To address time delays commonly observed in industrial thermal processes, this article proposes an observer-based Robin boundary control strategy for a class of convection-coupled thermal models. Traditional infinite-dimensional control approaches are often constrained by the simultaneous presence of delayed Robin boundary actuation and convection-coupled dynamics, which prevents the direct use of standard modal decoupling and predictor-based compensation techniques. To overcome this challenge, a lifting function and coordinate transformation are introduced to convert the system into a homogeneous form. This transformation ensures the orthogonality of the weighted Sturm–Liouville eigenfunctions, thereby facilitating the analytical computation of system coefficients through spectral decomposition. Additionally, a finite-dimensional observer is constructed using measurements from a limited number of points, and an integral predictor is designed as the boundary controller to compensate for time delays, enabling precise temperature regulation. The closed-loop stability of the system is established via an explicit Artstein transformation and a carefully designed Lyapunov function. Simulation and experimental results demonstrate that the proposed approach significantly outperforms traditional methods by mitigating actuator saturation and reducing temperature overshoot by 12%, thereby validating its practical value in complex heat transfer processes.
In the above article [1], there were errors on pages 2874 and 2875. They are explained below, and the corrections are provided.
A constrained adaptive actor-critic (AAC)-based policy iteration controller for vision-based shape servoing (VSS) of a deformable object is presented in this article. A finite point-based representation of the deformable object is used, and the deformation Jacobian matrix is approximated using Fourier-series basis functions. An integral concurrent learning (ICL)-based parameter update law is designed to update the deformation model parameters. The high-dimensional state representation of the object requires a large number of parameters to learn the deformation model, value function, and the optimal policy. To address this, a principal component analysis (PCA)-based dimensionality reduction method is used to compute a low-dimensional representation of the object, which is then used to design the constrained AAC controller. A barrier transform (BT) method is used to transform part of the state into a constrained state, thereby ensuring that the physical constraints are satisfied. The proposed controller is extensively tested in simulations across several desired configurations, and through Monte Carlo runs on an ABB IRB120 robot platform.
Although existing methods can steer unmanned surface vehicle (USV) to stably maintain their position at the desired point, poor robustness and slow convergence can be observed in the presence of fast time-varying environmental disturbance. In this article, novel robust positioning laws based on a heading parameter tuning method are proposed to enhance robustness and accelerate convergence of positioning control. By taking the amplitude of resultant environmental disturbance force as the coefficients of the rotation angle adaptive law and the desired heading law, robust positioning laws are developed to weaken the impact of external disturbance. Additionally, the heading parameter tuning method is designed to determine the bounds of both heading controller parameter and the desired heading law parameter. When these parameters approach their boundary values, the sensitivity of the heading error controller can be enhanced, enabling rapid minimization of track errors. Both numerical simulations and field experiments demonstrate that the proposed methodology can achieve rapid convergence of track errors and maintain stable positioning at the desired location under fast time-varying environmental disturbance.
Direct teleoperation of vehicles faces critical technical bottlenecks: communication latency and the operator's inability to physically perceive unmodeled environmental disturbances (e.g., aerodynamic drag, bank angles) coupled with highly nonlinear tire-road dynamics. To address these challenges, we propose a tailored unilateral teleoperation framework. The system integrates the Wave Variable (WV) approach to passively guarantee stability under stochastic delays, and an adaptive Radial Basis Function Network (RBFN) to actively compensate for vehicle-specific uncertainties. Unlike existing WV-neural network architectures designed for bilateral robotic arms, our framework features decoupled adaptive laws specifically designed for vehicle longitudinal and lateral dynamics. Furthermore, compared to model-heavy predictive controllers, the model-free RBFN offers rapid online adaptation without heavy computational overhead. Building upon our preliminary theoretical formulation, this brief paper presents comprehensive comparative analyses and real-world hardware validations. Simulation benchmarks against PID, LQR, MPC, and NMPC demonstrate that the RBFN achieves superior robustness against unmodeled disturbances while requiring orders of magnitude less execution time than MPC and NMPC, making it ideal for resource-constrained vehicle edge computing. Finally, hardware-in-the-loop experiments using a 1/10th scale vehicle over a 4G network validate the system's practical feasibility, safety, and robust trajectory tracking under physical road uncertainties.
