A new approach to dynamic feedback linearization control of aninduction motor

A new approach to dynamic feedback linearization control of an induction motor is given, Previously, it has been shown that the dynamic model of an induction motor, consisting of speed, stator currents, and rotor flux, is dynamically feedback linearizable. However, the controller and transformation were valid only as long as the motor torque was nonzero and the methodology required switching between two computationally complex transformations. Here it is shown that by considering the direct-quadrature model of the induction motor, a single dynamic feedback linearizing transformation exists and is valid (essentially) as long as the (magnitude of the) rotor flux is nonzero. Furthermore, the resulting control computations are well within the capabilities of contemporary microprocessor technology     

A new approach to version control

A method for controlling versions of software and other hierarchically structured entities is presented. Using the variant structure principle, a particular version of an entire system is formed by combining the most relevant existing versions of the various components of the system. An algebraic version language that allows histories (numbered series), subversions (or variants), and joins is described. It is shown that the join operation is simply the lattice least upper bound and together with the variant structure principle, provides a systematic framework for recombining divergent variants. The utility of this approach is demonstrated using LEMUR, a programming environment for modular C programs, which was developed using itself. The ways in which this notion of versions is related to the possible world semantics of intensional logic are discussed     

Sensorless point-to-point control for a musculoskeletal tendon-driven manipulator: analysis of a two-DOF planar system with six tendons

Tendon-driven robot utilizes only tensile force (i.e. tension) for motion generation. Therefore, a redundant actuation is characteristically necessary, and then it yields the internal force among tendons. Given the internal force for balance at a desired posture, the musculoskeletal tendon-driven manipulator has the inherent possibility of point-to-point position control without any sensory feedback. However, the motion convergence is strongly governed by the arrangement of tendons.This study analyzes the mathematical conditions of convergence for this sensorless position control by use of a Lyapunov function. Subsequently, targeting the two-link musculoskeletal structure with six tendons, the sufficient conditions for the convergence at desired posture are further defined by employing an approximation of the tendon-length based on a Taylor expansion. Finally, the convergent conditions are verified through simulation and validated via experimental results.     

A new approach to adaptive control: no nonlinearities

In adaptive control the goal is to design a controller to control an uncertain system whose parameters may be changing with time. Typically the controller consists of an identifier (or tuner) which is used to adjust the parameters of a linear time-invariant (LTI) compensator, and under suitable assumptions on the plant model uncertainty it is proven that good asymptotic behaviour is achieved, such as model matching (for minimum phase systems) or stability. However, a typical adaptive controller does not track time-varying parameters very well, and it is often highly nonlinear, which can result in undesirable behaviour, such as large transients or a large control signal. Furthermore, most adaptive controllers provide only asymptotic tracking, with no ability to design for a pre-specified settling time.Here we propose an alternative approach, which yields a linear periodic controller. Rather than estimating the plant or compensator parameters, instead we estimate what the control signal would be if the plant parameters were known. In this paper we argue the utility of this approach and then examine the first order case in detail, including a simulation. We also explore the benefits and limitations of the approach.     

A new approach to averaging level control

In this paper, a new simple algorithm for averaging level control is presented. The proposed algorithm is based on the use of a static nonlinear controller to improve the input flow filtering by reducing the slope of the controller around the average input flow. In order to adapt the controller to changes in the average input flow, a low-pass filtered value of the estimated input flow is used to update the nonlinear function that defines the static controller. The stability of the closed loop is demonstrated, and the validity of the proposed strategy is tested in simulations and on a real oil refinery process.     

A new approach to stochastic adaptive control

The principal techniques used up to now for the analysis of stochastic adaptive control systems have been 1) super-martingale (often called stochastic Lyapunov) methods and 2) methods relying upon the strong consistency of some parameter estimation scheme. Optimal stochastic control and filtering methods have also been employed. Although there have been some successes, the extension of these techniques to a broader class of adaptive control problems, including the case of time-varying parameters, has been difficult. In this paper a new approach is adopted: if an underlying Markovian state-space system for the controlled process is available, and if this process possesses stationary transition probabilities, then the powerful ergodic theory of Markov processes may be applied. Subject to technical conditions, such as stability, one may deduce 1) the existence of an invariant measure for the process and 2) the convergence almost surely of the sample averages of a function of the state process (and of its expectation) to its conditional expectation. The technique is illustrated by an application to a previously unsolved problem involving a linear system with unbounded random time-varying parameters.     

