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Control Theory and Applications, IEE Proceedings -

Issue 4 • Date Jul 1995

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Displaying Results 1 - 17 of 17
  • Robustness bounds for continuous systems with LQ regulators

    Page(s): 272 - 276
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (248 KB)  

    The problem of robust stability in linear quadratic (LQ) regulator systems with parametric uncertainties is considered. The method presented yields improved bounds on uncertain parameters. A robustification procedure for LQ regulators in systems is presented View full abstract»

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  • Stable and fast neurocontroller for robot arm movement

    Page(s): 378 - 384
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (472 KB)  

    The authors present new learning algorithm schemes using feedback error learning for a neural network model applied to adaptive nonlinear control of a robot arm, namely the QR-WRLS algorithm and its parallel counterpart algorithms. It involves a QR decomposition to transform the system into upper triangular form, and estimation of the neural network weights by a weighted recursive least squares (WRLS) technique. The QR decomposition method, which is known to be numerically stable, is exploited in an algorithm which involves successive applications of a unitary transformation (Givens rotation) directly to the data matrix. The WRLS weight estimation method chosen allows the selection of weighting factors such that each of the linear equations is weighted differently. The QR-WRLS algorithm is shown to provide fast, robust and stable online learning of the dynamic relations necessary for robot control. We show the results of applying these learning schemes with some flexible forgetting strategies to a two-link manipulator. A comparison of their performance with backpropagation algorithm and the recursive prediction error learning algorithm is included View full abstract»

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  • Neural network approach to signal modelling in power systems

    Page(s): 257 - 264
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (520 KB)  

    A neural network approach to a system identification problem is presented. Traditional system identification techniques require knowledge of the model structure before parameter estimation methods can be applied. This approach requires less a priori information and the knowledge of model structure is not essential. A simulation study on a power system has demonstrated the application of this technique View full abstract»

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  • New frequency-domain design method for PID controllers

    Page(s): 265 - 271
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (428 KB)  

    This paper presents a new PID controller design method based on process frequency response information. The novel ideas lie in the way that the closed-loop performance is specified via the desired response of the control signal, and in the use of only one (for PI control) or two (for PID control) process frequency response points in the design. Straightforward analytical formulas are given for the PID controller parameters. Simulation studies are given to compare this design method with other design methods found in the literature. The results indicate that the new method provides much smoother responses in both the control signal and process output, which are generally more desirable in the process control setting View full abstract»

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  • Mobile vehicle navigation in unknown environments: a multiple hypothesis approach

    Page(s): 385 - 400
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (1072 KB)  

    The paper describes an algorithm for sensor-based map building and navigation for an autonomous mobile vehicle. The algorithm is based on the use of an extended Kalman filter to obtain estimates of the location and identity of geometric features in an unknown environment. A multitarget tracking methodology is applied to the evaluation of multiple hypotheses about the locations of geometric features in the environment. The algorithm does not require any a priori information about the environment. It is capable of initiating new geometric features and identifying the type of a geometric feature from the given set of geometric features, utilising the data provided by a set of sonar sensors. The algorithm is also capable of deleting geometric features from the map of the environment when they are no longer detected by the sensors. The implementation of the algorithm is discussed, and results using real sonar data are presented View full abstract»

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  • Multifrequency Routh approximants for linear systems

    Page(s): 351 - 358
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (468 KB)  

    A multipoint Routh γ-δ canonical continued-fraction expansion for the transfer function of a linear system is derived. Based on this general form, a multifrequency Routh approximant to the system is derived by selecting the expansion points on the imaginary axis and truncating the resulting continued-fraction expansion. A connection between the stability preservation property of the multifrequency Routh approximants and the expansion points on the imaginary axis is established. Thus the multifrequency Routh approximation procedure is flexible in deriving stable reduced-order models while fitting the frequency responses and retaining the time moments and/or Markov parameters of the impulse response of the system View full abstract»

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  • Real time identification of robot dynamic parameters using parallel processing. 1. Theory

    Page(s): 359 - 368
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (536 KB)  

    The paper develops the mathematical foundations for a parallel recursive estimator for the identification of dynamic parameters for a general n-link high performance robot. The estimation is structured so that one estimator is assigned to each link. Only composite parameters used for calculating joint torques (the inverse dynamic equations) are identified. The parallel structure makes possible the real-time estimation of dynamic parameters, including load and frictions, using only medium-speed processors View full abstract»

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  • Digital interval model conversion and simulation of continuous-time uncertain systems

    Page(s): 315 - 322
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (492 KB)  

    The paper deals with the problem of converting a continuous-time uncertain linear system to an equivalent discrete-time uncertain model and its digital simulation. The system matrices characterising the state-space representation of the original uncertain system are assumed to be interval matrices. The geometric series method together with interval arithmetic is employed to obtain the approximate discrete-time interval models. A new technique is developed to estimate the modelling errors. These modelling errors are used to modify the approximate interval models obtained via the interval geometric-series method. The resulting interval models (the enclosing interval models) are able to tightly enclose the exact uncertain model. Also their approximate discrete-time interval solutions are able to tightly enclose the exact interval solution of the continuous-time uncertain state-space equation. The proposed digital uncertain models can be used for digital simulation and digital design of continuous-time uncertain systems View full abstract»

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  • Dynamic recurrent neural network for system identification and control

    Page(s): 307 - 314
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (388 KB)  

