For robot manipulators, there are two types of disturbances. One is model parametric uncertainty; the other is unmodelled parameters such as joint friction forces and external disturbances. Unmodelled joint frictions and external disturbances reduce performance in terms of positioning accuracy and repeatability. In order to compensate for unmodelled parameters, the design of a new controller is considered. First, the modelled and unmodelled parameters are included in a dynamic model. Then, based on the dynamic model, a new Lyapunov function is developed. After that, new nonlinear joint friction and external disturbance estimation laws are derived as an analytic solution from the Lyapunov function; thus, the stability of the closed system is guaranteed. Better values of the adaptive dynamic compensators can be extracted by fuzzy rules according to the tracking error. Limitations and knowledge about friction and external disturbances are not required for the design of the controller. The controller compensates for all possible model parameter uncertainties, all possible unknown joint frictions and external disturbances.

The aim of the virtual torsion sensor (VTS) is to observe the nonlinear deflection in the flexible joints of robotic manipulators and, by its use, improve positioning control of the joint load. This model-based approach utilizes the motor-side sensing only and, therefore, replaces the load-side encoders at nearly zero hardware costs. For being applied in the closed control loop, the stability and robustness of VTS are most crucial. This work extends the previous analysis by a general case of nonlinear joint stiffness with hysteresis and provides straightforward conditions with respect to the system dynamics. The dissipativity and passivity of the torsion-torque hysteresis map are analyzed and discussed in detail. The absolute stability of VTS inclusion into position control loop is shown based on the equivalent loop transformations and Popov criteria, including the sector conditions. Illustrative numerical examples of the control error dynamics and its convergence are provided.

A time-optimal problem for redundantly actuated robots moving on a specified path is a challenging problem. Although the problem is well explored and there are proposed solutions based on phase plane analysis, there are still several unresolved issues regarding calculation of solution curves. In this paper, we explore the characteristics of the maximum velocity curve and propose an efficient algorithm to establish the solution curve. Then we propose a straightforward method to calculate the maximum or minimum possible acceleration on the path based on the pattern of saturated actuators, which substantially reduces the computational cost. Two numerical examples are provided to illustrate the issues and the solutions.

This paper contributes by presenting a parameter identification procedure for n-degrees-of-freedom flexible joint robot manipulators. An advantage of the given procedure is the obtaining of robot parameters in a single experiment. Guidelines are provided for the computing of the joint position filtering and velocity estimation. The method relies in the filtered robot model, for which no acceleration measurements are required. The filtered model is expressed in regressor form, which allows applying a parameter identification procedure based on the least squares algorithm. In order to assess the performance of the proposed parameter identification scheme, an implementation of a least squares with forgetting factor (LSFF) parameter identification method is carried out. In order to assess the reliability of the tested identification schemes, a model-based trajectory tracking controller has been implemented twice in different conditions: one control experiment using the estimated parameters provided by the proposed scheme, and another experiment using the parameters given by the LSFF method. These real-time control experiments are compared with respect to numerical simulations using the estimated parameters for each identification method. For the proposed scheme, the comparison between experiments and numerical simulations indicates better accuracy in the torque and position prediction.

This paper focuses on fast terminal sliding mode control (FTSMC) of robot manipulators using wavelet neural networks (WNN) with guaranteed H∞ tracking performance. The FTSMC for trajectory tracking is employed to drive the tracking error of the system to converge to an equilibrium point in finite time. The tracking error arrives at the sliding surface in finite time and then converges to zero in finite time along the sliding surface. To deal with the case of uncertain and unknown robot dynamics, a WNN is proposed to fully compensate the robot dynamics. The online tuning algorithms for the WNN parameters are derived using Lyapunov approach. To attenuate the effect of approximation errors to a prescribed level, H∞ tracking performance is proposed. It is shown that the proposed WNN is able to learn the system dynamics with guaranteed H∞ tracking performance and finite time convergence for trajectory tracking. Finally, the simulation results are performed on a 3D-Microbot manipulator to show the effectiveness of the controller.

This paper presents the design, kinematics, dynamics and control of a low-cost parallel rehabilitation robot developed at the Universitat Politècnica de Valencia. Several position and force controllers have been tested to ensure accurate tracking performances. An orthopedic boot, equipped with a force sensor, has been placed over the platform of the parallel robot to perform exercises for injured ankles. Passive, active-assistive and active-resistive exercises have been implemented to train dorsi/plantar flexion, inversion and eversion ankle movements. In order to implement the controllers, the component-based middleware Orocos has been used with the advantage over other solutions that the whole scheme control can be implemented modularly. These modules are independent and can be configured and reconfigured in both configuration and runtime. This means that no specific knowledge is needed by medical staff, for example, to carry out rehabilitation exercises using this low-cost parallel robot. The integration between Orocos and ROS, with a CAD model displaying the actual position of the rehabilitation robot in real time, makes it possible to develop a teleoperation application. In addition, a teleoperated rehabilitation exercise can be performed by a specialist using a Wiimote (or any other Bluetooth device).