Hopf bifurcation control: A new approach

In this paper we find conditions to control a Hopf bifurcation in a class of nonlinear systems whose linear approximation has two eigenvalues on the imaginary axis, without assuming that the system is controllable. We use the center manifold theorem to project the dynamics on a two dimensional manifold, and design a controller that permits us to decide the stability and direction of the emerging periodic solution.     

A new approach to adaptive fuzzy control: the controller outputerror method

The controller output error method (COEM) is introduced and applied to the design of adaptive fuzzy control systems. The method employs a gradient descent algorithm to minimize a cost function which is based on the error at the controller output. This contrasts with more conventional methods which use the error at the plant output. The cost function is minimized by adapting some or all of the parameters of the fuzzy controller. The proposed adaptive fuzzy controller is applied to the adaptive control of a nonlinear plant and is shown to be capable of providing good overall system performance.     

A constrained optimization approach to resolving manipulator redundancy

A redundant manipulator can achieve additional tasks by utilizing the degree of redundancy, in addition to a basic motion task. While some additional tasks can be performed by optimizing a proper objective function, other additional tasks can be performed by satisfying a set of kinematic inequality constraints. In this article, we reformulate a redundancy resolution problem with multiple criteria into a local equality and inequality constrained optimization problem, and propose a method to solve it at velocity level. The proposed method is efficient, especially when the number of additional tasks is larger than the degree of redundancy. It also systematically assigns the priorities between the additional tasks. Besides the computational efficiency, the method has a cyclic property. © 1996 John Wiley & Sons, Inc.     

A new delay system approach to network-based control

This paper presents a new delay system approach to network-based control. This approach is based on a new time-delay model proposed recently, which contains multiple successive delay components in the state. Firstly, new results on stability and H_∞ performance are proposed for systems with two successive delay components, by exploiting a new Lyapunov-Krasovskii functional and by making use of novel techniques for time-delay systems. An illustrative example is provided to show the advantage of these results. The second part of this paper utilizes the new model to investigate the problem of network-based control, which has emerged as a topic of significant interest in the control community. A sampled-data networked control system with simultaneous consideration of network induced delays, data packet dropouts and measurement quantization is modeled as a nonlinear time-delay system with two successive delay components in the state and, the problem of network-based H_∞ control is solved accordingly. Illustrative examples are provided to show the advantage and applicability of the developed results for network-based controller design.     

A new approach to model reference adaptive control

In classical model reference adaptive control, the goal is to design a controller to make the closed-loop system act like a prespecified reference model in the face of significant plant uncertainty. Typically, the controller consists of an identifier (or tuner) which is used to adjust the parameters of a linear time-invariant (LTI) compensator, and under suitable assumptions on the plant model uncertainty it is proven that asymptotic matching is achieved. However, the controller is typically highly nonlinear, and the closed loop system can exhibit undesirable behavior, such as large transients or a large control signal, especially if the initial parameter estimates are poor. Furthermore, its ability to tolerate time-varying parameters is typically limited. Here, we propose an alternative approach. Rather than estimating the plant or compensator parameters, instead we estimate what the control signal would be if the plant parameters and plant state were known and the "ideal LTI compensator" were applied. We end up with a linear periodic controller. Our assumptions are reasonably natural extensions of the classical ones to the time-varying setting; we allow rapid parameter variations, although we add a compactness requirement. We prove that we can obtain arbitrarily good tracking, explore the benefits and limitations of the approach, and provide a simulation study.     

A new approach to quantized feedback control systems

This paper presents a new approach to the problems of analysis and synthesis for quantized feedback control systems. Both single- and multiple-input cases are considered, with complete results provided for stability and H_∞ performance analysis as well as controller synthesis for discrete-time state-feedback control systems with logarithmic quantizers. The most significant feature is the utilization of a quantization dependent Lyapunov function, leading to less conservative results, which is shown both theoretically and through numerical examples.     

Robust adaptive control of a robotic manipulator including motor dynamics

An adaptive nonlinear control law that incorporates the manipulator dynamics as well as dynamics of the actuator is developed in this article. The proposed adaptive robust tracking controller requires position measurements only. The controller consists of two parts: a linear observer that generates an estimated error from the error on the joint position, together with a linear feedback controller that utilizes the estimated states. The second part is an adaptive controller that utilizes the feedback states from the linear observer to generate a control effort that takes into consideration the dynamic parameters variation of the robot and actuator. The closed loop system is locally stable in the Lyapunov sense. © 1998 John Wiley & Sons, Inc.     

