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In this article, a novel reinforcement learning (RL)-based robust control approach is proposed for quadrotors, which guarantees efficient learning and satisfactory tracking performance by simultaneously evaluating the RL and the baseline method in training. Different from existing works, the key novelty is to design a practice-reliable RL control framework for quadrotors in a two-part cooperative manner. In the first part, based on the hierarchical property, a new robust integral of the signum of the error (RISE) design is proposed to ensure asymptotic convergence, which includes the nonlinear and the disturbance rejection terms. In the second part, a one-actor-dual-critic (OADC) learning framework is proposed, where the designed switching logic in the first part works as a benchmark to guide the learning. Specifically, the two critics independently evaluate the RL policy and the switching logic simultaneously, which are utilized for policy update, only when both are positive, corresponding to the remarkable actor-better exploration actions. The asymptotic RISE controller, together with the two critics in OADC learning framework, guarantees accurate judgment on every exploration. On this basis, the satisfactory performance of the RL policy is guaranteed by the actor-better exploration based learning while the chattering problem arisen from the switching logic is addressed completely. Plenty of comparative experimental tests are presented to illustrate the superior performance of the proposed RL controller in terms of tracking accuracy and robustness. [ABSTRACT FROM AUTHOR]

Aerial delivery is becoming a reality due to the development of microelectronics and communication technology. Most existing methods for cable-suspended transportation systems utilize fixed-length cable to connect the unmanned aerial vehicle quadrotor and the payload. Such aerial transportation systems present underactuated property, which is caused by the indirectly controllable payload motion and the underactuation of the quadrotor itself. In practical applications, payload hoisting and lowering motion independent of the quadrotor altitude will further expand the application scope in such areas as limited space crossing and offshore sample collection. To realize the aforementioned objectives, a flexible connection between the quadrotor and the payload is realized by mounting an actuator beneath the fuselage. Suffering from strong nonlinearity and complex dynamic coupling, the control problem becomes extremely challenging and more cumbersome, as the system’s degree of freedom (DOF) increases. To deal with these problems, in this article, a nonlinear control approach is presented by energy-based analysis, which achieves simultaneous quadrotor positioning, payload swing elimination and hoisting/lowering. Lyapunov techniques and LaSalle’s Invariance theorem are utilized to prove the asymptotic convergence of the equilibrium point. Finally, a series of hardware experiments are conducted on a self-built aerial transportation platform. As far as we know, this article provides the first mechanism and control solution for payload hoisting/lowering independent of the quadrotor altitude. [ABSTRACT FROM AUTHOR]

Tendon–sheath artificial muscle (TSAM) is a type of artificial muscle, which is widely used in robotic flexible endoscopies. The characteristics of the tendon force/position transmission were studied recently, but the control issue of the TSAM twist is still barely concerned. In this article, an effective robust control scheme is proposed for the TSAM twist, which achieves satisfactory tracking performance. First, a rigorous model was developed for the TSAM twist with consideration of parameters uncertainty and actuator input constraints, moreover, the disturbance of the discontinuous friction (Coulomb friction) was analyzed. Next, by applying some signal operation methods, a nonlinear robust controller was designed. In addition, the stability of the control strategy was proven theoretically by utilizing Lyapunov-based theory. Finally, a series of hardware experiments were conducted on a robotic flexible ureteroscope test-bed. The feasibility of the proposed model, the effectiveness and robustness of the designed control method were extensively validated. [ABSTRACT FROM AUTHOR]

