Author: IEEE Transactions on Control Systems Technology - new TOC

Executing safe brachiation maneuvers with a cable-suspended underactuated robot is a challenging problem due to the complications induced by the cable dynamics. We present and experimentally implement an online adaptive controller for a wire-borne brachiating robot swinging on a vibrating cable. An adaptive function approximation approach is proposed to estimate the unknown dynamics of the flexible cable as an external force applied to the robot. Robust control Lyapunov and barrier functions are designed and incorporated into quadratic programs to synthesize a unified adaptive control framework, which formally guarantees the stability and safety of the brachiating robot in the presence of dynamic uncertainties, actuator constraints, and obstacles in the environment. The stability analysis and derivation of the adaptation law are carried out through a Lyapunov analysis. We demonstrate and validate the proposed control framework using extensive hardware experiments with an underactuated brachiating robot operating on a flexible cable. Simulation results, hardware experiments, and comparisons with a baseline controller show that the proposed quadratic programming-based controller achieves reliable tracking performance and disturbance estimation in the presence of model uncertainties, actuator limits, and safety constraints.

In this article, a real-time interactive time-optimal trajectory planning (TOTP) strategy is proposed for aircraft slung-load systems, in which the practical performance is guaranteed by taking advantage of convex optimization and reinforcement learning (RL) techniques. To the best of our knowledge, it is the first TOTP solution for an aircraft suspension system subject to path, velocity, acceleration, and cable tension constraints, where a practice-oriented RL policy is designed to interact with the system online. Specifically, by exploring the differential flatness property of the system, the states are projected into path coordinate space, where all the physical constraints are formulated in the TOTP with convex forms. Then, the TOTP is transformed into a large sparse optimization problem after discretization, which can be efficiently solved using convex solvers. Subsequently, a deep RL network is designed to optimize the TOTP results, where efficient learning is achieved in the solution space, thereby guaranteeing practical reliability and strong robustness via online interaction in applications. In addition, by incorporating convex optimization and learning techniques, the gap between simulation and experiment is successfully bridged, where the physical feasibility and efficient learning are ensured in the framework. Finally, comparative simulations and experimental results are included to show the effectiveness of the proposed strategy.

In practice, the operation of high-speed trains is often affected by adverse weather conditions or equipment failures, which result in delays and even cancellations of train services. In this article, a novel two-layer hierarchical model predictive control (MPC) model is proposed for on-line high-speed railway delay management and train control for minimizing train delays and cancellations. The upper layer manages the global objectives of the train operation, that is, minimizing the total train delays and providing guidance for the speed control in the lower layer. The objectives of the lower layer are to satisfy the running time requirements given by the upper layer and to save energy at the same time. The optimization problems in both levels of the hierarchical MPC framework are formulated as small-scale mixed integer linear programming problems, which can be solved efficiently by existing solvers. Particularly, the train control problem is solved in a distributed way for each train. Simulation analysis based on the real-life data of the Beijing–Shanghai high-speed railway shows that the proposed hierarchical MPC framework can meet the real-time requirements and reduce train delays effectively when compared with widely accepted strategies, for example, first-scheduled-first-serve and first-come-first-serve. Moreover, the proposed hierarchical MPC framework also provides good robustness performance for different disturbance scenarios.

The design process and complexity of existing safety controls are heavily determined by the geometrical properties of the environment, which affects the proof of convergence, design scalability, performance robustness, and numerical efficiency of the control. Hence, this brief proposes a variable structure control to isolate the environment’s geometrical complexity from the control structure. A super-twisting algorithm (STA) is used to achieve accurate trajectory tracking and robust safety control. Safety control is designed solely based on distance measurement. First, a nominal safety model for obstacle avoidance is derived, where realistic system constraints are considered. The nominal model is well-suited for safety control design for obstacle avoidance and border patrol with analytically proven stability results. The safety control uses distance measurement to maintain a safe distance by compensating the robot’s angular velocity. Also, a supervisory logic is constructed to unify the stability and safety features, and operational safety and precision tracking are proven under parametric uncertainty. The proposed design is modular with minimal tuning parameters, which reduces the computational burden of the algorithm and improves control scalability. The effectiveness of the proposed method is verified against various case studies.