This article proposes a modular semi-active vibration control unit formed by a piezoelectric patch with a model-free self-tuning analog shunt, which can be bonded in batches on flexible structures to control the resonant responses of low-order flexural modes. Unlike conventional self-tuning systems, this unit does not require additional sensors or digital control. On the contrary, it relies on an analog shunt, which implements an extremum-seeking algorithm that sets the shunt to maximize the time-averaged electrical power absorbed from the vibration of the target flexural mode. The analog shunt includes a separate opto-coupled tuning board, which ensures stable operation and avoids unwanted electrical interactions. In first place, this article describes the architecture of the self-tuning analog shunt and provides general guidelines on how the dithering signal of the extremum seeking algorithm affects its stability and convergence speed. Then, it presents practical tests where five units are bonded on a thin plate to control the resonant response of a single or multiple flexural modes. More specifically, two configurations are investigated: in the first, all units are set to control the resonant response of a specific target mode, which shows reductions of 20–30 dB, whereas in the second, the units are set to control separate target modes, which show reductions of 10–15 dB. These results confirm the potential of the proposed unit, which can be miniaturized and mass-produced to operate straightforwardly on a wide range of structures.
This article presents an adaptive observer framework for lithium-ion batteries in parallel configuration. The method leverages a descriptor system formulation to capture the coupled dynamics of individual cells while enforcing Kirchhoff’s laws as algebraic constraints. Based on this structure, a parallel architecture model is formulated to enable adaptive estimation of cell-level states and key parameters, particularly charge capacity, using only total voltage and current measurements. An adaptive observer is then designed using Lyapunov stability theory to guarantee convergence of both state and parameter estimates. Numerical simulations under dynamic current profiles demonstrate accurate reconstruction of the state-of-charge (SOC), cell-level currents, and charge capacities of individual cells, even in the presence of large initial condition mismatches and parameter heterogeneity. Experimental validation using a two-cell parallel configuration further confirms the effectiveness of the approach, with reliable estimation of SOCs, branch currents, and capacities, and robust performance under cell aging and current-sharing imbalances. These results highlight the practical viability of the proposed framework for advanced battery management systems (BMSs) to perform state estimation and state-of-health (SOH) monitoring without requiring cell-level sensing hardware.
Multirobot coverage control becomes particularly challenging when multiple robots must simultaneously leave the workspace to recharge, disrupting communication and connectivity. To address this challenge, we propose an energy-aware bearing-rigidity-based resilient network design and a nonlinear model predictive control (MPC) framework that enables robots to achieve coverage objectives while satisfying energy constraints and preserving network connectivity. The coupled motion and energy dynamics are modeled as a hybrid system with three modes: 1) coverage; 2) return-to-base; and 3) recharge. Energy constraints are enforced by carefully designed guard conditions that govern transitions between modes. In addition, we introduce an energy-aware self-organizing hierarchy within the bearing rigid network that enables systematic network maintenance and reconfiguration, thereby enhancing resilience even when multiple robots temporarily leave to recharge. Finally, we demonstrate the effectiveness of the proposed approach through numerical simulations.
The direct AC/AC modular multilevel converter (MMC) is a novel topology that offers excellent efficiency and performance in applications requiring high or medium power transfer. Achieving the desired MMC operation depends on the capacity to control MMC currents, so that they track sinusoidal references while keeping the states and control inputs in a safe operating region. Therefore, this article presents an indirect multivariable current controller (IMVCC), i.e., a novel static feedback controller capable of tracking sinusoidal references while satisfying input and state constraints. In this case, we leverage Sylvester’s equation for tracking and linear matrix inequalities (LMIs) to synthesize a controller that guarantees global closed-loop stability. Unlike the existing tracking controller design approaches, the proposed method explicitly incorporates constraints in the synthesis process. Thus, the resulting LMI allows us to compute an attracting invariant region that is constraint-admissible where the tracking error is guaranteed to be exponentially stable. Finally, we use input-to-state stability (ISS) notions to derive the conditions that must be met to keep the MMC’s state dynamics stable and contained in a safe ellipsoidal operation region. We evaluate the efficacy of this method on a scaled-down direct AC/AC MMC prototype design for electric vehicle charging research. Furthermore, we demonstrate the proposed method’s ability to enforce constrained control by comparing it with a tracking model predictive controller.
