In this paper, a nonlinear controller is proposed to handle some nonlinear control problems. In this scheme, the controller uses the system error, the integral of the system error, and the derivative of the system err...
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In this paper, a nonlinear controller is proposed to handle some nonlinear control problems. In this scheme, the controller uses the system error, the integral of the system error, and the derivative of the system error as its inputs, but the mapping from the inputs to the output is nonlinear. The corresponding nonlinear mapping may be specified based on the control requirement. Therefore, the proposed controller is defined as a generalized nonlinear PID controller (GNPIDC). The GNPIDC strategy is realized using neural networks. For online training of the neural network based GNPIDC, a PID gradient descent optimizing algorithm with momentum term is proposed. Then, the convergent characteristic of the algorithm is presented. Finally, simulation study of applying the neural GNPIDC strategy to a continuous-stirred-tank-reactor and a van de Vusse reactor is illustrated.
A fault detection and isolation scheme for a brushless DC motor driving a mechanical actuation system is presented. First, a detailed mathematical model of the drive is derived. The residual generator is based on pari...
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A fault detection and isolation scheme for a brushless DC motor driving a mechanical actuation system is presented. First, a detailed mathematical model of the drive is derived. The residual generator is based on parity relations while the fault isolation part is implemented by means of the transferable belief model. The problem of distinguishing between faults with the same fault signatures is addressed. It is shown that additional improvements in terms of resolution can be achieved by employing parameter estimation. The proposed FDI scheme is tested on the actual drive under various faults. The achieved performance features the highest resolution, diagnostic stability and accuracy.
Pseudo-linear neural networks (PNN) based modeling and control strategy for a class of nonlinear dynamic systems is proposed. The training of the PNN is implemented by using a recursive prediction error (RPE) algorith...
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Pseudo-linear neural networks (PNN) based modeling and control strategy for a class of nonlinear dynamic systems is proposed. The training of the PNN is implemented by using a recursive prediction error (RPE) algorithm. The control strategy is based on one-step-ahead prediction, in which a direct controller design for an affine nonlinear system and the second order based optimization for a general nonlinear system are adopted. The stability of the closed loop control system is investigated, and some sufficient conditions for the local asymptotic stability are derived. The some simulated examples showed the good results of proposed control strategies in this paper.
A new generalized predictive control (GPC) algorithm is presented, which makes use of the concept of output end-point weightings. The aim of the algorithm is a reduced computational burden with respect to the state en...
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A new generalized predictive control (GPC) algorithm is presented, which makes use of the concept of output end-point weightings. The aim of the algorithm is a reduced computational burden with respect to the state end-point weighted GPC and a relaxation of the infinite output weighting of constrained receding horizon predictive control. The new output end-point weighted GPC algorithm is developed using the polynomial approach. After a proof of nominal stability the relation of Kalman filtering and predictive control is derived. A final simulation of a benchmark example emphasizes the reduced computational burden and the enhanced stochastic tracking capability.
Many evolutionary processes, particularly some biological systems, exhibit impulsive dynamical behaviors, and they can be well described by impulsive Hopfield neural networks. This paper formulates and studies a model...
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Many evolutionary processes, particularly some biological systems, exhibit impulsive dynamical behaviors, and they can be well described by impulsive Hopfield neural networks. This paper formulates and studies a model of impulsive Hopfield neural networks. Several fundamental issues such as global exponential stability, existence, and uniqueness of equilibria of such neural networks are established.
This paper investigates the practicality problem of the equivalent control method for nonlinear neutral type variable structure control system (VSCS) with time-delays. Existence of the sliding mode for the system is g...
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This paper investigates the practicality problem of the equivalent control method for nonlinear neutral type variable structure control system (VSCS) with time-delays. Existence of the sliding mode for the system is given, and the conditions under which the sliding process has nothing to do with the external disturbance and parameter perturbation are obtained. At last, the synthetical method of the VSCS is provided and illustrated by an example of chemical process.
In this paper, the selection of H/sub /spl infin// weighting functions for general practical applications is investigated. Uncertainty weighting functions for typical low-order plants with parametric uncertainties are...
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In this paper, the selection of H/sub /spl infin// weighting functions for general practical applications is investigated. Uncertainty weighting functions for typical low-order plants with parametric uncertainties are derived. It is shown that H/sub /spl infin// weighting function for a general SISO plant can be obtained by the combination of these elementary low-order plants. Weighting functions of reflecting other system specifications in H/sub /spl infin// design are also investigated. Explicit weighting function expressions are given to address position and rate limits of an actuator, tracking error performance, overshoot, etc. Finally, the proposed methods are demonstrated by the roll-angle control design of a laboratory-scale model of a vertical take-off aircraft via simulation and real-time control.
The LMS algorithm forms the core of a number of adaptive control schemes. It has on one hand provided certain degree of robustness to these algorithms but on the other hand its convergence rate is notoriously slow. We...
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The LMS algorithm forms the core of a number of adaptive control schemes. It has on one hand provided certain degree of robustness to these algorithms but on the other hand its convergence rate is notoriously slow. We propose a method to initialize the LMS algorithm which dramatically shortens its adaptation period. We also prove that under certain condition this initialization can be computed without explicit knowledge of either the reference or noise spectrum. Although the work is illustrated using the filtered-X scheme, this idea can also be applied to other similar schemes. Two successful experiments starting from black-box identification to real-time control have verified the effectiveness of this idea.
This paper presents a new real-time architecture for motion control of industrial robots. The new control system obtained has two main advantages: first it provides a total open control architecture and the second adv...
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This paper presents a new real-time architecture for motion control of industrial robots. The new control system obtained has two main advantages: first it provides a total open control architecture and the second advantage is the simplicity and the interactivity of the platform developed. Experimental evaluation of a passivity-based control scheme shows the benefits of the architecture which is unique in the sense that open and advanced control can be combined with built-in safety logic as required in industrial applications.
Embedded controllers in safety critical applications rely on the highest of software quality standards. Testing is performed to ensure that requirements and specifications are met for all the different environments in...
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Embedded controllers in safety critical applications rely on the highest of software quality standards. Testing is performed to ensure that requirements and specifications are met for all the different environments in which the controllers operate. This paper describes the architecture of a system that uses a relational database for tracking tests, requirements, and configurations. The relational sub-schemas are integrated in a data warehouse and allow traceability from requirements to tests. A normalized representation of test cases enables the system to reason about the test topologies and is used for constructing clusters of similar tests. A representative test from each cluster can in turn provide a rapid estimation, of the software's requirement coverage and quality.
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