ON SUFFICIENT AND NECESSARY OF EXISTENCE FOR A CLASS OF SINGULAR OPTIMAL STOCHASTIC CONTROL

We study a class of discounted models of singular stochastic control. In this kind of models, not only the structure of cost function has been extended to some general type, but also the state can be represented as the solution of a class of stochastic differential equations with nonlinear drift and diffusion term. By the various methods of stochastic analysis, we derive the sufllcient and necessary conditions of the existence of optimal control.     

Robust output regulation problem with prescribed performance for nonlinear strict feedback systems

This paper investigates the problem of robust output regulation control with prospected transient property for strict feedback systems. By employing the internal model principle,the robust output regulation problem with a prospected property can be transformed to a robust stabilization problem with a new output constraint. Then, by constructing the speed function and adopting barrier Lyapunov function technique, the dynamic feedback controller can be designed not only to drive error output of th...     

Intelligent coordinated control of power-plant main steam pressure and power output

An intelligent coordinated control strategy has been proposed and successfully applied to a 300MW boiler-turbine unit i. e. Unit 1 of Yuanbaoshan power plant in China. Load following operation of coal-fired boiler-turbine unit in the power plant leads to changes in operating points which result in nonlinear variations of the plant variables and parameters. For the variation of operating condition and slowly varying dynamics, an intelligent control scheme has been developed by combining fuzzy self-tuning with adaptive control and auto-tuning techniques. As there exist strong couplings between control loops of main steam pressure and power output in the unit, a new design for static decoupler aimed at decoupling for setpoints and unmeasured pulverized coal disturbance of the system at the same time is presented. Satisfactory industrial application results show that such a control system has enhanced adaptability and robustness to the complex process, and better control performance and high economic benefi     

Adaptive Robust Dead-Zone Compensation Control of Electro-Hydraulic Servo Systems with Load Disturbance Rejection

A backstepping method based adaptive robust dead-zone compensation controller is proposed for the electro-hydraulic servo systems(EHSSs) with unknown dead-zone and uncertain system parameters.Variable load is seen as a sum of a constant and a variable part.The constant part is regarded as a parameter of the system to be estimated real time.The variable part together with the friction are seen as disturbance so that a robust term in the controller can be adopted to reject them.Compared with the traditional dead-zone compensation method,a dead-zone compensator is incorporated in the EHSS without constructing a dead-zone inverse.Combining backstepping method,an adaptive robust controller(ARC) with dead-zone compensation is formed.An easy-to-use ARC tuning method is also proposed after a further analysis of the ARC structure.Simulations show that the proposed method has a splendid tracking performance,all the uncertain parameters can be estimated,and the disturbance has been rejected while the dead-zone term is well estimated and compensated.     

Model algorithm control using neural networks for input delayed nonlinear control system

The performance of the model algorithm control method is partially based on the accuracy of the system’s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to "learn"the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.     

MATTER-ELEMENT MODELING OF PARALLEL STRUCTURE AND APPLICATION ABOUT EXTENSION PID CONTROL SYSTEM

This article describes in detail a new method via the extension predictable algorithm of the matter-element model of parallel structure tuning the parameters of the extension PID controller. In comparison with fuzzy and extension PID controllers, the proposed extension PID predictable controller shows higher control gains when system states are away from equilibrium, and retains a lower profile of control signals at the same time. Consequently, better control performance is achieved. Through the proposed tuning formula, the weighting factors of an extension-logic predictable controller can be systematically selected according to the control plant. An experimental example through industrial field data and site engineers＇ experience demonstrates the superior performance of the proposed controller over the fuzzy controller.     

Novel active fault-tolerant control scheme and its application to a double inverted pendulum system

On the basis of the gain-scheduled H∞ design strategy, a novel active fault-tolerant control scheme is proposed. Under the assumption that the effects of faults on the state-space matrices of systems can be of affine parameter dependence, a reconfigurable robust H∞ linear parameter varying controller is developed. The designed controller is a function of the fault effect factors that can be derived online by using a well-trained neural network. To demonstrate the effectiveness of the proposed method, a double inverted pendulum system, with a fault in the motor tachometer loop, is considered.     

Adaptive stabilization for cascade nonlinear systems

An adaptive controller of full state feedback for certain cascade nonlinear systems achieving input-to-state stability with respect to unknown bounded disturbance is designed using backstepping and control Lyapunov function (CLF) techniques. We show that unknown bounded disturbance can be estimated by update laws, which requires less information on unknown disturbance, as a part of stabilizing control. The design method achieves the desired property: global robust stability. Our contribution is illustrated with the example of a disturbed pendulum.     

Stabilization of discrete nonlinear systems based on control Lyapunov functions

The stabilization of discrete nonlinear systems is studied. Based on control Lyapunov functions, a sufficient and necessary condition for a quadratic function to be a control Lyapunov function is given. From this condition, a continuous state feedback law is constructed explicitly. It can globally asymptotically stabilize the equilibrium of the closed-loop system. A simulation example shows the effectiveness of the proposed method.     

ON LIMITATIONS OF THE SAMPLED—DATA FEEDBACK FOR NONPARAMETRIC DYNAMICAL SYSTEMS

In this paper, we study a basic class of first order sampled-data control systems with unknown nonlinear structure and with sampling rate not necessarily fast enough, aiming at understanding the capability and limitations of the sampled-data feedback. We show that if the unknown nonlinear function has a linear growth rate with its "slope" (denoted by L) being a measure of the "size" of uncertainty, then the sampling rate should not exceed 1/L multiplied by a constant (≈ 7.53) for the system to be globally stabilizable by the sampled-data feedback. If, however, the unknown nonlinear function has a growth rate faster than linear, and if the system is disturbed by noises modeled as the standard Brownian motion, then an example is given, showing that the corresponding sampled-data system is not stabilizable by the sampled-data feedback in general, no matter how fast the sampling rate is.     