Complementary filtering is a frequency based method used to design data processing algorithms exploiting signals with complementary spectra. The technique is mostly used in sensor fusion architectures, but it may also be effective in the design of state estimators. In spite of its potential in several areas of robotics, the complementary filtering paradigm is poorly used as compared to alternative time domain methods. The first part of the paper aims at reviewing the basics of complementary filtering in sensor data processing and linear systems state estimation. The second part of the paper describes how to exploit the main ideas of complementary filtering to design a depth controller for an actuator redundant autonomous underwater vehicle (AUV). Unlike with alternative state space methods commonly used to address the design of control solutions for actuator redundant systems, the proposed approach allows to fully exploit the knowledge of frequency characteristics of actuators. Simulation results are reported to demonstrate the effectiveness of the proposed solution.

An iterative algorithm to minimize energy loss in kinematic chains is proposed. This algorithm is designed to low level of control where variables such as terminal states, runtime, and physical and electrical parameters of the movement are given by higher levels of control. An original complex problem of optimization is transformed into a simple quadratic programming problem subject to linear constraints by discretizing all dynamic system variables. The whole system is then converted into a recursive matrix equation that is solved iteratively. A proof of convergence is suggested. The performance of the algorithm is illustrated by using it in the motion planning of a quadruped robot.

There is increasing interest in using rehabilitation robots to assist post-stroke patients during rehabilitation therapy. The motion control of the robot plays an important role in the process of functional recovery training. Due to the change of the arm impedance of the post-stroke patient in the passive recovery training, the conventional motion control based on a proportional-integral (PI) controller is difficult to produce smooth movement of the robot to track the designed trajectory set by the rehabilitation therapist. In this paper, we model the dynamics of post-stroke patient arm as an impedance model, and propose an adaptive control scheme, which consists of an adaptive PI control algorithm and an adaptive damping control algorithm, to control the rehabilitation robot moving along predefined trajectories stably and smoothly. An equivalent two-port circuit of the rehabilitation robot and human arm is built, and the passivity theory of circuits is used to analyze the stability and smoothness performance of the robot. A slide Least Mean Square with adaptive window (SLMS-AW) method is presented for on-line estimation of the parameters of the arm impedance model, which is used for adjusting the gains of the PI-damping controller. In this paper, the Barrett WAM Arm manipulator is used as the main hardware platform for the functional recovery training of the post-stroke patient. Passive recovery training has been implemented on the WAM Arm, and the experimental results demonstrate the effectiveness and potential of the proposed adaptive control strategies.

A new image-based controller is proposed for the robotic system with the joint velocity signals unavailable. The Immersion and Invariance (I&I) observer is applied to estimate the unknown velocity information. Compared with the general velocity observer, the I&I observer can estimate the unknown velocity exponentially. We consider the case that the exact camera parameters are not known. The corresponding adaptive controller is designed for the robot system and the stability is rigorously proven by using Lyapunov theorem. Finally, simulations are performed and the results show the effectiveness of the proposed control approach.

In this work, a generalized adaptive control scheme for the global position stabilization of robot manipulators with bounded inputs is proposed. It gives rise to various families of bounded controllers with adaptive gravity compensation. Compared with the adaptive approaches previously developed in a bounded-input context, the proposed scheme guarantees the adaptive regulation objective: globally, avoiding discontinuities in the control expression as well as in the adaptation auxiliary dynamics, preventing the inputs to reach their natural saturation bounds, and imposing no saturation-avoidance restriction on the control gains. Experimental results corroborate the efficiency of the proposed adaptive scheme.

In this paper a new sliding mode controller for set-point control of robot manipulators is proposed. The controller does not use any part of the robot dynamics in the control law. Thus, it is structurally simpler than other sliding mode controllers where the control law uses a nominal model of the robot dynamics. The controller uses a new nonlinear Proportional-Integral-Derivative (PID) sliding surface. The stability of the controlled robot dynamics is proved. On applying the boundary-layer approach to remove chattering, a nonlinear PID controller exists inside the boundary layer. This PID controller ensures that the error tend to zero asymptotically if there is no disturbances applied to the robot dynamics.

This paper presents biped walking control using a library of optimal trajectories. Biped walking control is formulated as an optimal control problem. We use a parametric trajectory optimization method to find the periodic steady-state walking trajectory. As a second stage, we use Differential Dynamic Programming to generate a library of optimal trajectories and locally linear models of the optimal control law, which are used to construct a more global control law. The proposed controller is compared with a trajectory tracking controller using optimal gains. The utility and performance of the proposed method are evaluated using simulated walking control of a planar five-link biped robot.