Presents the 2022 author/subject index for this issue of the publication.

Cooperatively optimizing a vast number of agents over a large-scale network faces unprecedented scalability challenges. The scalability of existing optimization algorithms is limited by either the agent population size or the network dimension. As a radical improvement, this article, for the first time, constructs a two-facet scalable distributed optimization framework. This novel framework distributes the computing load among agents (scalability w.r.t. population size) and enables each agent to only consider partial network constraints in its primal variable updates (scalability w.r.t. network dimension). We first develop a systemic network dimension reduction technique to virtually cluster the agents and lower the dimension of network-induced constraints and then constitute a novel shrunken primal-multi-dual subgradient (SPMDS) algorithm based on the reduced-dimension network for strongly coupled convex optimization problems. Optimality and convergence of the proposed distributed optimization framework are rigorously proved. The SPMDS-based optimization framework is free of agent-to-agent or cluster-to-cluster communication. Besides, the proposed method can achieve significant floating-point operations (FLOPs) reduction compared with full-dimension cases. The efficiency and efficacy of the proposed approaches are demonstrated, in comparison with benchmark methods, through simulations of electric vehicle charging control problems and traffic congestion control problems.

Dynamic statistical process monitoring methods have been widely studied and applied in modern industrial processes. These methods aim to extract the most predictable temporal information and develop the corresponding dynamic monitoring schemes. However, measurement noise is widespread in real-world industrial processes, and ignoring its effect will lead to suboptimal modeling and monitoring performance. In this article, a probabilistic predictable feature analysis (PPFA) is proposed for multivariate time series modeling, and a multistep dynamic predictive monitoring scheme is developed. The model parameters are estimated with an efficient expectation–maximization algorithm, where the genetic algorithm and the Kalman filter are designed and incorporated. Furthermore, a novel dynamic statistical monitoring index, the dynamic index, is proposed as an important supplement of T2 and SPE to detect dynamic anomalies. The effectiveness of the proposed algorithm is demonstrated via its application on the three-phase flow facility and a medium-speed coal mill.

This article presents a method for redundant manipulators working in the small opening workspace without collision. To achieve this aim, we began with an improved incremental radial basis function (RBF) neural network (RBFNN) method to estimate manipulator dynamic parameters, and then with the help of Lyapunov function, the control strategy could converge within a fixed time. To avoid the collision of workspace and constrain the posture of end-effector, we proposed a safety region convolutional neural network (CNN) method adapted with the Remote Center of Motion method inspired by the minimally invasive surgical manipulator. Torque observer is also implied to estimate the external force to resist external interference. Experiments on Baxter, a seven-degrees of freedom (DoF) redundant manipulator, demonstrate the feasibility of the proposed control strategy.

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This article investigates a deterministic sea wave prediction-based noncausal control scheme for the launch and recovery (L&R) from a mother ship of small rigid-hulled inflatable boats (RHIBs) for maritime rescue missions. The proposed control scheme achieves an automatic hoisting process ensuring that no collisions occur between the RHIB and mothership hull by using the cable tension force as the manipulated control input. A state-space model of the L&R system is established for the first time where the wave forces and external disturbances such as wind acting on both the mothership and the small boat are fully considered. A fast and safe recovery is ensured by a fixed-time convergent sliding-mode controller, which shortens the cable length to a target value with zero terminal velocity at a predefined time instant subject to unknown disturbances and model mismatches. Since the overall dynamics of the swing angle is underactuated, a feasibility check is proposed to avoid collisions between two vessels and overlarge angular velocities by determining a proper time instant to initiate the hoisting process. To cope with the model mismatch and the external disturbance, the constraints on the swing angle and angular velocity are tightened to ensure safety. The stability of the proposed controller is proven and details of the feasibility check are given. The fidelity of the model and the effectiveness of the proposed scheme are demonstrated in simulation where a realistic sea wave is applied.