agents can communicate with each other only if their distance is within a certain range. To handle such a problem, a new continuous connectivity-preserving mechanism is presented to preserve the connectivity of the considered network. Then, two distributed optimization algorithms are presented to solve the optimization problem with time-varying CFs and time-invariant CFs, respectively. Theoretical analysis and two numerical examples are provided to verify the effectiveness of the methods.

This work investigates the consensus tracking problem for high-power nonlinear multiagent systems with partially unknown control directions. The main challenge of considering such dynamics lies in the fact that their linearized dynamics contain uncontrollable modes, making the standard backstepping technique fail; also, the presence of mixed unknown control directions (some being known and some being unknown) requires a piecewise Nussbaum function that exploits the a priori knowledge of the known control directions. The piecewise Nussbaum function technique leaves some open problems, such as Can the technique handle multiagent dynamics beyond the standard backstepping procedure? and Can the technique handle more than one control direction for each agent? In this work, we propose a hybrid Nussbaum technique that can handle uncertain agents with high-power dynamics where the backstepping procedure fails, with nonsmooth behaviors (switching and quantization), and with multiple unknown control directions for each agent.

Adaptive output-feedback consensus with funnel performance is studied for nonlinear uncertain multiagent systems (MASs). The nonlinear MASs contain unknown dynamics and only an output variable can be measured. The other states are not measured directly and are reconstructed via fuzzy state observers. The presence of output feedback makes the proposed design significantly different from the existing literature on control with funnel performance. In particular, appropriate adaptive laws must be defined to handle the presence of uncertain dynamics and local output information from a few neighboring nodes. Interestingly, with a suitable error transformation, it is shown that the proposed control law has a simpler structure than barrier function methods proposed in the literature to handle funnel-like performance. With respect to this point, simulation studies illustrate that the proposed method can dramatically reduce the control effort while satisfying transient and steady-state performance imposed by the funnel.

This paper presents a comprehensive study on the impact of information flow topologies on the resilience of distributed algorithms that are widely used for estimation and control in vehicle platoons. In the state of the art, the influence of information flow topology on both internal and string stability of vehicle platoons has been well studied. However, understanding the impact of information flow topology on cyber-security tasks, e.g., attack detection, resilient estimation and formation algorithms, is largely open. By means of a general graph theory framework, we study connectivity measures of several platoon topologies and we reveal how these measures affect the ability of distributed algorithms to reject communication disturbances, to detect cyber-attacks, and to be resilient against them. We show that the traditional platoon topologies relying on interaction with the nearest neighbor are very fragile with respect to performance and security criteria. On the other hand, appropriate platoon topologies, namely$k$-nearest neighbor topologies, are shown to fulfill desired security and performance levels. The framework we study covers undirected and directed topologies, ungrounded and grounded topologies, or topologies on a line and on a ring. We show that there is a trade-off in the network design between the robustness to disturbances and the resilience to adversarial actions. Theoretical results are validated via simulations.

The broad learning system (BLS) paradigm has recently emerged as a computationally efficient approach to supervised learning. Its efficiency arises from a learning mechanism based on the method of least-squares. However, the need for storing and inverting large matrices can put the efficiency of such mechanism at risk in big-data scenarios. In this work, we propose a new implementation of BLS in which the need for storing and inverting large matrices is avoided. The distinguishing features of the designed learning mechanism are as follows: 1) the training process can balance between efficient usage of memory and required iterations (hybrid recursive learning) and 2) retraining is avoided when the network is expanded (incremental learning). It is shown that, while the proposed framework is equivalent to the standard BLS in terms of trained network weights,much larger networks than the standard BLS can be smoothly trained by the proposed solution, projecting BLS toward the big-data frontier.

This work investigates a reduced-complexity adaptive methodology to consensus tracking for a team of uncertain high-order nonlinear systems with switched (possibly asynchronous) dynamics. It is well known that high-order nonlinear systems are intrinsically challenging as feedback linearization and backstepping methods successfully developed for low-order systems fail to work. Even the adding-one-power-integrator methodology, well explored for the single-agent high-order case, presents some complexity issues and is unsuited for distributed control. At the core of the proposed distributed methodology is a newly proposed definition for separable functions: this definition allows the formulation of a separation-based lemma to handle the high-order terms with reduced complexity in the control design. Complexity is reduced in a twofold sense: the control gain of each virtual control law does not have to be incorporated in the next virtual control law iteratively, thus leading to a simpler expression of the control laws; the power of the virtual and actual control laws increases only proportionally (rather than exponentially) with the order of the systems, dramatically reducing high-gain issues.

A fast architecture for real-time (i.e., minute-based) training of a traffic predictor is studied, based on the so-called broad learning system (BLS) paradigm. The study uses various traffic datasets by the California Department of Transportation, and employs a variety of standard algorithms (LASSO regression, shallow and deep neural networks, stacked autoencoders, convolutional, and recurrent neural networks) for comparison purposes: all algorithms are implemented in MATLAB on the same computing platform. The study demonstrates a BLS training process two-three orders of magnitude faster (tens of seconds against tens-hundreds of thousands of seconds), allowing unprecedented real-time capabilities. Additional comparisons with the extreme learning machine architecture, a learning algorithm sharing some features with BLS, confirm the fast training of least-square training as compared to gradient training.

