Time-varying Target Research Articles

Sliding-mode control for target tracking of omnidirectional mobile robots subject to impulsive deception attacks

This article focuses on the target tracking of omnidirectional mobile robots (OMRs) subject to impulsive deception attacks. The impulsive deception attacks describe the cyber attacks causing instantaneous changes of system states at uncertain moments. To resist such kind of attacks, sliding-mode control (SMC) strategies are designed for the global asymptotic stability of the tracking errors of the OMRs. With the designed sliding-mode controller, the OMRs are capable of tracking the fixed and time-varying target in an experiment platform consisting of OMRs, wireless router and control center, which shows the robustness of the system with respect to impulsive deception attacks and the effectiveness of theoretical results.

Affine formation maneuver control of underactuated surface vessels: Guaranteed safety under moving obstacles in narrow channels

This research tackles the challenge of dynamic formation control and collision avoidance in underactuated surface vessels (USVs). Firstly, an affine transformation is employed to implement arbitrary configuration transformations within the multi-USV system. Secondly, by considering both distances and velocities between USVs and obstacles, a novel collision avoidance scheme is proposed based on conventional distance-based artificial potential field methods. This method, building on traditional distance-based artificial potential fields, offers a more precise assessment of collision risks, thereby notably shrinking the potential collision zones. Such refinement leads to reduced energy consumption and minimizes disruption to formation integrity. Thirdly, an anti-perturbation formation maneuver safety control scheme is introduced to achieve a time-varying target formation without collisions, ensuring that local position tracking errors remain bounded. The effectiveness of the proposed approaches is substantiated through comprehensive theoretical analysis and extensive numerical simulations, underscoring their practical applicability.

An adaptive error-based observer method in electro-optical tracking system

Electro-optical tracking system which is used to track and aim at targets, is a key equipment in long-distance laser communication, astronomical observation, and so on. And the motion state of the target and the complicated external disturbance often limit the tracking accuracy of the system. In previous studies, target tracking and disturbance suppression are usually discussed separately. In this paper, in order to solve the tracking and disturbance problems together, the error-based observer (EOB) structure was proposed. It is a feedforward control method that only needs an error signal to observe the target trajectory and external disturbances. And it can be used to improve the tracking and anti-disturbance performance of the system without additional sensors. However, in the actual scene, the target motion and external disturbance faced by the electro-optical tracking system are often variable. So an adaptive error-based observer (AEOB) structure based on signal identification and adaptive compensation was proposed, which can reduce the effect of time-varying target motion and narrowband disturbance on tracking accuracy. And the adaptive process of AEOB contains AR parameter model identification with high precision and adaptive feedforward of variable parameter Q filter. In addition, an improved notch Q filter is proposed, which had stronger feedforward capabilities for narrowband signals. Finally, the effectiveness is verified by simulations and experiments of the electro-optical tracking system.

Hierarchical Deep Reinforcement Learning-Based Propofol Infusion Assistant Framework in Anesthesia.

This article aims to provide a hierarchical reinforcement learning (RL)-based solution to the automated drug infusion field. The learning policy is divided into the tasks of: 1) learning trajectory generative model and 2) planning policy model. The proposed deep infusion assistant policy gradient (DIAPG) model draws inspiration from adversarial autoencoders (AAEs) and learns latent representations of hypnotic depth trajectories. Given the trajectories drawn from the generative model, the planning policy infers a dose of propofol for stable sedation of a patient under total intravenous anesthesia (TIVA) using propofol and remifentanil. Through extensive evaluation, the DIAPG model can effectively stabilize bispectral index (BIS) and effect site concentration given a potentially time-varying target sequence. The proposed DIAPG shows an increased performance of 530% and 15% when a human expert and a standard reinforcement algorithm are used to infuse drugs, respectively.

Distributed time-varying optimization control protocol for multi-agent systems via finite-time consensus approach

This paper addresses a distributed time-varying optimization problem with inequality constraints based on multi-agent systems over switching communication graphs. To reduce the influence of time-varying inequality constraints, an exact penalty method and smoothing technique are employed. Then, a Hessian-based distributed control protocol is presented to seek the time-varying optimal solution of the distributed time-varying optimization problem by virtue of only local information and interaction. It is shown that all agents not only achieve finite-time consensus but also track the time-varying global optimal target eventually. Compared with the existing distributed optimization protocols, the proposed control protocol is suitable for more general distributed time-varying optimization problems and enjoys high-efficiency convergence. Finally, numerical examples and experiment on moving target tracking of Unmanned Aircraft Vehicle (UAV) are performed to illustrate the effectiveness of the proposed control protocol.