Underactuated systems are widely applied in industry, construction, manufacturing, etc., and the complex working environment puts forward higher demands for safety and transient performance. Hence, it is necessary to consider how to simultaneously ensure actuated and unactuated motion constraints by fewer control inputs, especially when systems suffer from model uncertainties, unavailable velocities, etc. Unfortunately, it is still a significant challenge to overcome in real applications and theoretical analysis. Additionally, most existing studies merely consider the specific control objects and few general methods are applicable to a class of underactuated systems. To this end, we design a new adaptive output-feedback controller for a class of uncertain underactuated systems. Compared with existing methods only handling actuated constraints, an important merit of this article is that by introducing the elaborately designed coupling term composed of actuated and unactuated constraints together, all state variables are kept within the preset time-variant ranges and converge to their desired values. Furthermore, a new Lyapunov function candidate is utilized to provide a theoretical guarantee. As far as we know, without the need of exact model knowledge and velocity feedback, this article provides the first solution to achieve accurate motion control and state constraints for both actuated and unactuated variables, which is meaningful both theoretically and practically. Meanwhile, the asymptotic stability of the equilibrium point for the closed-loop system is proven by utilizing Lyapunov techniques and Barbalat’s lemma. For verification, the presented controller is applied to underactuated overhead and rotary cranes, respectively, together with detailed theoretical analysis and experimental validations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Due to high biological adaptability and flexibility, pneumatic artificial muscle (PAM) systems are widely employed in exoskeleton robots to accomplish rehabilitation training with repetitive motions. However, some intrinsic characteristics of PAMs and inevitable practical factors, e.g., high nonlinearity, hysteresis, uncertain dynamics, and limited working space, may badly degrade tracking performance and safety. Hence, this paper designs a new learning-based motion controller for PAMs, to simultaneously compensate for model uncertainties, eliminate tracking errors, and satisfy preset motion constraints. Particularly, when PAMs suffer from periodically non-parametric uncertainties, the elaborately designed continuous update algorithm can repetitively learn them online to enhance tracking accuracy, without employing upper/lower bounds of unknown parts for controller design and gain selections. Meanwhile, some non-periodic uncertainties are handled by a robust term, whose value is only related to the initial states of PAMs, instead of exact upper bounds of unknown dynamics. From safety concerns, we introduce error-related saturation terms to limit initial amplitudes of control inputs within saturation constraints and avoid overlarge errors inducing overlarge acceleration. Meanwhile, the constraint-related auxiliary term is utilized to keep tracking errors within allowable ranges. To the best of our knowledge, this paper presents the first learning-based error-constrained controller for uncertain PAM-actuated exoskeleton robots, to realize high-precision tracking control and improve safety without additional gain conditions. Moreover, the asymptotic convergence of tracking errors is strictly proven by Lyapunov-based stability analysis. Finally, based on a self-built exoskeleton robot, the effectiveness of the proposed controller is verified by hardware experiments. Note to Practitioners—This work is motivated by the practical requirements of exoskeleton robots in rehabilitation training and exploration fields. Currently, PAM systems, as a kind of new flexible actuator equipment, are playing increasingly important roles in the development of exoskeleton robot control. However, uncertain (or time-varying) parameters/structures and highly nonlinear dynamics, such as creep and hysteresis, may badly increase the control difficulty of PAMs. Moreover, higher and higher tracking accuracy and safety requirements also induce urgently solved problems to practical PAM-actuated exoskeleton robots, e.g., smooth start, motion constraints, and rapid error elimination. To this end, this paper proposes a new learning-based adaptive controller, which realizes accurate tracking control for PAM-actuated exoskeleton robots by utilizing an elaborately designed repetitive learning algorithm and a robust term to handle periodic and non-periodic uncertainties, respectively. More importantly, the proposed controller simultaneously enhances transient performance of PAMs, including gradually improved tracking accuracy, effective constraints for startup acceleration and tracking errors. Additionally, it is not required to consider the upper bounds of unknown dynamics and additional gain selection conditions, which is theoretically and practically important for PAM systems. Some hardware experiments further verify the effectiveness and robustness of the suggested controller. In our future work, we intend to design more effective methods for PAMs with unmeasurable states and time-delay. [ABSTRACT FROM AUTHOR]

