Study on the exponential path tracking control of robot manipulators via direct adaptive methods

Exponential path tracking control represents an important issue pertaining to the transient performance of robot control systems. In this paper, the so-called Exp-transformation is applied to obtain transformed robot dynamics models which are used to derive several adaptive control algorithms that achieve exponential path tracking. In contrast to the existing composite adaptive control method; where both the tracking error and the prediction error are used and persistent excitation (p.e.) is required, the proposed strategy requires only the tracking error. This makes the control structure simpler and easier to implement. The main contribution of this paper is the development of practical control strategies for which the p.e. requirement is completely removed (as opposed to relaxing it to semi-p.e. as was done in a recent work). The fundamental idea introduced for exponential stability analysis is conceptually simple and global results are obtained.     

Cartesian path generation of robot manipulators using continuous genetic algorithms

In this paper, the authors describe a novel technique based on continuous genetic algorithms (CGAs) to solve the path generation problem for robot manipulators. We consider the following scenario: given the desired Cartesian path of the end-effector of the manipulator in a free-of-obstacles workspace, off-line smooth geometric paths in the joint space of the manipulator are obtained. The inverse kinematics problem is formulated as an optimization problem based on the concept of the minimization of the accumulative path deviation and is then solved using CGAs where smooth curves are used for representing the required geometric paths in the joint space through out the evolution process. In general, CGA uses smooth operators and avoids sharp jumps in the parameter values. This novel approach possesses several distinct advantages: first, it can be applied to any general serial manipulator with positional degrees of freedom that might not have any derived closed-form solution for its inverse kinematics. Second, to the authors’ knowledge, it is the first singularity-free path generation algorithm that can be applied at the path update rate of the manipulator. Third, extremely high accuracy can be achieved along the generated path almost similar to analytical solutions, if available. Fourth, the proposed approach can be adopted to any general serial manipulator including both nonredundant and redundant systems. Fifth, when applied on parallel computers, the real time implementation is possible due to the implicit parallel nature of genetic algorithms. The generality and efficiency of the proposed algorithm are demonstrated through simulations that include 2R and 3R planar manipulators, PUMA manipulator, and a general 6R serial manipulator.     

Minimum-time control of robotic manipulators with geometric path constraints

Conventionally, robot control algorithms are divided into two stages, namely, path or trajectory planning and path tracking (or path control). This division has been adopted mainly as a means of alleviating difficulties in dealing with complex, coupled manipulator dynamics. Trajectory planning usually determines the timing of manipulator position and velocity without considering its dynamics. Consequently, the simplicity obtained from the division comes at the expense of efficiency in utilizing robot's capabilities. To remove at least partially this inefficiency, this paper considers a solution to the problem of moving a manipulator in minimum time along a specified geometric path subject to input torque/force constraints. We first describe the manipulator dynamics using parametric functions which represent geometric path constraints to be honored for collision avoidance as well as task requirements. Second, constraints on input torques/ forces are converted to those on the parameters. Third, the minimum-time solution is deduced in an algorithm form using phase-plane techniques. Finally, numerical examples are presented to demonstrate utility of the trajectory planning method developed.     

A fault-tolerant control algorithm having a decentralizedautonomous architecture for space hyper-redundant manipulators

Adaptation to partial failure is one of the most important requirements for space robotics, since space robots cannot be repaired after they have been launched. We propose a decentralized autonomous control algorithm for hyper-redundant manipulators that uses parallel processing with low-performance processors to achieve this adaptation. In this paper, a number of manipulator joints are locked at a certain angle in a computer simulation and the adaptability of the control algorithm to these failures is assessed. The control algorithm successfully continues its positioning task at a rate of more than 90%, even after half of its joints have failed. The control algorithm is also compared with behavior-based control architecture     