Dynamic Regret Bounds for Constrained Online Nonconvex Optimization Based on Polyak–Lojasiewicz Regions

Online optimization problems are well-understood in the convex case, where algorithmic performance is typically measured relative to the best fixed decision. In this paper, we shed light on online nonconvex optimization problems in which algorithms are evaluated against the optimal decision at each time using the more useful notion of dynamic regret. The focus is on loss functions which are arbitrarily nonconvex, but have global solutions that are slowly time-varying. We address this problem by first analyzing the region around the global solution at each time to define time-varying target sets, which contain the global solution and exhibit desirable properties under the projected gradient descent algorithm. All points in a target set satisfy the proximal Polyak-Łojasiewicz inequality, among other conditions. Then, we introduce two algorithms and prove that the dynamic regret for each algorithm is bounded by a function of the temporal variation in the optimal decision. The first algorithm assumes that the decision maker has some prior knowledge about the initial objective function and may query the gradient repeatedly at each time. This algorithm ensures that decisions are within the target set at every time. The second algorithm makes no assumption about prior knowledge. It instead relies on random sampling and memory to find and then track the target sets over time. In this case, the landscape of the loss functions determines the likelihood that the dynamic regret will be small. Numerical experiments validate these theoretical results and highlight the impact of a single low-complexity problem early in the sequence.

Adaptive Modified Unbiased Minimum-Variance Estimation for Highly Maneuvering Target Tracking With Model Mismatch

Due to the complexity, time variability, and mutability of the noncooperative maneuvering target, the dynamic tracking model of the target does not match with the actual state, which reduces the tracking accuracy or even completely tracking loss. In this article, an expectation maximization-based adaptive modified unbiased minimum-variance estimation (EM-AMUMVE) algorithm is proposed for highly maneuvering target tracking with model mismatch. First, the virtual maneuvering noise and the first-order Markov process model are integrated to quantitatively describe the maneuvering acceleration according to the boundness of the maneuvering acceleration. Then, the maneuvering acceleration and state estimation are derived based on the unbiased minimum-variance criterion, and the expectation maximization (EM) is introduced in the updating procedure of the quadratic Bayesian filter to adaptively estimate the mean and covariance of virtual maneuvering noise and thus to improve the accuracy of maneuvering acceleration estimation. Finally, the optimality of the adaptive modified maneuvering acceleration estimation is theoretically proved, and the tracking scenarios of time-varying maneuvering target with model mismatch and reentry gliding trajectory of typical hypersonic vehicle hypersonic technology vehicle 2 (HTV-2) are built to demonstrate the validness, convergence, sensitivity, and engineering practicability of the proposed algorithm.

A novel framework for generalizing dynamic movement primitives under kinematic constraints

In this work, we propose a novel framework for generalizing a desired trajectory pattern, encoded using Dynamic Movement Primitives (DMP), subject to kinematic constraints. DMP have been extensively used in robotics for encoding and reproducing kinematic behaviours, thanks to their generalization, stability and robustness properties. However, incorporating kinematic constraints has not yet been fully addressed. To this end, we design an optimization framework, based on the DMP formulation from our previous work, for generalizing trajectory patterns, encoded with DMP subject to kinematic constraints, considering also time-varying target and time duration, via-point and obstacle constraints. Simulations highlight these properties and comparisons are drawn with other approaches for enforcing constraints on DMP. The usefulness and applicability of the proposed framework is showcased in experimental scenarios, including a handover, where the target and time duration vary, and placing scenarios, where obstacles are dynamically introduced in the scene.

Probabilistic reach-Avoid problems in nondeterministic systems with time-Varying targets and obstacles

The probabilistic reachability problem, which involves the computation of probabilistic reachable sets, is studied for nondeterministic systems. In the existing works, the system evolution initialized from the probabilistic reachable set is required to reach the target set with a certain probability in a given time horizon. In this paper, the definition of probabilistic reachable sets is refined by taking into account time-varying target set and obstacle. In the context of this definition, the evolution of the system is required not only to reach the target set but also to avoid obstacle. We address two distinct interpretations of probabilistic reachability problem via dynamic planning. In the first case, the control policy is given. In the second case, the control policy is a parameter to be optimized. A numerical method is proposed to compute probabilistic reachable sets. First, a scalar function in the state space is constructed by backward recursion and grid interpolation, and then the probabilistic reachable set is represented as a nonzero upper level set of this scalar function. In addition, based on the constructed scalar function, the optimal control policy can be designed. Two examples are provided at the end of this article. The first example consists of a one-dimensional problem where the analytical solution is compared with the results of the proposed method in order to analyze the computational accuracy. The second example, which contains a three-dimensional problem, is used to demonstrate the effectiveness of the proposed method.

