Discrete-time learning control for robotic manipulators

A discrete-time learning control for robotic manipulators is studied from a viewpoint of zeros of a control system. The signal used for learning is a position signal of each joint detected by a pulse encoder. Firstly, we show that when sampling period is small, the learning is unstable because of an unstable zero of the system. Secondly, we show that we can stabilize the learning control system with small sampling period by using a low pass filter, and also show that a tracking error converges not to zero but to a certain value in that case. Finally, experimental results on the trajectory control of robotic manipulators using the discrete-time learning control are shown. These experimental results agree well with the analytical ones.

Learning control for robotic manipulators has been developed over the past decade and to the best of the authors’ knowledge, it is still in its infant development stage; the authors believe that it will become one of the most promising directions in the control area in robotic manipulators. Learning control in robotic manipulators is mainly used to address the issue that the friction at the joints of robotic mechanisms and other uncertainties may exist in the dynamic models, which are very complex and may even be impossible to model mathematically. In this paper, the authors review and discuss the learning control in robotic manipulators and some issues in learning control for robotic manipulators are also illustrated. This review is able to give a general guideline for future research in learning control for robotic manipulators.

Further results on adaptive iterative learning control of robot manipulators

Although model-free reinforcement learning methods are illustrated to own abilities to learn a wide range of control and decision-making tasks, it is time-consuming and needs a huge number of samples to learn optimal policy. Therefore, model-based reinforcement learning algorithms are always treated as the most efficient ways to settle robotic control problems. In this work, a model-based reinforcement learning tracking control method with free energy is proposed. The control objective is to make sure that the proposed controller can let a robotic manipulator with parameters uncertainties and disturbance follow the desired trajectory and obtain good tracking performance. The main contribution of this work is to employ the free energy principle to optimize the problem of robotic control. The probability distribution of robotic dynamic model is learnt by a evolving neural network, a free energy based reinforcement learning control method uses the learnt model to find optimal control policy. The experiment results demonstrate that the proposed tracking control method is effective in solving robotic tracking control problems.

Adaptive learning control of robot manipulators via incremental hybrid neural network

Learning control algorithms have been proposed for error compensation in repetitive robotic manipulator tasks. It is shown that the performance of such control algorithms can be seriously degraded when the feedback data they use is relatively sparse in time, such as might be provided by vision systems. It is also shown that learning control algorithms can be modified to compensate for the effects of sparse data and thereby yield performance which approaches that of systems without limitations on the sensory information available for control.

This paper presents a robust learning control algorithm which learns the entire span of robot trajectory within a finite time interval. The learning controller treats the uncertain parameters as well as unknown external disturbances with the aid of the linear parameterization property of the robot system and robust feedback control input. It is shown that the robot motion converges exponentially to the desired one as the iteration continues.

In this paper, we propose an error-tracking iterative learning control scheme to tackle the position tracking problem for robot manipulators with random initial errors and iteration-varying reference trajectories. Different from general usual ones, the control strategy in our work is to drive system errors perfectly track the desired error trajectories over the whole time interval as the iteration number increases, by which, the position trajectory and velocity trajectory can respectively track their reference trajectories during the predefined part operation interval. For fulfilling the control design, a new construction method of desired error trajectories is presented to remove the perfect initial resetting condition, which must be satisfied in most traditional iterative learning control algorithms. The uncertainties and disturbances in the robotic system dynamics are compensated by the robust approach and iterative learning approach, combinedly.

Adaptive iterative learning control for robot manipulators: Experimental results

A method for optimal trajectory control of robotic manipulators is considered in this paper. In particular, a control scheme is proposed such that, while the manipulator passes two or more prescribed points, the resulting trajectory of motion is smooth. Such a problem is formulated by employing a criterion of minimum-mean-square acceleration during the entire period of motion. Although the motion of robot manipulators is governed by a system of second-order differential equations which are highly non-linear, it is shown that the control problem can be solved rigorously under such a criterion. The optimal control consists of non-linear compensation terms and other linear feedback terms. The results of computer simulation studies for a two-link manipulator are also included.

Discrete iterative learning control of robotic manipulators

Trajectory extension learning is an incremental method for training an artificial neural network to approximate the inverse dynamics of a robot manipulator. Training data near a desired trajectory is obtained by slowly varying a parameter of the trajectory from a region of easy solvability of the inverse dynamics toward the desired behavior. The parameter can be average speed, path shape, feedback gain, or any other controllable variable. As learning proceeds, an approximate solution to the local inverse dynamics for each value of the parameter is used to guide learning for the next value of the parameter. Convergence conditions are given for two variations on the algorithm. Examples are shown of application to a real 2-joint direct drive robot arm and a simulated 3-joint redundant arm, both using simulated equilibrium point control.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A real-time Machine Learning Control (MLC) of articulated robotic manipulators is presented in this work by exploiting the Lion Optimization Algorithm (LOA) which is combined with the Naïve Bayes (LNB).Here, the proposed model is used for the real-time MLC of robotic manipulators by exploiting the "Fractional Order Proportional-Integral-Derivative (FOPID)" control scheme.Using the proposed LNB model MLC control gain parameters are tuned.By considering the dynamics of the actuator, to convene significant timing constraints, a "Real-Time Operating System (RTOS)" on a microprocessor collaborates with the LNB-MLC.At last, the Mechatronic design and investigational setup of a 6-degree of freedom (DOF) expressed robotic manipulator are modeled.The performance analysis is presented to show the advantage of the adopted models.While compared with the existing control models, the adopted optimization model has practice and theoretical consequences regarding online parameter tuning, real-time ability, and convergent behavior.In both industry and academia, the adopted MLC approaches are appropriate to design the real-time modern controllers.

Robust trajectories following control of a 2-link robot manipulator via coordinate transformation for manufacturing applications

A regressor-based variable-structure control scheme has been developed for the trajectory control of robot manipulators in the presence of disturbances, parameter variations, and unmodeled dynamics. The method is based on the regressor structure given by J.J.E. Slotine and W. Li, without parameter adaptation. This avoids the requirement of persistency of excitation, and the convergence of the overall transient exponential. The method is robust against a class of state-dependent uncertainties, which may result, for example, from unmodeled dynamics. The problem of chattering is solved by the smoothing control law. It is with respect to a set around the origin, which can be made arbitrarily small. To illustrate the feasibility of this controller, it was implemented using a Motorola M68000 microprocessor on a two-link revolute joint manipulator subjected to a variable payload. Experimental results confirm the validity of accurate tracking capability and the robust performance. >