Enhanced direct adaptive fuzzy control of robot manipulators

In this article, an enhanced direct adaptive fuzzy robot controller is developed to overcome problems of high‐frequency oscillations across the boundary of the constraint set and large control signals. The direct adaptive fuzzy robot control algorithm employs tracking errors of the joint motion to drive the parameter adaptation. The predominant concern of the adaptation law is to reduce the tracking errors, and closed‐loop stability is ensured by appending a supervisory controller. This adaptive controller, appended with the supervisory controller, does not require the exact robot dynamics, but only the boundary of the dynamics. Theoretical results and simulation studies on a two‐link robot manipulator show that by modifying the activation function of the supervisory controller, the enhanced direct adaptive fuzzy robot controller is as robust as before and the problems of high‐frequency oscillations across the boundary of the constraint set and large control signals are alleviated. ©1999 John Wiley & Sons, Inc.     

A variational approach to path planning for hyper-redundant manipulators

Motion planning for hyper-redundant manipulators in a complicated and cluttered workspace is a challenging problem. Many of the path planning algorithms, based on cell decomposition or potential field, fail due to the high dimensionality and complex nature of the C-space. Probabilistic roadmap methods (PRM) which have been proven to be successful in high dimensional C-spaces suffer from the drawback of generating paths which involve a lot of redundant motion. In this paper, we propose a path optimizing method to improve a given path in terms of path length and the safety against the collisions, using a variational approach. The capability of variational calculus to optimize a path is demonstrated on a variety of examples. The approach succeeds in providing a good quality path even in high dimensional C-spaces.     

Time-optimal path generation for continuous and quasi-continuous path control of industrial robots

For a given continuous path, the problem of designing a time-optimal time-parametrization is considered. First, algorithms are presented which, under rather mild assumptions, yield the exact solution within two computational steps consisting of a forward and a backward computation. Then, the problem of quasi-continuous robot motion is investigated in detail. An algorithm of the same type results, but the computational burden is considerably reduced by making appropriate use of the special structure of the problem. By this, on-line use becomes feasible.Work supported by Oesterreichischer Fonds zur Foerderung der wissenschaftlichen Forschung.     

Biologically inspired node generation algorithm for path planning of hyper-redundant manipulators using probabilistic roadmap

This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration space surrounding existing nodes in the roadmap and uses a combination of random and deterministic search methods that emulate the behaviour of octopus limbs. The strategy consists of randomly mutating the states of the links near the end-effector, and mutating the states of the links near the base of the robot toward the states of the goal configuration. When combined with the small tree probabilistic roadmap planner, the method was successfully used to solve the narrow passage motion planning problem of a 17 degree-of-freedom manipulator.     

一种结合直达声补偿策略的混响抑制算法

混响是声音经过室内墙壁等物体反射、吸收后多径传播叠加产生的,是导致语音识别系统性能下降的主要因素之一。基于TF-GSC的混响消除算法在估计混响功率谱时可能会出现过估计的现象,导致输出语音失真。提出一种直达声补偿策略,并将其应用到混响抑制算法中去。实验结果表明,直达声补偿策略减小了输出语音失真,提高了输出语音质量。     

Hybrid adaptive control laws solving a path following problem for non-holonomic mobile manipulators

In this paper a general solution to the path following problem for mobile manipulators with non-holonomic mobile platform has been presented. New proposed control algorithms — for mobile manipulators with fully known dynamics or with parametric uncertainty in the dynamics — take into considerations the kinematics as well as the dynamics of the non-holonomic mobile manipulator. The convergence of the control algorithms is proved using the LaSalle's invariance principle.     

A multivariable control scheme for robot manipulators

The article puts forward a simple scheme for multivariable control of robot manipulators to achieve trajectory tracking. The scheme is composed of an inner loop stabilizing controller and an outer loop tracking controller. The inner loop utilizes a multivariable PD controller to stabilize the robot by placing the poles of the linearized robot model at some desired locations. The outer loop employs a multivariable PID controller to achieve input-output decoupling and trajectory tracking. The gains of the PD and PID controllers are related directly to the linearized robot model by simple closed-form expressions. The controller gains are updated on-line to cope with variations in the robot model during gross motion and for payload change. Alternatively, the use of high gain controllers for gross motion and payload change is discussed. Computer simulation results are given for illustration.     

