This paper presents a model-free solution to the robust stabilization problem of discrete-time linear dynamical systems with bounded and mismatched uncertainty. An optimal controller design method is derived to solve the robust control problem, which results in solving an algebraic Riccati equation (ARE). It is shown that the optimal controller obtained by solving the ARE can robustly stabilize the uncertain system. To develop a model-free solution to the translated ARE, off-policy reinforcement learning (RL) is employed to solve the problem in hand without the requirement of system dynamics. In addition, the comparisons between on- and off-policy RL methods are presented regarding the robustness to probing noise and the dependence on system dynamics. Finally, a simulation example is carried out to validate the efficacy of the presented off-policy RL approach.

Realistic and diverse simulation scenarios with reactive and feasible agent behaviors can be used for validation and verification of self-driving system performance without relying on expensive and time-consuming real-world testing. Existing simulators rely on heuristic-based behavior models for background vehicles, which cannot capture the complex interactive behaviors in real-world scenarios. Meanwhile, existing learning-based methods are typical insufficient, yielding behaviors of traffic participants that frequently collide or drive off the road especially for a long horizon. To address this issue, we propose TrajGen, a two-stage trajectory generation framework, which can capture more realistic and diverse behaviors directly from human demonstration. In particular, TrajGen consists of the multi-modal trajectory prediction stage and the reinforcement learning based trajectory modification stage. In the first stage, we propose a novel auxiliary RouteLoss for the trajectory prediction model to generate multi-modal diverse trajectories in the drivable area. In the second stage, reinforcement learning is used to track the predicted trajectories while avoiding collisions, which can improve the reactivity and feasibility of generated trajectories. In addition, we develop a simulator I-Sim that can provide support for data-driven agent behavior simulation and train reinforcement learning models in parallel based on naturalistic driving data. The vehicle model in I-Sim can guarantee that the generated trajectories by TrajGen satisfy vehicle kinematic constraints. Finally, we give comprehensive metrics to evaluate generated trajectories for simulation scenarios, which shows that TrajGen outperforms either trajectory prediction or inverse reinforcement learning in terms of fidelity, reactivity, feasibility, and diversity.

This article is concerned with the design of robust resilient control strategy for nonlinear systems under Denial-of-Service (DoS) attacks. First, Takagi–Sugeno fuzzy model is employed to approximate the nonlinear dynamics, and an improved sensor system is constructed by fuzzy observer and fuzzy predictor. By applying periodic event-triggered control strategy, the relationship between the estimated state and predicted state is obtained by event-triggering mechanism, which can reduce transmission attempts significantly in the sensor-to-controller channel. Second, caused by DoS attacks, the transmission attempts may be denied over the communication network. Under the proposed transmission policy with a bounded update period, input-to-state stability (ISS) of closed-loop systems can be guaranteed. Moreover, the maximum frequency and duration of DoS attacks are calculated. Finally, simulation results are provided to show the effectiveness of the proposed method.

This article focuses on developing a static output feedback (SOF) control scheme for Takagi–Sugeno fuzzy systems. The proposed SOF controller does not share the same premise membership functions with the model, which permits enhancing the flexibility in controller design and implementation. By contrast with the state feedback case, the SOF control generally results in nonconvex design conditions. To circumvent this problem, we develop a successive convex optimization algorithm, which is based on solving a sequence of more tractable convex optimization problems obtained by approximating the nonconvex constraints with some convex ones. As a heuristic algorithm, the validity of the developed successive convex optimization algorithm is highly affected by initial conditions, and, recognizing this, we put forward an iterative procedure for determining the feasible initial condition. Finally, two illustrative examples are presented to validate the efficiency of the proposed algorithms.

This article investigated the tracking control problem for a category of state-constrained high-order uncertain nonlinear cyber-physical systems (CPSs) with unmodeled dynamics, which are subject to malicious attacks launched in controller-actuator (C-A) channel. Combining the backstepping method, dynamic surface control (DSC) approach and fuzzy logic systems (FLSs), a novel adaptive fuzzy tracking control strategy is developed, where a one to one mapping is brought in to transform the state-constrained CPSs to be the ones without state constraints and a dynamical signal is introduced to cope with the unmodeled dynamics. The proposed control strategy is not only independent of the exact system model, but also accommodates the external disturbances, unmodeled dynamics and malicious attacks. In particular, the developed control strategy can make sure that the output tracking error enters a predefined small neighborhood of the origin, and guarantee all the signals of the resulting closed-loop system satisfy the corresponding state constraints during the total operation, where the tracking accuracy level are known and can be preconfigured by selecting the design parameters appropriately. Ultimately, a representative simulation is provided to illustrate the validity and superiority of the proposed control scheme.