Yu Liu

Increased space missions rely on ground-based cyber-physical systems (CPS) to facilitate remote interventions during satellite anomalies. When solar panel deployment failures occur, integrating real-time sensing, computation, and precise robotic control becomes essential for recovery operations. To this end, this paper introduces a cable-driven hyper-redundant flexible manipulator (CHFM) for teleoperated intervention through ground control. To achieve continuous configuration control of the CHFM in constrained environments, we propose a novel planning approach to facilitate end-arm synchronization. Based on this planning strategy, a comprehensive teleoperation CPS architecture is developed, integrating operating, computing and processing, visualization, safety detection, and network modules. Experimental results demonstrate the system's ability to navigate through narrow satellite panel slits, verifying both the system's operational reliability and the effectiveness of the proposed planning method. The results indicate significant improvements in the accuracy of task completion for complex remote manipulation.

This paper proposes an online robust self-learning terminal sliding mode control (RS-TSMC) with stability guarantee for balancing control of reaction wheel bicycle robots (RWBR) under model uncertainties and disturbances, which improves the balancing control performance of RWBR by optimising the constrained output of TSMC. The TSMC is designed for a second-order mathematical model of RWBR. Then robust adaptive dynamic programming based on an actor-critic algorithm is used to optimise the TSMC only by data sampled online. The system closed-loop stability and convergence of the neural network weights are guaranteed based on the Lyapunov analysis. The effectiveness of the proposed algorithm is demonstrated through simulations and experiments.

The two-wheeled legged robot combines the advantages of legged robot and wheeled robot and has high terrain adaptability. Spherical robots are highly resistant to interference during detection. In this paper, a new sphere-wheel-legged robot is designed by combining these three motion modes. This paper begins by introducing the mechanical design, hardware, and software. Then, kinematics and dynamics of wheel-legged motion and spherical motion are analyzed in detail. Subsequently, the controllers for wheel-legged balancing motion, wheel-legged jumping motion, and sphere rolling motion are developed, respectively. Finally, experiments are carried out for different modes. The results demonstrate that the designed robot has excellent locomotor capabilities over different terrains.

In this paper, we develop a method to compute ellipsoidal maximal tolerable fault ( $e$ -MTF) sets of robust model predictive control (MPC) for passive fault tolerance. The maximal tolerable fault (MTF) set serves as an index to evaluate the passive fault-tolerance capabilities of robust MPC frameworks. In particular, a regulation problem for a discrete-time linear time-invariant system with an additive fault model is considered. We construct convex optimization problems to compute the $e$ -MTF sets for the general robust MPC scheme under the predefined feasible region and ultimate bound requirements, respectively. Then, we analyze the set-based fault-tolerance limits for multiplicative faults and sensor faults via the computed $e$ -MTF set. At the end of this paper, a numerical example illustrates the effectiveness of the proposed method.

Current constrained reinforcement learning (RL) methods guarantee constraint satisfaction only in expectation, which is inadequate for safety-critical decision problems. Since a constraint satisfied in expectation remains a high probability of exceeding the cost threshold, solving constrained RL problems with high probabilities of satisfaction is critical for RL safety. In this work, we consider the safety criterion as a constraint on the conditional value-at-risk (CVaR) of cumulative costs, and propose the CVaR-constrained policy optimization algorithm (CVaR-CPO) to maximize the expected return while ensuring agents pay attention to the upper tail of constraint costs. According to the bound on the CVaR-related performance between two policies, we first reformulate the CVaR-constrained problem in augmented state space using the state extension procedure and the trust-region method. CVaR-CPO then derives the optimal update policy by applying the Lagrangian method to the constrained optimization problem. In addition, CVaR-CPO utilizes the distribution of constraint costs to provide an efficient quantile-based estimation of the CVaR-related value function. We conduct experiments on constrained control tasks to show that the proposed method can produce behaviors that satisfy safety constraints, and achieve comparable performance to most safe RL (SRL) methods.