Adaptive control of parallel manipulators via fuzzy-neural network algorithm

This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms (FNNA).With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom (DoF). The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.     

A low-level control interface for robot manipulators

This paper will discuss a possible low-level control interface for a robot manipulator. The first section will present background information describing the capabilities and limitations afforded by the use of interfaces and a proposed system modularization that supports interface specification. The next section presents three possible low-level robot control interfaces within this system. Each will be elaborated on by a specification of the interface information and its use, timing considerations and potential limitations. The paper concludes with a summary discussion and recommendation.     

Microprocessor control of multiaxes continuous path motion

Continuous path position control systems differ from simple point-to-point controllers because of the additional requirement for path trajectory control. This paper describes a multimicrocomputer structure for the control of multiaxes continuous path motion. Path control is achieved by the means of real-time interpolation. Loop closures for position servomechanisms are implemented in software. Numerical control tasks are distributed in a manner to allow both interpolation and servo loop closure for each degree of freedom on a separate microcomputer. This approach has the advantage of expandibility from two to three or more degrees of freedom by simply adding extra axis controller microcomputer cards to the system.     

Adaptive control of parallel manipulators via fuzzy-neural network algorithm

This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom （DoF）. The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.     

A global approach for the optimal path generation of redundant robot manipulators

In this article the optimal path generation of redundant robot manipulators is considered as an optimization problem, with given kinematics and subject to the robot requirements and a singularities avoidance constraint. This problem is formulated as a constrained continuous optimal control problem, which allows to consider joints and velocities constraints and/or manipulator dynamics. This approach is exemplified for a planar redundant manipulator and the resultant state constrained problem is solved by an efficient iterative numerical technique.     

A direct search variant of the simulated annealing algorithm for optimization involving continuous variables

A memory-based simulated annealing algorithm is proposed which fundamentally differs from the previously developed simulated annealing algorithms for continuous variables by the fact that a set of points rather than a single working point is used. The implementation of the new method does not need differentiability properties of the function being optimized. The method is well tested on a range of problems classified as easy, moderately difficult and difficult. The new algorithm is compared with other simulated annealing methods on both test problems and practical problems. Results showing an improved performance in finding the global minimum are given.Scope and purposeThe inherent difficulty of global optimization problems lies in finding the very best optimum (maximum or minimum) from a multitude of local optima. Many practical global optimization problems of continuous variables are non-differentiable and noisy and even the function evaluation may involve simulation of some process. For such optimization problems direct search approaches are the methods of choice. Simulated annealing is a stochastic global optimization algorithm, initially designed for combinatorial (discrete) optimization problems. The algorithm that we propose here is a simulated annealing algorithm for optimization problems involving continuous variables. It is a direct search method. The strengths of the new algorithm are: it does not require differentiability or any other properties of the function being optimized and it is memory-based. Therefore, the algorithm can be applied to noisy and/or not exactly known functions. Although the algorithm is stochastic in nature, it can memorise the best solution. The new simulated annealing algorithm has been shown to be reliable, fast, general purpose and efficient for solving some difficult global optimization problems.     

A global adaptive learning control for robotic manipulators

This paper addresses the problem of designing a global adaptive learning control for robotic manipulators with revolute joints and uncertain dynamics. The reference signals to be tracked are assumed to be smooth and periodic with known period. By developing in Fourier series expansion the input reference signals of every joint, an adaptive learning PD control is designed which ‘learns’ the input reference signals by identifying their Fourier coefficients: global asymptotic and local exponential stability of the tracking error dynamics are obtained when the Fourier series expansion of each input reference signal is finite, while arbitrary small tracking errors are achieved otherwise. The resulting control is not model based and depends only on the period of the reference signals and on some constant bounds on the robot dynamics.