In this brief, we consider a decentralized framework of collaboratively manipulating a rigid object to track a desired trajectory on $\mathbb {SE}$ (3). Only a subset of robots, called the tractors, knows the desired trajectory, while the others, called the laborers, do not have access to the desired trajectory information. Moreover, the robots do not know the exact model parameters. We first design decentralized estimators for the laborers to estimate the desired trajectory and the dynamics parameters, and then we propose a decentralized controller to achieve cooperative manipulation. The unknown parameters are estimated using the dynamic regressor extension and mixing (DREM) approach, where parameter convergence to the true value is guaranteed when the condition of persistent excitation is met. The proposed framework does not need a communication network between robots. Numerical and physical simulations verify the effectiveness of the proposed framework.
In this article, from a pure discrete-time point of view, considering nonuniform sampling periods and uncertain large delays, exponential stability analysis with a given decay rate and controller design are investigated by applying integral quadratic constraints (IQCs) theory for load frequency control (LFC) of a power system with renewable energy sources (RESs). First, a decentralized dynamic model of a nonuniform sampled-data LFC scheme with RESs is structured by considering uncertain delays. Second, when delays may be smaller or larger than the sampling interval, control inputs that are available in one sampling interval are precisely determined. Then, a discrete-time model is derived for the modeled LFC system, which includes message rejection. Based on the uncertain discrete-time model, a feedback interconnection of a nominal linear time-invariant system and a perturbation operator is constructed, where the perturbation contains variable sampling intervals and uncertain delays. Third, scaling operators are introduced to obtain a related scaled feedback interconnection, and a matrix multiplier is constructed, which can specify IQC for the operator. Next, based on the equivalence between the exponential stability of feedback interconnection and the linear stability of a related scaled interconnection, an exponential stability criterion with a decay rate is developed for the resultant LFC system under the framework of IQC theory. Moreover, by using matrix decoupling to handle nonlinear terms in stability conditions, an improved LFC scheme is designed, and the corresponding heuristic algorithm is presented to find controller gains. Finally, based on the one-area power system, three-area power system, and IEEE 39-bus system, case studies are conducted for LFC of power systems to demonstrate the validity and advantages of the presented results.
The Bowden cable transmission system is employed in wearable exoskeleton devices because of its ability to transmit power remotely. However, achieving precise motion control in Bowden cable-driven exoskeletons is challenging due to two main factors: 1) Bowden cable transmission brings nonlinearities associated with cable configurations and 2) cable configurations can change at any time due to the wearer’s movements. In this study, we achieve finite-time tracking control for Bowden cable-driven exoskeletons with time-varying cable configurations. First, a general dynamic model is established for a class of multiple-degree-of-freedom (DOF) Bowden cable-driven exoskeletons. Then, based on a suitably defined nonsingular terminal sliding vector and Lyapunov stability theory, a novel robust controller is proposed for Bowden cable-driven exoskeletons. This controller does not rely on the accurate model parameters and can guarantee that the tracking error converges to the origin within a finite time. Notably, unlike existing controllers, which are available only when cable configurations remain invariant or vary slowly, the proposed controller can achieve precise control even under time-varying cable configurations. Finally, we build a Bowden cable-driven hip exoskeleton and validate the effectiveness of the proposed method through simulations and experiments.