To solve the autonomous navigation problem in complex environments, an efficient motion planning approach is newly presented in this paper. Considering the challenges from large-scale, partially unknown complex environments, a three-layer motion planning framework is elaborately designed, including global path planning, local path optimization, and time-optimal velocity planning. Compared with existing approaches, the novelty of this work is twofold: 1) a novel heuristic-guided pruning strategy of motion primitives is proposed and fully integrated into the state lattice-based global path planner to further improve the computational efficiency of graph search, and 2) a new soft-constrained local path optimization approach is proposed, wherein the sparse-banded system structure of the underlying optimization problem is fully exploited to efficiently solve the problem. We validate the safety, smoothness, flexibility, and efficiency of our approach in various complex simulation scenarios and challenging real-world tasks. It is shown that the computational efficiency is improved by 66.21% in the global planning stage and the motion efficiency of the robot is improved by 22.87% compared with the recent quintic Bézier curve-based state space sampling approach. We name the proposed motion planning framework E $\mathbf {^{3}} $ MoP, where the number 3 not only means our approach is a three-layer framework but also means the proposed approach is efficient in three stages. Note to Practitioners—This paper is motivated by the challenges of motion planning problems of mobile robots. A three-layer motion planning framework is proposed by combining global path planning, local path optimization, and time-optimal velocity planning. For mobile robot navigation applications in semi-structured environments, optimization-based local planners are recommended. Extensive simulation and experimental results show the effectiveness of the proposed motion planning framework. However, due to the non-convexity of the path optimization formulation, the proposed local planner may get stuck in local optima. In future research, we will concentrate on extending the proposed local path optimization approach with the theory of homology classes to maintain several homotopically distinct local paths and seek global optima. [ABSTRACT FROM AUTHOR]

With large load capacity and flexible payload attitude adjustment capability, dual rotary cranes (DRCs) play crucial roles in infrastructure construction with heavy hoisting demands. However, as a kind of collaborative control systems, DRCs have complex collaborative constraints, and large-scale payloads cannot be simply regarded as mass points; moreover, due to the lack of control inputs, some state variables can only be indirectly controlled through complicated nonlinear coupling relationships, which make the controller design and corresponding analysis particularly difficult. Additionally, in practical applications of DRCs, many factors (such as boom motion overshoots, inaccurate gravity (torque) compensation, etc.) are prone to result in unexpected steady errors, large payload swing, and boom collisions, which may result in inaccurate assembly, and even lead to safety accidents. To this end, this article proposes an adaptive nonlinear proportional-integral-derivative-like collaborative control method for DRCs, which can realize accurate and efficient antiswing hoisting. To our knowledge, this is the first controller embedded with integral terms without any linearization during the controller design or stability analysis, which can effectively reduce steady errors, and keep boom motions within safety ranges by adaptive gravity (torque) compensation and elaborately designed constraint terms. Theoretically, the closed-loop stability and convergence are proven through strict mathematical analysis by using Lyapunov techniques and LaSalle’s invariance theorem. Finally, several groups of hardware experimental results are presented for effectiveness and robustness verification. [ABSTRACT FROM AUTHOR]

Output feedback control without using velocities is an effective way of dealing with problems caused by unmeasurable velocities and noise magnification by numerical difference, which are practical issues associated with crane control. This article proposes a new adaptive dynamic output feedback control approach for double-pendulum ship-mounted cranes in the presence of unknown parameters (e.g., cargo masses). The problems caused by the unknown masses are addressed through a new adaptive gravitational compensation law. Also, the input constraints are tackled by introducing bounded functions. Specifically, to handle the complicated disturbances caused by ship motions, new state variables are constructed to transform the dynamic model. Then, Lyapunov-based stability analysis is proceeded to design the controller and the adaptive law. To the best of authors’ knowledge, this is the first control method proposed for double-pendulum ship-mounted crane systems without using velocity signals, which can simultaneously eliminate steady errors, suppress unexpected cargo swing, achieve accurate gravitational compensation, and satisfy input constraints. The closed-loop system’s state variables asymptotically converge to the equilibrium point, which is rigorously proven. A series of experiments are implemented on a hardware testbed, which further illustrate the satisfactory performance of the proposed method. [ABSTRACT FROM AUTHOR]

