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The problem of robotic co-manipulation is often addressed using impedance control based methods where the authors seek to establish a mathematical relation between the velocity of the human-robot interaction point and the force applied by the human operator (HO) at this point. This paper aims to address the problem of co-manipulation for handling tasks seen as a constrained optimal control problem.

The proposed point of view relies on the implementation of a specific online trajectory generator (OTG) associated with a kinematic feedback loop. This OTG is designed so as to translate the HO intentions to ideal trajectories that the robot must follow. It works as an automaton with two states of motion whose transitions are controlled by comparing the magnitude of the force to an adjustable threshold, in order to enable the operator to keep authority over the robot's states of motion.

To ensure the smoothness of the interaction, the authors propose to generate a velocity profile collinear to the force applied at the interaction point. The feedback control loop is then used to satisfy the requirements of stability and of trajectory tracking to guarantee assistance and operator security. The overall strategy is applied to the penducobot problem.

The approach stands out for the nature of the problem to be tackled (heavy load handling tasks) and for its vision on the co-manipulation. It is based on the implementation of two main ingredients. The first one lies in the online generation of an appropriate trajectory of the interaction point located at the end-effector and describing the HO intention. The other consists in the design of a control structure allowing a good tracking of the generated trajectory.

The purpose of this paper is to provide a hybrid adaptive impedance-leader-follower control algorithm for multi-arm coordination manipulators, which is significant for dealing with the problems of kinematics inconsistency and error accumulation of interactive force in multi-arm system.

This paper utilized a motion mapping theory in Cartesian space to establish a centralized dynamic leader-follower control algorithm which helped to reduce the possibility of kinematics inconsistency for multiple manipulators. A virtual linear spring model (VLSM) was presented based on a recognition approach of characteristic marker. This paper accomplished an adaptive impedance control algorithm based on the VLSM, which took into account the non-rigid contact characteristic. Experimentally demonstrated results showed the proposed algorithm guarantees that the motion and interactive forces asymptotically converge to the prescribed values.

The hybrid control method improves the accuracy and reliability of multi-arm coordination system, which presents a new control framework for multiple manipulators.

This algorithm has significant commercial applications, as a means of controlling multi-arm coordination manipulators that could serve to handle large objects and assemble complicated objects in industrial and hazardous environment.

This work presented a new control framework for multiple coordination manipulators, which can ensure consistent kinematics and reduce the influence of error accumulation, and thus can improve the accuracy and reliability of multi-arm coordination system.

This paper aims to develop a novel trajectory optimization algorithm which is capable of producing high accuracy optimal solution with superior computational efficiency for the hypersonic entry problem.

A two-stage trajectory optimization framework is constructed by combining a convex-optimization-based algorithm and the pseudospectral-nonlinear programming (NLP) method. With a warm-start strategy, the initial-guess-sensitive issue of the general NLP method is significantly alleviated, and an accurate optimal solution can be obtained rapidly. Specifically, a successive convexification algorithm is developed, and it serves as an initial trajectory generator in the first stage. This algorithm is initial-guess-insensitive and efficient. However, approximation error would be brought by the convexification procedure as the hypersonic entry problem is highly nonlinear. Then, the classic pseudospectral-NLP solver is adopted in the second stage to obtain an accurate solution. Provided with high-quality initial guesses, the NLP solver would converge efficiently.

Numerical experiments show that the overall computation time of the two-stage algorithm is much less than that of the single pseudospectral-NLP algorithm; meanwhile, the solution accuracy is satisfactory.

Due to its high computational efficiency and solution accuracy, the algorithm developed in this paper provides an option for rapid trajectory designing, and it has the potential to evolve into an online algorithm.

The paper provides a novel strategy for rapid hypersonic entry trajectory optimization applications.

The purpose of this paper is to describe an innovative compliance control architecture for hybrid multi‐legged robots. The approach was verified on the hybrid legged‐wheeled robot ASGUARD, which was inspired by quadruped animals. The adaptive compliance controller allows the system to cope with a variety of stairs, very rough terrain, and is also able to move with high velocity on flat ground without changing the control parameters.