An accepted autopilot control architecture for fixed-wing unmanned aerial vehicles (UAVs) is the so-called cascaded loop closure, in which inner velocity loops and outer position loops are successively closed with proportional-integral-derivative (PID) controllers. This architecture has become so standard that popular open-source autopilots (e.g. ArduPilot, PX4) implement it in their codes. Despite its popularity, such architecture cannot adequately cope with the inevitable uncertainty in the UAV dynamics. In this work we present a "plug-and-play" adaptive module integrated in standard cascaded autopilot architectures, so as to can guarantee adaptation in the presence of uncertainty. The proposed module is analyzed and tested in a software-in-the-loop environment for an ArduPilot-based autopilot. The tests show that, in the presence of uncertainties occurring during flight, the proposed adaptation module outperforms the original autopilot as well as non-adaptive autopilots.

In adaptive platooning strategies proposed in literature to handle uncertain and nonidentical uncertain vehicle dynamics (uncertain heterogeneous platoons) two aspects requiring proper design are neglected: bidirectional interaction among vehicles which might lead to loss of string stability, and engine saturation constraints which might lead to loss of cohesiveness. This work proposes a novel adaptive platooning strategy handling these two crucial aspects. Specifically, bidirectional interaction is handled by designing bidirectional reference dynamics with proven string stability properties, to which the uncertain heterogeneous platoon should homogenize; engine constraints are handled via a proposed a mechanism that makes such reference dynamics `not too demanding', by properly saturating their action. The saturation action will allow all vehicles in the platoon to not hit their engine limits, preserving cohesiveness. Simulations are conducted to validate the theoretical analysis and show the effectiveness of the method in retaining cohesiveness of the platoon.

In recent years, field experiments have been performed on ring roadways with human-driven vehicles or with a mix of human-driven and autonomous vehicles. While these experiments demonstrate the potential for controlling traffic flows by a small number of autonomous vehicles, the theoretical framework about such a possibility is to a large extent incomplete. Indeed, most work on mixed traffic focused on classical asymptotical stability notions, neglecting that human drivers are prone to the interconnected instability known in the literature as string instability. This work aims to enhance the existing theories to meet the questions raised by the field experiments. It starts from the observation that the standard notion of string stability on a ring roadway is too demanding for a mixed traffic scenario: therefore, a new interconnected stability definition, named weak ring stability, is proposed. This new interconnected stability notion, in combination with classical stability, is able to explain phenomena observed in field experiments and to highlight possibilities and limitations of traffic control via sparse autonomous vehicle. Furthermore, it allows designing AV controllers with improved string stability specifications, at the price of reducing the sparsity of the autonomous vehicles.

This article addresses and solves the adaptive asymptotic tracking for a class of uncertain switched positive linear dynamics (also known in the literature as compartmental models) subject to dwell-time constraints. Compared to the state-of-the-art, the innovative feature of this method is to attain for the first time asymptotic set-point tracking, while guaranteeing non-negativity of the systems states. To achieve asymptotic tracking, an interpolated Lyapunov function is adopted, which is nonincreasing at the switching instants and decreasing in two consecutive switching instants. Such Lyapunov function results in a novel adaptive law with time-varying adaptive gains, as opposed to state-of-the-art laws with fixed positive adaptive gains. The developed design is applicable to classes of compartmental systems compatible with those proposed in the literature: an example involving the infusion of anesthesia is conducted to show that the proposed method can achieve better performance than existing methods.

This paper proposes a novel set-invariance adaptive dynamic surface control (DSC) design for a larger class of uncertain large-scale nonlinear input-saturated systems. The peculiarity of this class is that noa prioribound on the continuous control gain functions is assumed (i.e., their boundedness cannot be assumed before obtaining system stability). This requires a new design. Differently from the available methods, the proposed design involves the construction of appropriate invariant sets for the closed-loop trajectories, which allows to remove the restrictive assumption ofa prioribounds of the control gain functions. Furthermore, we show that such set-invariance design can handle input constraints in the form of input saturation. In line with the DSC methodology, semi-globally uniformly ultimate boundedness is proven: however, differently from the standard methodology, stability analysis requires the combination of Lyapunov and invariant set theories.

This work studies a new reinforcement learning method in the framework of Recursive Least-Squares Temporal Difference (RLS-TD). Differently from the standard mechanism of eligibility traces, leading to RLS-TD(λ), in this work we show that the forgetting factor commonly used in gradient-based estimation has a similar role to the mechanism of eligibility traces. We adopt an instrumental variable perspective to illustrate this point and we propose a new algorithm, namely - RLS-TD with forgetting factor (RLS-TD-f). We test the proposed algorithm in a Policy Iteration setting, i.e. when the performance of an initially stabilizing controller must be improved. We take the cart-pole benchmark as experimental platform: extensive experiments show that the proposed RLS-TD algorithm exhibits larger performance improvements in the largest portion of the state space.