Completely Distributed Time-Varying Formation Target Tracking for Quadrotor Team via Image-Based Visual Servoing

In this article, the completely distributed adaptive time-varying formation target tracking control problem for quadrotor team via image-based visual servoing is addressed. An adaptive cascade formation controller is proposed, which consists of a trajectory tracking controller, an attitude controller, and an image-based visual servoing controller. All the controllers consist of a nominal part to achieve the desired performance and a robust part to retrain the influence of parameter uncertainties and external disturbance. The robust properties of the closed-loop control system are proven via the theoretical analysis. Moreover, the simulation results validate the effectiveness of the proposed formation tracking control protocol in this paper.

Estimations of time-varying birth cardinality distribution and birth intensity in Gaussian mixture CPHD filter for multi-target tracking

Cardinalized probability hypothesis density (CPHD) filter is a promising algorithm for multi-target tracking. However, the performance of standard CPHD is degraded when the target birth process is unknown and dynamically changing. To remove this limitation, a discrete kernel estimator in conjunction with exponential weighted moving average scheme is introduced in this study to estimate the time-varying target birth cardinality distribution (i.e., probability distribution on number of newborn targets appearing during one sampling time) at each processing step. The target birth intensity is then updated according to the resulting estimated birth cardinality distribution. The estimated birth intensity and cardinality distribution can be employed by a tracker based on Gaussian mixture CPHD (GMCPHD) to modulate its filtering strength for target tracking. The performance of the proposed framework is demonstrated by both numerical simulations and experiment on a video surveillance dataset. Results show that the proposed algorithm can reduce the delay of standard GMCPHD filter's response to the changes in the number of targets, and thus improve the accuracy of cardinality estimates. Satisfactory estimation of cardinality can be obtained even when there is a fast change in the rate of birth.

NEWS SHOCKS AND THE EFFECTS OF MONETARY POLICY

Traditionally identified monetary shocks in a structural vector autoregression (SVAR) model typically result in long-lasting effects on output and total factor productivity (TFP). In this paper, I argue that the typical monetary shock has been confounded with the news shock about future technology. I propose and implement a novel SVAR approach that effectively “cleans” the technology component from the traditional Cholesky monetary shock. With the new identification, I find that a monetary shock exerts smaller and less persistent effects on output and the level of measured TFP than a traditionally identified monetary shock. Finally, I show that the SVAR impulse responses can be replicated by augmenting the standard New Keynesian model with a time-varying inflation target and a non-Ricardian fiscal policy regime.

How stable are corporate capital structures? International evidence

Abstract Using a large sample of firms from 43 markets, we find significant time-series variation in firms’ leverage ratios around the world. Industry median leverage ratios and aggregate leverage ratios also change substantially over time. Relative to actual leverage ratios, target leverage ratios estimated from the time-varying target models are much more stable. Variance decomposition shows that leverage instability is largely driven by deviations from the target. A number of firm and market characteristics are related to capital structure instability. We also find evidence consistent with firms using financing activities to adjust their leverage ratios towards the target in global markets.

How Stable Are Corporate Capital Structures? International Evidence

Using a large sample of firms from 43 markets, we find significant time-series variations in firms’ leverage ratios around the world. Industry median leverage ratios and aggregate leverage ratios also change substantially over time. Relative to actual leverage ratios, target leverage ratios estimated from the time-varying target models are much more stable. Variance decomposition shows that leverage instability is largely driven by deviations from the target. A number of firm and market characteristics are related to capital structure instability. We also find evidence consistent with firms using financing activities to adjust their leverage ratios towards the target in global markets.

Control Contraction Metric Synthesis for Discrete-time Nonlinear Systems

Flexible manufacturing has been the trend in the area of the modern chemical process nowadays. One of the essential characteristics of flexible manufacturing is to track time-varying target trajectories (e.g. diversity and quantity of products). A possible tool to achieve time-varying targets is contraction theory. However, the contraction theory was developed for continuous time systems and there lacks analysis and synthesis tools for discrete-time systems. This article develops a systematic approach to discrete-time contraction analysis and control synthesis using Discrete-time Control Contraction Metrics (DCCM) which can be implemented using Sum of Square (SOS) programming. The proposed approach is demonstrated by illustrative example.