Autonomous robot navigation in crowded human environments must ensure both physical safety and pedestrian psychological comfort. Optimization-based methods such as model predictive control (MPC) are well-suited to this task due to their ability to handle hard and soft constraints. This brief extends a control strategy for unicycle robots, including personal mobility vehicles, to trajectory tracking in human crowded settings. A linear MPC controller exploiting an inner feedback-linearizing loop is proposed, incorporating obstacle avoidance that explicitly accounts for pedestrian comfort, and directly imposing linear velocity constraints on the actuation variables without approximation. Simulation results in a realistic, complex scenario demonstrate the effectiveness of the approach.
This study combines recent developments in computer science and control theory to develop an auto-tuning mechanism for controllers in unmanned aerial vehicles (UAVs). An optimal nonparametric auto-tuning framework is presented that integrates homogeneity theory with deep reinforcement learning (DRL) for UAV controller tuning. The method involves generation of tuning rules (formulas relating the results of tests and controller parameters) by means of a deep neural network (DNN) trained via DRL using the modified relay feedback test (MRFT)-induced oscillations; the trained network can map shape features to tuning rules and the homogeneity-based relationships map the measured amplitude and period of the test oscillations to PD/proportional–integral–derivative (PID) controller gains. Therefore, the controller tuning parameters are obtained from the amplitude and frequency of the test oscillations through the mappings (tuning formulas) that are generated by means of the DNN. The developed controller auto-tuning approach is valid for an arbitrary UAV from the considered class due to the proven homogeneity property of the test-and-tuning mapping. The auto-tuner successfully performs proportional-derivative controller tuning for the UAV dynamics requiring only a fewseconds of the test and a few milliseconds for the controller parameters computation. The effectiveness of the tuning approach is demonstrated by both simulation and experiments; a video demonstration is available in https://www.youtube.com/watch?v=o7_Ubm2h0F4&t=3s
This brief proposes an adaptive genetic algorithm (AGA) for aerodynamic parameter identification of a powered parafoil model that explicitly accounts for parafoil–payload coupled dynamics. The mutation rate is adaptively adjusted according to population fitness, which improves convergence and enhances identification performance under nonlinear dynamics. Flight-test data are used for parameter estimation and validation. Comparative results show that the identified model achieves the closest agreement with the measured trajectory, outperforming a conventional GA-based baseline and a simplified model that neglects relative parafoil–payload motion. The proposed approach offers a practical identification framework for powered parafoil systems.
To reduce CO2 emissions and tackle increasing fuel costs, the aviation industry is swiftly moving toward the electrification of aircraft. From the viewpoint of systems and control, a key challenge brought by this transition corresponds to the management and safe operation of the propulsion system's onboard electrical power distribution network. Motivated by this transition, in this work, we propose a distributed adaptive controller for regulating the voltage of a DC bus in a class of DC power distribution networks inspired by a series-hybrid-electric propulsion system. The proposed controller-whose design is based on principles of backstepping, adaptive, and passivity-based control techniques-also enables the proportional sharing of the electric load among multiple converter-interfaced sources, which reduces the likelihood of overstressing individual sources. Compared to existing control strategies, our method ensures stable, convergent, and accurate voltage regulation and load sharing even if the effects of power lines of unknown resistances and inductances are considered. The performance of the proposed control scheme is experimentally validated and compared to state-of-the-art controllers in a power hardware-in-the-loop (PHIL) environment.
Due to the fluctuations in renewable energy generation, renewable energy-based electric vehicle (EV) charging stations often face energy imbalance issues. This article constructs a potential game that can uniformly simulate the interactions between battery EVs (BEVs), gasoline vehicles (GVs), and hybrid EVs (HEVs) in the traffic network. To better reflect practical scenarios, vehicle owners are modeled as autonomous decision-makers. We show that their self-interested behaviors converge to a Nash equilibrium and derive an upper bound on the price of anarchy (PoA). Furthermore, we introduce the marginal pricing mechanism to guide vehicle owners’ Nash equilibrium toward improving charging stations’ energy balance. By leveraging the geometric properties between the cost function’s gradient and the boundary constraint function’s gradient, we classify and discuss the nine possible distributions of extremum points, subsequently deriving the analytical form of the extent of improvement in energy balance. Simulation results validate the effectiveness of the theoretical findings.