    A dynamic recurrent neural network (DRNN) that can be viewed as a generalisation of the Hopfield neural network is proposed to identify and control a class of control affine systems. In this approach, the identified network is used in the context of the differential geometric control to synthesise a state feedback that cancels the nonlinear terms of the plant yielding a linear plant which can then be controlled using a standard PID controller View full abstract»

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  • Real time identification of robot dynamic parameters using parallel processing. 2. Implementation and testing

    Page(s): 369 - 377
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (460 KB)  

    The paper implements and demonstrates a parallel recursive estimator for the real-time identification of dynamic parameters for a general n-link high performance robot. The implementation entails the use of a parallel processor platform which includes the real-time simulation of the forward dynamics of the robot. The parallel estimator is first tested for parameter convergence using PBRS test signals and then tested in a closed loop robot control environment. A case study is presented showing the real-time convergence of the dynamic parameters following the application of a payload View full abstract»

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  • Discretisation of continuous-time control systems with guaranteed stability

    Page(s): 323 - 328
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (380 KB)  

    The paper proposes a new method for discretisation of analogue control systems which guarantees the stability of the resulting digital control system for almost any sampling period and recovers the continuous-time performance when the sampling period approaches zero. In this method the digital control system is obtained such that its transfer function from the reference input to the plant input approximately matches that of the original continuous-time control system, such that the H-norm of the error transfer function is minimum. The effectiveness of the method is illustrated through an example View full abstract»

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  • Two-degree-of-freedom linear quadratic Gaussian predictive control

    Page(s): 295 - 306
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (712 KB)  

    A two-degree-of-freedom LQGPC optimal control law is derived, which has properties in common with both the LQG and GPC control laws. The stability and robustness properties are the same as for an LQG optimal controller, but the cost of future predictive error and control action is dealt with in the same manner as for the GPC control law. An advantage of the approach is that true predictive action is possible, so the control at time t will optimally use the future reference-trajectory knowledge. The feedback controller to be implemented is time-invariant, but the future predicted control action is obtained from the polynomial equivalent of a time-varying solution, which is analogous to the solution of the finite-time deterministic LQ optimal control problem. The results can be presented in a form similar to that employed in GPC algorithms, suggesting an immediate way of achieving input and output constraints View full abstract»

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  • Stability robustness bounds for linear systems with delayed perturbations

    Page(s): 345 - 350
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (360 KB)  

    The paper presents sufficient conditions for delay-independent asymptotic stability for linear systems with multiple time-varying delayed perturbations. It demonstrates that judicious selection of Lyapunov functionals leads to improved bounds on allowed perturbations. It is shown that the bounds obtained for delayed perturbations are better than those derived in the literature for nondelayed perturbations. Numerical examples are given to illustrate the results View full abstract»

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  • Mixed objective constrained stable generalised predictive control

    Page(s): 286 - 294
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (628 KB)  

    Constrained stable generalised predictive control (CSGPC) provides a means for handling constraints within the predictive control context and has guaranteed stability properties. However, to guarantee stability, an assumption concerning the feasibility of making the output reach its set-point over a finite horizon is required. If the performance objective is changed from a two-norm of the predicted errors to an infinity-norm, then the finite horizon feasibility assumption is not needed to guarantee stability. As might be expected, though, performance under an infinity-norm objective is often not as good. Here we propose an algorithm which overcomes these difficulties by mixing the two- and infinity-norm objectives View full abstract»

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  • Control system design issues for unstable linear systems with saturated inputs

    Page(s): 335 - 344
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (700 KB)  

    The paper studies stable setpoint tracking for linear open-loop unstable systems with saturated control input. A linear reference filter is introduced that allows the independent design of the prestabilising and setpoint tracking controllers in an internal model control configuration. A nonlinear reference conditioning filter is also proposed that avoids saturation in the main control loop, which remains stable thereafter. Rules of thumb giving bounds on amplitude and frequency for sinusoidal references are given that ensure stable saturated operation of the nonlinear reference filter View full abstract»

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  • Nonlinear control design for a Hammerstein model system

    Page(s): 277 - 285
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (504 KB)  

    A nonlinear control law including an inverse of memoryless nonlinear gain and a sliding mode control with an observer is proposed to stabilise the Hammerstein model system with the parameter uncertainties. The nonlinear inverse gain of a nonlinear system is provided for improving the system performance over the competing designs developed by using the linear assumptions, The sliding mode control is employed to enhance the system performances. The stability of the overall system is assured by a Lyapunov's direct method including the sliding surface and the error of the state estimate and by a stable sliding surface which is independent of the parameter uncertainties. Finally, the simulations are used to evaluate the validity of the proposed method View full abstract»

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  • Linear switching controller convergence

    Page(s): 329 - 334
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (372 KB)  

    Time-optimal control laws are known to be bang-bang, and well suited to relay or power electronic controllers. Closed-form solutions for the time-optimal control switching are obtained only for a limited set of low-order systems. The control approach in the paper is to determine the control value for a linear quadratic regulator, and then project this value onto the closest available switching control vector. The paper uses Lyapunov functions to show convergence of this projection approach, provided that the requested LQR control signal is limited in magnitude. A sequence of LQR solutions is used to cover the full-state space, and is found to give a control performance close to time optimal. When the system is open-loop unstable, convergence regions are determined from these Lyapunov functions. The region of guaranteed convergence is shown, in the limit, to be close to the full region of possible controllability. The sequence of LQR solutions with control projections is found to be a well defined control design for arbitrary order linear multi-input multi-output systems, leading to good switching control performance View full abstract»

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