The paper shows how this adaptivity results in a versatile controller for hybrid legged‐wheeled robots. For the locomotion control we use an adaptive model of motion pattern generators. The control approach takes into account the proprioceptive information of the torques, which are applied on the legs. The controller itself is embedded on a FPGA‐based, custom designed motor control board. An additional proprioceptive inclination feedback is used to make the same controller more robust in terms of stair‐climbing capabilities.

The robot is well suited for disaster mitigation as well as for urban search and rescue missions, where it is often necessary to place sensors or cameras into dangerous or inaccessible areas to get a better situation awareness for the rescue personnel, before they enter a possibly dangerous area. A rugged, waterproof and dust‐proof corpus and the ability to swim are additional features of the robot.

Contrary to existing approaches, a pre‐defined walking pattern for stair‐climbing was not used, but an adaptive approach based only on internal sensor information. In contrast to many other walking pattern based robots, the direct proprioceptive feedback was used in order to modify the internal control loop, thus adapting the compliance of each leg on‐line.

The purpose of this paper is to address the problem of control in a typical chaotic power system. Chaotic oscillations cannot only extremely endanger the stabilization of the power system but they can also not be controlled by adding the traditional controllers. So, the sliding mode control based on a fuzzy supervisor can sufficiently ensure perfect tracking and controlling in the presence of uncertainties. Closed-loop stability is proved using the Lyapunov stability theory. The simulation results show the effectiveness of the proposed method in damping chaotic oscillations of the power system, eliminating control signal chattering and also show less control effort in comparison with the methods considered in previous literatures.

The sliding mode control based on a fuzzy supervisor can sufficiently ensure perfect tracking and controlling in the presence of uncertainties. Closed-loop stability is proved using the Lyapunov stability theory.

Closed-loop stability is proved using the Lyapunov stability theory. The simulation results show the effectiveness of the proposed method in damping chaotic oscillations of power system, eliminating control signal chattering and also less control effort in comparison with the methods considered in previous literatures.

Main contributions of the paper are as follows: the chaotic behavior of power systems with two uncertainty parameters and tracking reference signal for the control of generator angle and the controller signal are discussed; designing sliding mode control based on a fuzzy supervisor in order to practically implement for the first time; while the generator speed is constant, the proposed controller will enable the power system to go in any desired trajectory for generator angle at first time; stability of the closed-loop sliding mode control based on the fuzzy supervisor system is proved using the Lyapunov stability theory; simulation of the proposed controller shows that the chattering is low control signal.

The purpose of this paper is to design a novel optimized biped robot gait generator which plays an important role in helping the robot to move forward stably. Based on a mathematical point of view, the gait design problem is investigated as a constrained optimum problem. Then the task to be solved is closely related to the evolutionary calculation technique.

Based on this fact, this paper proposes a new way to optimize the biped gait design for humanoid robots that allows stable stepping with preset foot-lifting magnitude. The newly proposed central force optimization (CFO) algorithm is used to optimize the biped gait parameters to help a nonlinear uncertain humanoid robot walk robustly and steadily. The efficiency of the proposed method is compared with the genetic algorithm, particle swarm optimization and improved differential evolution algorithm (modified differential evolution).

The simulated and experimental results carried out on the small-sized nonlinear uncertain humanoid robot clearly demonstrate that the novel algorithm offers an efficient and stable gait for humanoid robots with respect to accurate preset foot-lifting magnitude.

This paper proposes a new algorithm based on four key gait parameters that enable dynamic equilibrium in stable walking for nonlinear uncertain humanoid robots of which gait parameters are initiatively optimized with CFO algorithm.

Humanoid robots should have the ability of walking in complex environment and overcoming large obstacles in rescue mission. Previous research mainly discusses the problem of humanoid robots stepping over or on/off one obstacle statically or dynamically. As an extreme case, this paper aims to demonstrate how the robots can step over two large obstacles continuously.