With the advent of intelligent transport, quadrotors are becoming an attractive aerial transport solution during emergency evacuations, construction works etc. During such operations, dynamic variations in (possibly unknown) payload and unknown external disturbances cause considerable control challenges for path tracking algorithms. In fact, the state-dependent nature of the resulting uncertainties makes state-of-the-art adaptive control solutions ineffective against such uncertainties that can be completely unknown and possibly unbounded a priori. This paper, to the best of the knowledge of the authors, proposes the first adaptive control solution for quadrotors, which does not require any a priori knowledge of the parameters of quadrotor dynamics as well as of external disturbances. The stability of the closed-loop system is studied analytically via Lyapunov theory and the effectiveness of the proposed solution is verified on a realistic simulator.

This paper proposes a novel adaptive fuzzy dynamic surface control (DSC) method for an extended class of periodically disturbed strict-feedback nonlinear systems. The peculiarity of this extended class is that the control gain functions are not bounded a priori but simply taken to be continuous and with a known sign. In contrast with existing strategies, controllability must be guaranteed by constructing appropriate compact sets ensuring that all trajectories in the closed-loop system never leave these sets. We manage to do this by means of invariant set theory in combination with the Lyapunov theory. In other words, boundedness is achieved a posteriori as a result of stability analysis. The approximator composed of fuzzy logic systems and Fourier series expansion is constructed to deal with the unknown periodic disturbance terms.

In this article, the dynamic economic dispatch (DED) problem for smart grid is solved under the assumption that no knowledge of the mathematical formulation of the actual generation cost functions is available. The objective of the DED problem is to find the optimal power output of each unit at each time so as to minimize the total generation cost. To address the lack of a priori knowledge, a new distributed reinforcement learning optimization algorithm is proposed. The algorithm combines the state-action-value function approximation with a distributed optimization based on multiplier splitting. Theoretical analysis of the proposed algorithm is provided to prove the feasibility of the algorithm, and several case studies are presented to demonstrate its effectiveness.

The work presented in this paper concerns a switching-based control formulation for multi-intersection and multiphase traffic light systems. A macroscopic traffic flow modeling approach is first presented, which is instrumental to the development of a model-based and switching-based optimization method for traffic signal operation, in the framework of adaptive dynamic programming (ADP). The main advantage of the switching-based formulation is its capability to determine both “when”' to switch and “which” mode to switch on without the need to use the cycle-based average flow approximation typical of state-of-the-art formulations. In addition, the framework can handle different cycle times across intersections without the need for synchronization constraints and, moreover, minimum dwell-time constraints can be directly enforced to comply with minimum green/red times in each phase. The simulation experiments on a multi-intersection and multiphase traffic light systems are presented to show the effectiveness of the method.

This work removes the critical assumptions of continuity, differentiability, and state-independent boundedness, which are typical of compounded disturbances in disturbance observer-based adaptive designs. Crucial in removing such assumptions are a novel observer-based design with state-dependent gain in place of a constant one, and a novel set-invariance design. The designs use different a priori knowledge of the disturbance, but they can both handle state-dependent (e.g., possibly unbounded) disturbances, as well as non-smooth (e.g., non-differentiable and jump discontinuous) disturbances. The tracking error is proven to be as small as desired by appropriately choosing design parameters. For the second design, which uses the least a priori knowledge of the disturbance, stability is proven by enhancing Lyapunov theory with an invariant-set mechanism, so as to construct an appropriate compact set resulting an invariant set for the closed-loop trajectories.

This paper addresses robust adaptive stabilization of uncertain switched time-delay systems with unknown disturbances in a high-order form. In addition to parametric uncertainty, an extra source of uncertainty arises from having time-varying delays. In particular, the upper bound of the changing rate of the delay is assumed to be unknown. To this purpose, a new reparametrization method is proposed, which incorporates both sources of uncertainty in a single scalar parameter. Therefore, a single scalar adaptive law is designed for parametric uncertainties and time-varying delays, which is combined with a new dynamic gain embedded in the control action. The new dynamic gain is designed to dominate the nonlinear effects caused by both the parametric uncertainty and the unknown variation of the time delay. The proposed design guarantees global asymptotic stability for arbitrary switching. A numerical example illustrates the effectiveness of the method.

Managing grid-connected charging stations for fleets of electric vehicles leads to an optimal control problem where user preferences must be met with minimum energy costs (e.g., by exploiting lower electricity prices through the day, renewable energy production, and stored energy of parked vehicles). Instead of state-of-the-art charging scheduling based on open-loop strategies that explicitly depend on initial operating conditions, this paper proposes an approximate dynamic programming feedback-based optimization method with continuous state space and action space, where the feedback action guarantees uniformity with respect to initial operating conditions, while price variations in the electricity and available solar energy are handled automatically in the optimization. The resulting control action is a multi-modal feedback, which is shown to handle a wide range of operating regimes, via a set of controllers whose action that can be activated or deactivated depending on availability of solar energy and pricing model. Extensive simulations via a charging test case demonstrate the effectiveness of the approach.