This paper designs a high-performance adaptive controller for the uncertain model of hypersonic flight vehicles (HFVs) proceed by faulty and hysteretic actuators. A parameterized HFV model is derived, based on which adaptive tracking controllers for velocity and altitude are designed, sequentially. Compared with other adaptive strategies that mainly focus on the parametric uncertainty and asymptotic tracking, the utilization of prescribed performance control technique can largely improve the transient characteristics of HFVs. A novel handling on angleof- attack (AOA) is proposed with the help of barrier functions. As a result, the magnitude of AOA is limited to match the requirement of the scramjet. Partial loss of effectiveness (PLOE) of actuators is also taken into account, while the backlash hysteresis in aerodynamic control surfaces is accommodated by an adaptive inverse compensation. Corresponding analysis shows that both the limited AOA and the desired performance can be guaranteed despite PLOE of actuators. A simulation study is provided to verify the effectiveness of the proposed controller.

This article deals with the problem of mobile robot path planning in an unknown environment that contains both static and dynamic obstacles, utilizing a reinforcement learning approach. We propose an improved Dyna- ${Q}$ algorithm, which incorporates heuristic search strategies, simulated annealing mechanism, and reactive navigation principle into ${Q}$ -learning based on the Dyna architecture. A novel action-selection strategy combining $\varepsilon $ -greedy policy with the cooling schedule control is presented, which, together with the heuristic reward function and heuristic actions, can tackle the exploration-exploitation dilemma and enhance the performance of global searching, convergence property, and learning efficiency for path planning. The proposed method is superior to the classical ${Q}$ -learning and Dyna- ${Q}$ algorithms in an unknown static environment, and it is successfully applied to an uncertain environment with multiple dynamic obstacles in simulations. Further, practical experiments are conducted by integrating MATLAB and robot operating system (ROS) on a physical robot platform, and the mobile robot manages to find a collision-free path, thus fulfilling autonomous navigation tasks in the real world.

In this article, the finite-time observer-based sliding-mode control (SMC) problem is considered for stochastic Markovian jump systems (MJSs) with a deterministic switching chain (DSC) subject to time-varying delay and packet losses (PLs). First, the stochastic MJSs with DSC are appropriately modeled and the PLs case is characterized by using some Bernoulli random variables. Then, a nonfragile finite-time bounded sliding-mode observer is designed. Our objective is to propose a finite-time observer-based SMC approach such that for the above addressed system, the finite-time boundedness in a certain time interval can be guaranteed by giving sufficient criteria via the stochastic analysis skills and average dwell time (ADT) method. Moreover, a new robust finite-time sliding-mode controller can be designed to ensure reachability of the common sliding surface in the estimation space. Finally, a numerical example is provided to illustrate our theoretical results.

This article discusses the integral sliding mode control problem for a kind of periodically-impulsive uncertain reaction-diffusion systems (IURDSs). A novel integral sliding surface containing impulsive effects and RDTs is constructed, such that the impulsive effects for IURDSs can be removed. A novel sliding mode controller with impulsive effects is designed to ensure the reachability of the specified sliding surface in a finite time interval. By means of LMIs, the robust exponential stability of this kind of IURDSs are derived. Finally, a numerical example is provided to verify the availability of our findings.

This paper investigates the problem of H_∞ filter design for two-dimensional (2D) stochastic systems. The stochastic perturbation is first introduced into the well-known Fornasini-Marchesini local state-space model. Our attention is focused on the design of full-order and reduced-order filters, which guarantee the filtering error system to be mean-square asymptotically stable and to have a prescribed H_∞ disturbance attenuation performance. Sufficient conditions for the existence of such filters are established in terms of linear matrix inequalities, and the corresponding filter design is cast into a convex optimization problem, which can be efficiently handled by using available numerical software. In addition, the obtained results are further extended to more general cases where the system matrices also contain uncertain parameters. The most frequently used ways of dealing with parameter uncertainties, including polytopic and norm-bounded characterizations, are taken into consideration. A numerical example is provided to illustrate the usefulness of the proposed filter design procedures.