In the field of modern industrial engineering, many mechanical systems are underactuated, exhibiting strong nonlinear characteristics and high flexibility. However, the lack of control inputs brings about many difficulties for controller design and stability/convergence analysis., some unavoidable practical issues, e.g., plant uncertainties and actuator deadzones, make the control of underactuated systems even more challenging. Hence, with the aid of elaborately constructed finite-time convergent surfaces, this article provides the first solution to address the control problem for a class of multi-input-multi-output (MIMO) underactuated systems subject to plant uncertainties and actuator deadzones. Specifically, this article overcomes the main obstacle in sliding-mode surface analysis for MIMO underactuated systems, that is, by the presented analysis method, the asymptotic stability of the system equilibrium point is strictly proven based on the composite surfaces. In addition, the unknown parts of the actuated/unactuated dynamic equations and actuator deadzones can be simultaneously handled, which is important for real applications. Furthermore, we apply the proposed method to two kinds of typical underactuated systems, that is: 1) tower cranes and 2) double-pendulum cranes, and implement a series of hardware experiments to verify its effectiveness and robustness. [ABSTRACT FROM AUTHOR]

Underactuated dual boom crane (DBC) systems exhibit complicated nonlinearity and strong coupling due to the lack of independent actuators. Moreover, plant parameters may change in different transportation tasks and are difficult to be measured accurately, which leads to inaccurate gravity (torque) compensation and further brings positioning errors. Hence, most existing controllers based on exact model knowledge cannot ensure satisfactory control performance any longer. In order to handle the above issues, this article designs an adaptive sliding mode tracking controller for DBC systems based on the original complicated nonlinear dynamics without any linearization/simplification operations, which is the first one to effectively achieve both anti-swing and trajectory tracking control in the presence of parametric uncertainties. Not only can the state variables converge to the proposed sliding surface within finite time but also payload swing angles can be completely eliminated; consequently, the working efficiency and operation safety are further guaranteed. The corresponding stability and convergence for the equilibrium point of the closed-loop system are proven by rigorous mathematical analysis based on the Lyapunov techniques and Barbalat’s lemma. Hardware experimental results demonstrate the effectiveness and robustness of the presented controller. [ABSTRACT FROM AUTHOR]

The pneumatic artificial muscle (PAM) is a kind of flexible actuators used to simulate the characteristics of human muscles. Robots actuated by PAMs possess compliance and safety, which can achieve satisfactory man–machine interaction control. Nevertheless, such robots actuated by PAMs tend to have lots of control problems due to the inherent characteristics, such as hysteresis, creep, high nonlinearities, and so on, which is not conducive to accurate modeling and motion control. Moreover, most existing control methods do not consider constraining overshoots, etc.; however, based on safety requirements and actual physical constraints, systems with unconstrained overshoots may have potential risks. In this article, a new energy-based nonlinear control method is proposed for 2-link PAM-actuated robots to realize accurate positioning control. For this purpose, first, the dynamic model of 2-link PAM-actuated robots is presented. Further, a new energy storage function is constructed. Additionally, the overshoots and the terms coupled with control inputs are also constrained reasonably, which can reduce the unnecessary energy loss while improving the system safety. To the best of our knowledge, the proposed method is the first nonlinear control approach for 2-link PAM-actuated robots which is designed and analyzed based upon the original nonlinear dynamics without any linearization to provide high-performance positioning control with constrained overshoots and eliminated residual oscillations simultaneously. By rigorous analysis, the asymptotic stability of the system is proven. To validate the effectiveness of the proposed method, a series of hardware experimental results are presented based on a self-built 2-link PAM-actuated robot. [ABSTRACT FROM AUTHOR]

For pose stabilization task of nonholonomic mobile robots, this article proposes a novel integrated interactive framework, bridging the gap between visual servoing and simultaneous localization and mapping (SLAM). The framework consists of two cooperative components, control module for servoing task and SLAM module for feedback signals estimation. In most visual servoing methods, feedback signals for the servoing controller are estimated by means of multiple-view geometry assuming the target scene being always within the camera field of view (FOV). To handle the challenge that the target scene gets out of view during servoing process, the desired image is associated with the initial map by a two-step strategy, and an incremental map is constructed to guarantee available feedback signals estimation. In addition, on the basis of the kinematic model of the mobile robot and velocities designed by the servo controller, the predicted pose is exploited to discard moving objects in the camera FOV, thus making the proposed framework effective in dynamic scenes. Experimental results operated in different scenes without prior information demonstrate the effectiveness of the proposed approach to handle the FOV problem and dynamic scenes. Note to Practitioners—Traditional visual servoing stabilization approaches usually require that the feature points in the target scene remain within the FOV of the camera for feedback signals calculation, which is often neglected. Motivated by the requirement of continuous feedback signals to the servo controller, the SLAM technique is introduced to relax the FOV constraint during the servoing process. A novel integrated interactive framework is proposed in this article to further increase the applicability of the servoing system in practice, in which the SLAM module is also redesigned for the flexibility in dynamic scenes. The SLAM module provides feedback signals for the servo controller; meanwhile, velocities designed by the servo controller are utilized for the prediction mechanism in the SLAM module to discard features on moving objects. Experiments validate the applicability of the proposed framework in different scenarios. [ABSTRACT FROM AUTHOR]