基于步进电机的工业取料机械手的定位控制

给出了步进电机控制的工业取料机械手定位方法，给出了符合实际情况的电机升降速曲线图和软件实现方法。最后，通过实验证明了该方法精度高、稳定性好，目前该方法成功运用于工业取料机械手。     

Parametric modelling approach using bacterial foraging algorithms for modelling of flexible manipulator systems

This paper presents current work on biologically-inspired optimisation techniques based on bacterial foraging algorithms (BFAs) and their application to modelling of a single-link flexible manipulator. The objective of this work is to develop a single-link flexible manipulator model based on modified BFAs. First, three adaptation mechanisms of the chemotactic step size mechanism of BFA are proposed. New approaches of adaptable chemotactic step size are based on linear, quadratic and exponential functions of cost function value. Then, these three adaptive BFAs are used to develop three single-input single-output models to characterise a flexible manipulator from torque input to hub-angle, hub velocity and end-point acceleration responses. The performances of the adaptive BFAs are compared to that of standard BFA based on convergence to optimum value, the optimum value achieved and time-domain and frequency domain responses of the developed models.     

Adaptive manipulator control: A case study

The author's previous work (1986, 1987) utilized the particular structure of manipulator dynamics to develop a simple, globally convergent adaptive controller for manipulator trajectory control problems. After summarizing the basic algorithm, they demonstrate the approach on a high-speed two-degree-of-freedom semi-direct-drive robot. They show that the dynamic parameters of the manipulator, assumed to be initially unknown, can be estimated within the first half second of a typical run, and that accordingly, the manipulator trajectory can be precisely controlled. These experimental results demonstrate that the adaptive controller enjoys essentially the same level of robustness to unmodeled dynamics as a PD (proportional and differential) controller, yet achieves much better tracking accuracy than either PD or computed-torque schemes. Its superior performance for high-speed operations, in the presence of parametric and nonparametric uncertainties, and its relative computational simplicity, make it an attractive option both for addressing complex industrial tasks, and for simplifying high-level programming of more standard operations     

A new approach to fuzzy control using the distending function

Here, we develop a fuzzy controller using fuzzy arithmetics and a new type of membership function. The proposed new fuzzy control technique is simple, fast and computationally efficient, compared to the classical techniques (Mamdani, Takagi Sugeno) and it can also adapt to the process dynamics. The unique features are: 1) A new class of parametric membership function called the Distending Function (DF) is introduced; 2) A general parametric operator system is used. It utilizes most of the fuzzy operator systems for evaluating the knowledge base; 3) Inference is based on fuzzy arithmetic operations; 4) This leads to a computationally efficient single-step defuzzification. With these concepts, the paradigm of fuzzy control design changes radically. Using this technique with an optimization method, an adaptive fuzzy controller is designed. This adaptive controller adjusts to the changing dynamics of the non-linear processes by tuning our new type of membership function. The effectiveness of the proposed methodology is demonstrated on two industrial processes (a water tank system and continuously stirred tank reactor system).     

IDFC: A new approach to control bifurcation in TCP/RED

The main objective of this research is to analyze the bifurcation phenomenon in Internet congestion model for Transmission Control Protocol (TCP) with Random Early Detection (RED). This problem can be divided to many categories considering different viewpoints. Different approaches of modeling (continuous and discrete models) and various system structures (control in end node or routers) are some of these categories. The most significant method in control of such systems is Delayed Feedback Controller (DFC). In this paper, a discrete model is considered and a new algorithm Integral DFC (IDFC) is presented that has many preferences over similar algorithms, which are illustrated by simulation results and analytical discussions.     

A new spatial hyper-redundant manipulator

This work presents a novel hyper-redundant manipulator. Such a manipulator is built with a variable number of tandem-assembled modules. Each module consists of a 3-dof parallel manipulator with asymmetric extremities in which moving platform possesses mixed motions with respect to the base platform. The manipulator's architecture is so simple that the forward position analysis is presented in closed-form solution, more specifically, in echelon-form solution. On the other hand, the velocity and acceleration analyses are carried out using the theory of screws. Finally, a case study consisting of solving the kinematic analysis of a 30-dof hyper-redundant manipulator is presented.