Over the past two decades, robots have been increasingly used in biomedical applications such as bone cutting. Traditional automated manufacturing processes are often unable to meet the safety and accuracy requirements for such applications, particularly for cutting inhomogeneous constitutions of bone. In this case, human–robot cooperation may prove to be an effective approach. In this paper, we demonstrate that a hybrid parallel manipulator under cooperative force control can achieve accurate bone cutting with sufficient safety guaranteed. First, a hybrid parallel manipulator was constructed to provide the required rigidity for bone cutting. Then a two-loop controller was designed to implement the human–robot cooperation in bone cutting. The position control loop of adaptive fuzzy control is responsible for achieving high-tracking performance by overcoming varying friction forces from the mechanism. The force control loop of the cooperative force control adjusts the feed rate of the cutter according to the bone slicing conditions and operator's supervisory commands. The experimental results show that the proposed controller can effectively achieve the required accuracy in bone cutting with required safety.

This work is dedicated to the analysis of the application of active impedance control for the realisation of three objectives simultaneously: velocity regulation in free motion, impact attenuation and finally force tracking. At first, a brief analysis of active impedance control is made, deducing the value of each parameter in order to achieve the three objectives. It is demonstrated that the system may be made overdamped with the adequate selection of the parameters if the characteristics of the environment are known, avoiding high overshoots of force during the impact. The second and most important contribution of this work is an additional measure for impact control in the case when the characteristics of the environment are unknown. It consists in switching among different values of the parameters of the impedance in order to dissipate faster the energy of the system, limiting the peaks of force and avoiding losses of contact. The optimal switching criteria are deduced for every parameter in order to dissipate the energy of the system as fast as possible. The results are verified in simulation.

In this paper we are concerned with control of rigid robots equipped with brushless DC-motors (BLDC) when the electric dynamics of these actuators is taken into account. We show for the first time that a saturated PD controller suffices to achieve global asymptotic stability. Our controller is the simplest controller proposed until now to solve this problem: it only requires position measurements and linear feedback of electric current.

In this paper a control law, which consists of a fuzzy logic controller plus a nonlinear effects negotiator for a flexible robot manipulator, is presented. The nonlinear effects negotiator is used to enhence the control system's ability in dealing with the uncertainty of the mathematical model. The control algorithm is simple and easy to tune as opposed to conventional control law which requires time consuming gains selections. To obtain fuzzy control rules, an error response plane method is proposed.

This paper presents a neural network based control strategy for the trajectory control of robot manipulators. The neural network learns the inverse dynamics of a robot manipulator without any a priori knowledge of the manipulator inertial parameters nor any a priori knowledge of the equation of dynamics. A two step feedback-error-learning process is proposed. Strategies for selection of the training trajectories and difficulties with on-line training are discussed.

After the enthusiasm for creating “intelligent robots” in the early 1980's, progress of robotics research in the past decade has not fulfilled our expectations but revealed various difficulties in understanding motor control by man and implementing intelligent functions in robotic machines. To regain the initiative in the development of intelligent machines, this paper first presents a critical review of the state of the art of robot control and points out the necessity for improving robot servo-loops in order to facilitate skilled and dexterious motions in robotic manipulators and mechanical hands. It is then shown that the introduction of a quasi-natural potential in Lagrange's formulation of robot dynamics gives rise to the design of hyperstable PID servo-loops, which establish global asymptotic stability of set-point control. The hyperstability theoretical framework is then applied to the design of control commands in various control problems, such as hybrid (position/force) control, impedance control, model-based adaptive control, and learning control. In all cases, the passivity concept of residual robot dynamics plays a vital role in conjunction with the concept of feedback connections of two hyperstable nonlinear or linear blocks.

In this paper sliding mode motion design is considered for nonlinear plants which are linear with respect to control input. The dynamics of the robotic manipulators is treated with and without those of the actuators. When the dynamics of the actuators is included a design of the sliding modes for the systems with discontinuous control is performed. If actuators' dynamics is negelected the control is assumed to be continuous quantity. By combining the variable structure systems and Lyapunov designs a new algorithm is developed which has all the good properties of the sliding mode systems while avoiding unnecessary discontinuity of the control thus eliminating chattering. Neither the explicit calculation of the equivalent control, nor high gain inside the boundary layer are used. The parameters of the control depend on the plant's gain matrix, and the gradients of the sliding mode manifold. This control method is then applied to develop a unified control strategy for the motion control systems including the path tracking control, the impedance control and the force control of a robotic manipulator. It is shown that all these tasks can be formulated in the same mathematical form in which selected so-called sliding mode functions must track their references. In this way the systems state is forced to remain on the selected manifold in the state space after reaching it. The solution is interpreted in both the Joint space and the Work space for n -degrees of freedom robotic manipulators.