As a typical underactuated mechanical system, offshore ship-mounted cranes are widely used to carry out the tasks of transferring cargos from one ship to another in the marine environment. Different from land-fixed cranes as well as traditional harbor cranes, offshore ship-to-ship crane systems work in two noninertial (ship) frames, while the target locations of the cargos are also inevitably influenced by the movements of the target ship. Besides, various external disturbances, which are caused by persistent sea waves, sea winds, or currents, etc., bring much more challenges to the control task of offshore ship-to-ship crane systems. To properly address these practical problems, in this article, we propose an increased adaptive neural network (NN)-based anti-swing tracking control strategy for such coordinated offshore crane systems, with a ship-motion prediction algorithm suggested to generate the target trajectories for cargos, and an adaptive NN proposed to deal with complicated unknown wave-induced disturbances. Bounded tracking performance is also guaranteed through a complete Lyapunov-based stability analysis. To the best of our knowledge, without any simplification of the original nonlinear dynamics, this article provides a high-performance adaptive control approach to deal with the anti-interference tracking control problem for offshore ship-to-ship crane systems, which are subjected to trajectories uncertainties as well as unknown wave motions disturbances. Furthermore, comparative hardware experimental results are presented to demonstrate the efficiency of the proposed control method. [ABSTRACT FROM AUTHOR]

Rubber tire container gantry cranes (RTG) are the most commonly used equipments to stack containers in ports. With four sets of ropes hoisting the cargo, the system dynamics become much more complicated and unpredictable when compared with traditional gantry cranes, which imposes higher requirements to the skills of operators. During the delivering process, large sway motion of the container is often excited by inappropriate manual operations, which badly impairs efficiency and safety. This issue is made even worse when considering the elasticity of rubber tire, wind disturbances, and many other practical disturbances. To improve the working efficiency of ports, an automatic antiswing trajectory planning method is proposed for RTG in this article. Specifically, reference trajectories are first generated to ensure that the trolley/rope accurately reach the desired position. After that, appropriate feedback terms are incorporated to online optimize the trajectories, so that the container swing motion is sufficiently suppressed even under unfavorable conditions. Lyapunov-based analysis is presented to guarantee the correctness of our conclusion. Furthermore, extensive hardware experiments on a practical RTG are performed to demonstrate the efficiency of the proposed method. To our best knowledge, with concise structure and superior antiswing ability, the proposed method is one of the very few methods which can be successfully applied to the practical RTG systems. [ABSTRACT FROM AUTHOR]

To shorten the scanning time by focusing on the local scanning for such specimens as cells, this article proposes an advanced scanning strategy based on autonomous exploration, so as to detect the specimen in real time and achieve fast imaging for an atomic force microscopy. More specifically, fast raster scanning is first performed to locate the initial boundary point of the specimen. On this basis, a boundary tracking algorithm is proposed to construct the internal boundary of the specimen online through the autonomous exploration of the probe. Afterward, the boundary is expanded according to the internal boundary tracking direction, based on which a convex hull of the specimen is further constructed for the local scanning. Furthermore, the local slow scanning is performed for the specimen according to the generated scanning trajectory. Subsequently, unscanned areas in the sample can also be scanned by this autonomous method. Experimental results verify that the proposed method can achieve fast scanning while ensuring high-quality imaging. [ABSTRACT FROM AUTHOR]