In this paper, an improved adaptive motion-force control approach is introduced to control the cooperative manipulators transporting a shared object under limited communication. The adaptive controller is designed based on the backstepping approach to control the motion of the handled object in the presence of uncertainties and external disturbances. Moreover, the force controller is established to maintain constant internal forces. An event-triggered (ET) mechanism is derived based on the Lyapunov analysis to deal with the bandwidth restrictions and maintain the system stability during the cooperative manipulation. The effectiveness of the proposed control scheme is investigated by comparing it with the existing variations of adaptive backstepping control (i.e., traditional and state augmented schemes). Moreover, the designed triggering mechanism is compared with different triggering conditions presented in the literature. The proposed control approach is further validated in a more realistic virtual robot experimentation platform (i.e., V-REP) using two SCORBOT-ER VPlus manipulators. From the TrueTime-based simulation runs, the proposed control scheme exhibits superior performance in tandem with efficient utilization of the network resources during the transportation task.

During the last two decades, parallel robots have become more ubiquitous, employed in a great variety of sectors, from food to aerospace industries. In fact, they are much more efficient than their serial counterparts in terms of performing fast motions and consuming less energy. However, due to their mechanical complexity, they present a highly complex non-linear dynamics, which makes the modelling and control tasks difficult. Aiming to improve the performance and robustness of the control laws already used to control this type of mechanisms, this paper proposes two hybrid control techniques. The first hybrid control is derived from the combination of a pure PD control with a modified Sliding Mode control. The second hybrid control, in its turn, combines a pure Computed Torque with the altered Sliding Mode control. The proposed modifications in the Sliding Mode control aim to achieve a considerable reduction of the tracking errors and chattering. A stability analysis of the proposed control techniques and an experimental validation are carried out, comparing the performance of the pure and hybrid control laws in a 5R parallel mechanism. Moreover, simulations are also conducted to evaluate the behaviour of a 3-dof spatial parallel robot, when performing a 3D-path. Analysing the simulations and the experimental results, it is possible to observe a significant reduction of the path tracking and steady-state errors in both hybrid control strategies.

When the end-effector of a robotic arm grasps different payload masses, the output of joint motion will vary. By using a model reference adaptive control approach, the payload variation effect can be solved. This paper describes the design for a hybrid controller for serial robotic manipulators by combining a PID controller and a model reference adaptive controller (MRAC) in order to further improve the accuracy and joint convergence speed performance. The convergence performance of the PID controller, the MRAC and the PID+MRAC hybrid controller for 1-DOF, 2-DOF and subsequently 3-DOF manipulators is compared. The comparison results show that the convergence speed and its performance for the MRAC and the PID+ MRAC controllers is better than that of the PID controller, and the convergence performance for the hybrid control is better than that of the MRAC control.

In this work, a novel hybrid control strategy is proposed for robust trajectory tracking control of robotic systems. The main interest of using hybrid design is to reduce the controller gains so as to reduce control efforts from the single model certainty equivalence principle- based adaptive controllers. For this purpose, we allow the parameter estimate of conventional adaptive control design to be switched into a model that best approximates the plant among a finite set of models. First, we uniformly divide the compact set of unknown parameters into a finite number of smaller compact subsets. Then we construct a finite set of candidate controller for each of these smaller compact subsets. The derivative of the Lyapunov function candidate is employed to identify a controller that closely approximates the plant at each instant of time. The idea of introducing hybrid approach in adaptive control framework is to achieve good transient tracking performance with smaller values of controller gains in the presence of large-scale parametric uncertainties. The proposed method is implemented and evaluated on two 3 degree-of-freedom Phantom Premimum™ 1.5 telerobotic systems to demonstrate the effectiveness of the theoretical development.

The fundamentals of an approach to solving the control task of robots interaction with a dynamic environment based on the stability of a closed-loop control system are given in this paper. The task is set and solved in its general form. The traditional control concept of compliant robot motion—the hybrid position/force control is discussed. In the paper the proposed control laws ensure simultaneous stabilization of both the desired robot motion and the desired interaction force, as well as their required quality of transient responses. In order to emphasize the fundamental point of this approach in controlling the contact tasks in robotics, the authors have assumed ideal parameters of interacting dynamic systems. The proposed control procedure is demonstrated by one simple example.

In this paper, new hybrid control laws for the position control of robotic manipulators are proposed. The proposed control laws are composed of discrete feedforward component and continuous feedback component. The open loop nominal torque about the desired trajectories is taken as the feedforward component, while a modified version of the sliding mode control is taken as the feedback component. For the three proposed control laws, we give sufficient conditions which guarantee the bounded tracking errors in spite of the modeling errors. The existence of the control gains which satisfy these conditions is shown by numerical examples. The computational burden of feedback control is analyzed, which shows that the feedback control can be used in real time digital control. The robustness and the good tracking performance of proposed algorithms are demonstrated by the numerical simulation of a manipulator position control under payloads and parameter uncertainties.

We study a hybrid control system in which both discrete and continuous controls are involved. The discrete controls act on the system at a given set interface. The state of the system is changed discontinuously when the trajectory hits predefined sets, namely, an autonomous jumpset A or a controlled jump set C where controller can choose to jump or not. At each jump, trajectory can move to a different Euclidean space. We allow the cost functionals to be unbounded with certain growth and hence the corresponding value function can be unbounded. We characterize the value function as the unique viscosity solution of the associated quasivariational inequality in a suitable function class. We also consider the evolutionary, finite horizon hybrid control problem with similar model and prove that the value function is the unique viscosity solution in the continuous function class while allowing cost functionals as well as the dynamics to be unbounded.

This work proposes control structures that efficiently combine force control with vision servo control of robot manipulators. Impedance controllers are considered which are based both on visual servoing and on physical or fictitious force feedback, the force and visual information being combined in the image space. Force and visual servo controllers included in extended hybrid control structures are also considered. The combination of both force and vision based control allows the tasks range of the robot to be extended to partially structured environments. The proposed controllers, implemented on an industrial SCARA-type robot, are tested in tasks involving physical and virtual contact with the environment.

Force and position sensors have been widely used in robots to realize compliance and precise control. Traditional force/position control methods were studied and developed by the inverse dynamics for decades. Generally speaking, the controller contains two parts: One is the error-driven part that guarantees system stability; another is the identification model of inverse dynamics that can compensate for system influence. In practical control engineering, a system inverse dynamics or its identification model is not easy to obtain, even when using nonlinear estimation methods. Moreover, the complicated control algorithm cannot be implemented in on-board microprocessors because of the limited speed and memory. Thus, a simplified control method using a forward system model is introduced in this paper. Since the direct dynamics of the system can be more easily obtained than the inverse dynamics, this, in turn, simplifies the control structure and increases control speed. Therefore, the proposed control policy has a wider practical application.