Optimising Carbon Capture and Storage Supply Chains for the European Industry

Abstract Carbon capture and storage is considered of fundamental importance to achieve a remarkable decarbonisation of steel, cement and refining sectors. To operate carbon capture and storage at scale and address its inherent complexity, mathematical programming techniques can be exploited to optimise such systems. This contribution proposes a Europe-wide, spatially-explicit, time-dependent, carbon capture and storage chains optimisation, based on mixed integer linear programming architecture. Capture plants can be installed in all significant industrial CO2 emitters, which comprise 25 steel mills, 111 cement plants and 59 refineries. A techno-economic description of capture plants is provided, based on scale effects and different options. Transport can be operated through pipelines and offshore storage is taken into account in the North Sea and Adriatic area. The analysis allows identifying the most promising sectors and optimal specific plants where capture should be operated, and the evolution of the system throughout the time horizon. Considering a time-varying carbon reduction target, the avoidance cost is 75.6 €/t of CO2 for a North Sea targeted network, and decreases by 1.9% when sequestration in the Adriatic Sea is also taken into account.

Wearable Biofeedback System to Induce Desired Walking Speed in Overground Gait Training.

Biofeedback systems have been extensively used in walking exercises for gait improvement. Past research has focused on modulating the wearer’s cadence, gait variability, or symmetry, but none of the previous works has addressed the problem of inducing a desired walking speed in the wearer. In this paper, we present a new, minimally obtrusive wearable biofeedback system (WBS) that uses closed-loop vibrotactile control to elicit desired changes in the wearer’s walking speed, based on the predicted user response to anticipatory and delayed feedback. The performance of the proposed control was compared to conventional open-loop rhythmic vibrotactile stimulation with N = 10 healthy individuals who were asked to complete a set of walking tasks along an oval path. The closed-loop vibrotactile control consistently demonstrated better performance than the open-loop control in inducing desired changes in the wearer’s walking speed, both with constant and with time-varying target walking speeds. Neither open-loop nor closed-loop stimuli affected natural gait significantly, when the target walking speed was set to the individual’s preferred walking speed. Given the importance of walking speed as a summary indicator of health and physical performance, the closed-loop vibrotactile control can pave the way for new technology-enhanced protocols for gait rehabilitation.

Affine formation maneuver control of high-order multi-agent systems over directed networks

To drive a group of agents to maneuver continuously with the desired collective forms, this paper addresses a distributed formation maneuver control problem of directed networked high-order multi-agent systems in arbitrary dimensions. Unlike the conventional methods where the target formation is time invariant, we propose an affine formation method based on the properties of affine transformation, in which the target formation can be time-varying and affinely transformed from the given nominal formation. This paper provides and proves a sufficient and necessary condition of achieving the directed graphical affine localizability, and it only needs that the leaders have a generic configuration and the followers are accessible to the subset of leaders. To achieve the whole formation maneuvers, assume that the leaders decide the whole formation’s maneuver actions, then the control algorithms of the arbitrary-order integrator followers are proposed to track the time-varying target formation and the global convergence of tracking errors is also proved. Furthermore, this paper studies the practical problems of formation maneuvers when existing non-uniform time delays. Finally, both two-dimensional and three-dimensional simulation examples are given to demonstrate the effectiveness of theoretical results.

Mean-variance dynamic optimality for DC pension schemes

In this paper we deal with the mean-variance portfolio selection for a defined contribution (DC) pension fund. Since this problem is time-inconsistent, a number of papers have proposed to tackle it through either a Nash equilibrium approach or a precommitment strategy. Here, we adopt the dynamically optimal approach introduced by Pedersen and Peskir (Math Financ Econ 11:137–160, 2017), and we compare the dynamically optimal strategy with the precommitment one. While it is well known that the precommitment strategy is the solution to a target-based problem, we show that the same holds for the dynamically optimal strategy. In particular, the precommitment strategy has a constant target, while the dynamically optimal strategy has a time-varying target whose expectation coincides with the constant target of the previous case. We also show that the expected wealth is the same under the two approaches. Numerical applications show that (i) the median of the risky asset’s share is lower for the precommitment than the dynamically optimal strategy; (ii) the amount of money invested in the precommitment risky portfolio is highly more volatile than in the dynamically optimal case; (iii) the variance of wealth is lower with the precommitment strategy than with the dynamically optimal one; (iv) under scenarios of extreme market returns (either good or bad), the dynamically optimal strategy allows a more effective reaction because of the continuous adjustment of the final target.

Adaptive Control for Mechanical Ventilation for Improved Pressure Support

Respiratory modules are medical devices used to assist patients to breathe. The aim of this article is to develop a control method that achieves exact tracking of a time-varying target pressure, for unknown patient-hose-leak parameters and in the presence of patient breathing effort. This is achieved by an online estimation of the hose characteristics that enables compensation for the pressure drop over the hose. Stability of the closed-loop system is proven, and the performance improvement compared to the existing control strategies is demonstrated by simulation and experimental case studies.