Risk-Informed Model-Free Safe Control of Linear Parameter-Varying Systems

This paper presents a risk-informed data-driven safe control design approach for a class of stochastic uncertain nonlinear discrete-time systems.The nonlinear system is modeled using linear parameter-varying(LPV)systems.A model-based probabilistic safe controller is first designed to guarantee probabilisticλ-contractivity(i.e.,stability and invariance)of the LPV system with respect to a given polyhedral safe set.To obviate the requirement of knowing the LPV system model and to bypass identifying its open-loop model,its closed-loop data-based representation is provided in terms of state and scheduling data as well as a decision variable.It is shown that the variance of the closedloop system,as well as the probability of safety satisfaction,depends on the decision variable and the noise covariance.A minimum-variance direct data-driven gain-scheduling safe control design approach is presented next by designing the decision variable such that all possible closed-loop system realizations satisfy safety with the highest confidence level.This minimum-variance approach is a control-oriented learning method since it minimizes the variance of the state of the closed-loop system with respect to the safe set,and thus minimizes the risk of safety violation.Unlike the certainty-equivalent approach that results in a risk-neutral control design,the minimum-variance method leads to a risk-averse control design.It is shown that the presented direct risk-averse learning approach requires weaker data richness conditions than existing indirect learning methods based on system identification and can lead to a lower risk of safety violation.Two simulation examples along with an experimental validation on an autonomous vehicle are provided to show the effectiveness of the presented approach.

Disturbance Observer-Based Safe Tracking Control for Unmanned Helicopters With Partial State Constraints and Disturbances

In this paper, a disturbance observer-based safe tracking control scheme is proposed for a medium-scale unmanned helicopter with rotor flapping dynamics in the presence of partial state constraints and unknown external disturbances. A safety protection algorithm is proposed to keep the constrained states within the given safe-set. A second-order disturbance observer technique is utilized to estimate the external disturbances. It is shown that the desired tracking performance of the controlled unmanned helicopter can be achieved with the application of the backstepping approach, dynamic surface control technique, and Lyapunov method. Finally, the availability of the proposed control scheme has been shown by simulation results.

Comprehensive experiment and evaluation on safety due to fully-mechanized mining under the Xitian River at Linxin Mine

L3414 mechanized mining working face (No.14 coal seam) of Lingxin Mine is under the Xitian River.The mining disturbed zone where rock properties and conditions have been changed due to mining, the safety and environmental protection were con- cemed greatly. Based on engineering geological environment of L3414 working face and mining factors, the color bore-bole TV inspecting, leakage of drilling fluid monitoring, simulation experiment, numerical computing, in-situ ground stress measurement and subsidence measurement, etal, these programs contribute to the formation of a scientific basis for control water safe mining and normal mining or environmental protection in the condition of existing fully-mechanized mining.

Recent developments in Boolean networks control

The aim of this survey paper is to provide the state of the art of the research on control and optimal control of Boolean control networks,under the assumption that all the state variables are accessible and hence available for feedback.Necessary and sufficient conditions for stabilisability to a limit cycle or to an equilibrium point are given.Additionally,it is shown that when such conditions are satisfied,stabilisation can always be achieved by means of state feedback.Analogous results are obtained for the safe control problem that is investigated for the first time in this survey.Finite and infinite horizon optimal control are subsequently considered,and solution algorithms are provided,based on suitable adaptations of theRiccati difference and algebraic equations.Finally,an appropriate definition of the cost function allows to restate and to solve both stabilisation and safe control as infinite horizon optimal control problems.