The pursuit-evasion problem is widespread in nature, engineering, and societal applications. It is commonly observed in nature that predators often exhibit faster speeds than their prey but have less agile maneuverability. Over millions of years of evolution, animals have developed effective and efficient strategies for pursuit and evasion. In this paper, we provide a dynamic framework for the pursuit-evasion problem of unicycle systems, drawing inspiration from nature. First, we address the scenario with one pursuer and one evader by proposing an Alert-Turn control strategy, which consists of two efficient ingredients: a sudden turning maneuver and an alert condition for starting and maintaining the maneuver. We present and analyze the escape and capture results at two levels: a lower level of a single run and a higher level with respect to parameters’ changes. In addition, we provide a theorem with sufficient conditions for capture. The Alert-Turn strategy is then extended to more complex scenarios involving multiple pursuers and evaders by integrating aggregation control laws and a target-changing mechanism. By adjusting a ‘selfish parameter’, the aggregation control commands produce various escape patterns of evaders: cooperative mode, selfish mode, and their combinations. The influence of the selfish parameter is quantified, and the target-changing mechanism is explored from a statistical perspective. Our findings align closely with observations in nature. Finally, the proposed strategies are validated through numerical simulations that replicate some chasing behaviors of animals in nature.

Generalized adaptive feedforward cancellation with a reference sensor is considered to specifically suppress second harmonic torque oscillations with an ac fed propulsion system for an electric train. A single complex-valued design parameter is tracked through gradient-type adaptation. Both Cartesian and polar parameter representations are considered, resulting in quite varying convergence properties. Three different adaptation algorithms are proposed and evaluated using power lab experiments. At fixed operating conditions, a Cartesian form parameter adaptation is shown to be more robust to the choice of initial conditions, whereas a polar form representation shows better performance when covering a wide range of operating points.

This work conducts a systematic experimental evaluation of the state-of-the-art Reference Aware Model Predictive Controller (RA-MPC) for autonomous vehicles. The RA-MPC is a path-tracking controller, that maximizes tracking accuracy and comfort. The controller uses a kinematic vehicle model with a nonlinear curvature response table that adapts the steering response online to the vehicle and operating conditions. The adaptiveness and robustness of the controller are analyzed by evaluating the performance on a highway truck, loaded and empty mining trucks, and a city bus. Moreover, highway-like and city-like scenarios are performed using the exact same implementation and parameter settings for all vehicles. The controller and model adaption achieved a very good path tracking performance in all experiments, deviating at most 25 cm from the reference path.

We consider optimization algorithms designed for variable horizon model predictive control. Traditionally, such problems are considered intractable for real-time applications that require fast computations, as they need to solve multiple optimal control problems with varying horizons at each sampling instance. The main contribution is an algorithm that efficiently solves multiple optimal control problems with different prediction horizons in a recursive manner. This algorithm has been successfully implemented and integrated into the OSQP solver, resulting in a real-time controller that is both fast and reliable. To assess the effectiveness of the approach, we conducted evaluations in both a realistic simulation environment and on real hardware during outdoor flight experiments. Specifically, we focused on two distinct rendezvous maneuvers for autonomous landings of unmanned aerial vehicles. The results obtained from these evaluations further validate the practicality and efficacy of the proposed algorithm.

This paper presents decentralized solutions for addressing pursuit-evasion problems involving high-order in-tegrators with intracoalition cooperation and intercoalition confrontation. To ensure that the control strategies indepen-dent of the relative velocities, accelerations and higher order information of neighbors, we introduce distinct error vari-ables and hyper-variables. Consequently, this approach only requires agents to exchange position information or measure the relative positions of neighbors. The distributed strategies reflect the goals of intracoalition cooperation or intercoalition confrontation of the players. Additionally, we present the conditions for capture and formation control with exponential convergence for three cases: one-pursuer-one-evader, multiple-pursuer-one-evader, and multiple- pursuer- multiple-evader. The results show that the conditions depend on the structure of the communication graph, the weights in the control law, and the expected formation configuration. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation results.

This paper presents decentralized solutions for pursuit-evasion problems involving high-order integrators with intracoalition cooperation and intercoalition confrontation. Distinct error variables and hyper-variables are introduced to ensure the control strategies to be independent of the relative velocities, accelerations and higher order information of neighbors. Consequently, our approach only requires agents to exchange position information or to measure the relative positions of the neighbors. The distributed strategies take into consideration the goals of intracoalition cooperation or intercoalition confrontation of the players. Furthermore, after establishing a sufficient and necessary condition for a class of high-order integrators, we present conditions for capture and formation control with exponential convergence for three scenarios: one-pursuer-one-evader, multiple-pursuer-one-evader, and multiple-pursuer-multiple-evader. It is shown that the conditions depend on the structure of the communication graph, the weights in the control law, and the expected formation configuration. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation results.

In this paper, several inverse Kalman filtering problems are addressed, where unknown parameters and/or inputs in a filtering model are reconstructed from observations of the posterior estimates that can be noisy or incomplete. In particular, duality in inverse filtering and inverse optimal control is studied. It is shown that identifiability and solvability of the inverse Kalman filtering is closely related to that of an inverse linear quadratic regulator (LQR). Covariance matrices of model uncertainties are estimated by solving a well-posed inverse LQR problem. Identifiability of the considered inverse filtering models is established and least squares estimators are designed to be statistically consistent. In addition, algorithms are proposed to reconstruct the unknown sensor parameters as well as raw sensor measurements. Effectiveness and efficiency of the proposed methods are illustrated by numerical simulations.

We develop an algorithm to control an underactuated unmanned surface vehicle (USV) using kinodynamic motion planning with funnel control (KDF). KDF has two key components: motion planning used to generate trajectories with respect to kinodynamic constraints, and funnel control, also referred to as prescribed performance control (PPC), which enables trajectory tracking in the presence of uncertain dynamics and disturbances. We extend PPC to address the challenges posed by underactuation and control input saturation present on the USV. The proposed scheme guarantees stability under user-defined prescribed performance functions where model parameters and exogenous disturbances are unknown. Furthermore, we present an optimization problem to obtain smooth, collision-free trajectories while respecting kinodynamic constraints. We deploy the algorithm on a USV and verify its efficiency in real-world open-water experiments.

A major challenge in autonomous flights is unknown disturbances, which can jeopardize safety and cause collisions, especially in obstacle-rich environments. This letter presents a disturbance-aware motion planning and control framework for autonomous aerial flights. The framework is composed of two key components: a disturbance-aware motion planner and a tracking controller. The motion planner consists of a predictive control scheme and an online-adapted learned disturbance model. The tracking controller, developed using contraction control methods, ensures safety bounds on the quadrotor's behavior near obstacles with respect to the motion plan. The algorithm is tested in simulations with a quadrotor facing strong crosswind and ground-induced disturbances.

In this paper, the inverse Kalman filtering problem is addressed using a duality-based framework, where certain statistical properties of uncertainties in a dynamical model are recovered from observations of its posterior estimates. The duality relation in inverse filtering and inverse optimal control is established. It is shown that the inverse Kalman filtering problem can be solved using results from a well-posed inverse linear quadratic regulator. Identifiability of the considered inverse filtering model is proved and a unique covariance matrix is recovered by a least squares estimator, which is also shown to be statistically consistent. Effectiveness of the proposed methods is illustrated by numerical simulations.