The robot model uses linear inverted pendulum (LIP) model. The motion planning procedure includes feasibility analysis with constraints, footprints planning, legs trajectory planning with collision-free constraint, foot trajectory adapter and upper body motion planning.

The motion planning with the motion constraints is a key problem, which can be considered as global optimization issue with collision-free constraint, kinematic limits and balance constraint. With the given obstacles, the robot first needs to determine whether it can achieve stepping over, if feasible, and then the robot gets the motion trajectory for the legs, waist and upper body using consecutive obstacles stepping over planning algorithm which is presented in this paper.

The consecutive stepping over problem is proposed in this paper. First, the paper defines two consecutive stepping over conditions, sparse stepping over (SSO) and tight stepping over (TSO). Then, a novel feasibility analysis method with condition (SSO/TSO) decision criterion is proposed for consecutive obstacles stepping over. The feasibility analysis method’s output is walking parameters with obstacles’ information. Furthermore, a modified legs trajectory planning method with center of mass trajectory compensation using upper body motion is proposed. Finally, simulations and experiments for SSO and TSO are carried out by using the XT-I humanoid robot platform with the aim to verify the validity and feasibility of the novel methods proposed in this paper.

The purpose of this paper is to model a motor region named the mesencephalic locomotors region (MLR) which is located in the end part of the brain and first part of the spinal cord. This model will be used for a Nao soccer player humanoid robot. It consists of three main parts: High Level Decision Unit (HLDU), MLR‐Learner and the CPG layer. The authors focus on a special type of decision making named curvilinear walking.

The authors' model is based on stimulation of some programmable central pattern generators (PCPGs) to generate curvilinear bipedal walking patterns. PCPGs are made from adaptive Hopfs oscillators. High level decision, i.e. curvilinear bipedal walking, will be formulated as a policy gradient learning problem over some free parameters of the robot CPG controller.

The paper provides a basic model for generating different types of motions in humanoid robots using only simple stimulation of a CPG layer. A suitable and fast curvilinear walk has been achieved on a Nao humanoid robot, which is similar to human ordinary walking. This model can be extended and used in other types of humanoid.

The authors' work is limited to a special type of biped locomotion. Different types of other motions are encouraged to be tested and evaluated by this model.

The paper introduces a bio‐inspired model of skill learning for humanoid robots. It is used for curvilinear bipedal walking pattern, which is a beneficial movement in soccer‐playing Nao robots in Robocup competitions.

The paper uses a new biological motor concept in artificial humanoid robots, which is the mesencephalic locomotor region.

Component positioning is an important part of aircraft assembly, aiming at the problem that it is difficult to accurately fall into the corresponding ball socket for the ball head connected with aircraft component. This study aims to propose a ball head adaptive positioning method based on impedance control.

First, a target impedance model for ball head positioning is constructed, and a reference positioning trajectory is generated online based on the contact force between the ball head and the ball socket. Second, the target impedance parameters were optimized based on the artificial fish swarm algorithm. Third, to improve the robustness of the impedance controller in unknown environments, a controller is designed based on model reference adaptive control (MRAC) theory and an adaptive impedance control model is built in the Simulink environment. Finally, a series of ball head positioning experiments are carried out.

During the positioning of the ball head, the contact force between the ball head and the ball socket is maintained at a low level. After the positioning, the horizontal contact force between the ball head and the socket is less than 2 N. When the position of the contact environment has the same change during ball head positioning, the contact force between the ball head and the ball socket under standard impedance control will increase to 44 N, while the contact force of the ball head and the ball socket under adaptive impedance control will only increase to 19 N.

In this paper, impedance control is used to decouple the force-position relationship of the ball head during positioning, which makes the entire process of ball head positioning complete under low stress conditions. At the same time, by constructing an adaptive impedance controller based on MRAC, the robustness of the positioning system under changes in the contact environment position is greatly improved.