Stability analysis and design of time-delay uncertain systems using robust reliability method

A robust reliability method for stability analysis and reliability-based stabilization of time-delay dynamic systems with uncertain but bounded parameters is presented by treating the uncertain parameters as interval variables.The performance function used for robust reliability analysis is defined by a delayindependent stability criterion.The design of robust controllers is carried out by solving a reliability-based optimization problem in which the control cost satisfying design requirements is minimized.This kind of treatment makes it possible to achieve a balance between the reliability and control cost in the design of controller when uncertainties must be taken into account.By the method,a robust reliability measure of the degree of stability of a time-delay uncertain system can be provided,and the maximum robustness bounds of uncertain parameters such that the time-delay system to be stable can be obtained.All the procedures are based on the linear matrix inequality approach and therefore can be carried out conveniently.The effectiveness and feasibility of the proposed method are demonstrated with two practical examples.It is shown by numerical simulations and comparison that it is meaningful to take the robust reliability into account in the control design of uncertain systems.     

Robust Feedback Set Stabilization of Logic Networks with State-Dependent Uncertain Switching and Control Constraints

This study investigates the robust feedback set stabilization of switched logic control networks(SLCNs) with state-dependent uncertain switching and control constraints. First, based on the properties of the semi-tensor product of matrices and the vector representation of logic, an SLCN with state-dependent uncertain switching and control constraints is expressed in algebraic form. Second, an input transformation and a switching model are constructed to transfer the original SLCN into one with a...     

Decentralized adaptive fuzzy control of time-delayed interconnected systems with unknown backlash-like hysteresis

The problem of decentralized adaptive fuzzy control for a class of time-delayed interconnected nonlinear systems with unknown backlash-like hystersis is discussed. On the basis of the principle of variable structure control （VSC） and by using the fuzzy systems with linear adjustable parameters that are used to approximate plant unknown functions, a novel decentralized adaptive fuzzy control strategy with a supervisory controller is developed. A general method, which is modeled the backlash-like hysteresis, is proposed and removes the assumption that the boundedness of disturbance, and the slope of the backlash-like hystersis are known constants. Furthermore, the interconnection term is supposed to be pth-order polynomial in time-delayed states. In addition, the plant dynamic uncertainty and modeling errors are adaptively compensated by adjusting the parameters and gains on-line for each subsystems. By theoretical analysis, it is shown that the closed-loop fuzzy control systems are globally stable, with tracking error converging to zero. Simulation results demonstrate the effectiveness of the approach.     

Concurrent Production Engineering System for buffer size and flexible transfer line layout design

In this paper we propose a COncurrent Production Engineering System （COPES） for the flexible transfer line （FTL） layout design in a restricted area. COPES first determines the buffer size in front of the bay of each machine tool in the FTL and then initializes a computer aided design （CAD） system to draw the FTL in a restricted area. We develop a set of modules systems which have been integrated into a single framework, in accordance with the practice of concurrent engineering. Concurrent engineering involves the cooperation of these activities. It＇s expected that the developed COPES can improve the cooperation between production engineers＇ and the plant designer. This can be done by enabling the production engineers＇ to make better decision regarding FTL buffer size.     

ESTIMATION OF A KIND OF JUMP REGRESSION FUNCTION

Research on jump regression func(?)ons has not been adequate yet According to theinformation about the number of jumps,their positions and jump magnitudes,jump regressionfunctions can be classified into eight types.This paper deals especially with the secondjump regression function.First of all,a concept of trimmed spline estimate is proposed andwith it an L~2-consistent estimate of the smoothing part of the jump regression function isobtained.This along with the L~2-consistent estimate of jump magnitude constitutes an estimateof the second jump regression function.This paper discusses also the case that the jumppositions have some indeterminacy.A new criterion is suggested and its unique solutionderived.In the end,a few numerical results are given.     

Extended optimal guidance law with impact angle and acceleration constriants

The extended optima straints of miss distance and Schwartz inequality. To reduce guidance law with terminal conmpact angle is derived by the terminal acceleration and eliminate gravity disturbance absolutely, the object function, which designs the weight of control command to be the power function of time-to-go＇s reciprocal, is given. And the gravity is considered when building the state equation. Based on the parsing express of the guidance command change with varying time and adjoint system analysis method, the command characteristics and the non-dimensional miss distance of the guidance law are analyzed, a design principle of guidance order coefficients is discussed. Finally, based on the requirement of engineering, the method to calculate the guidance condition and maximal required acceleration of the guidance law is given. The simulation demonstrates that not only the guidance law can satisfy the terminal position and impact angle constraints, but also the terminal acceleration can be converged toward zero, which will support a good situation for the terminal angle of attacking control.     

OPTIMAL ADAPTIVE CONTROL WITH CONSTRAINT FOR ARMAX MODEL

For the ARMAX system with unknown coefficients the optimal adaptive control isdesigned so that the following requirements are met simultaneously:1)the transfer function from areference signal to the system output in the closed loop equals a prescribed rational function;2)under the constraint mentioned in 1)a quadratic loss function is minimized;3)the parameterestimate is strongly consistent.     

M／GI／1 MODELS WITH NEGATIVE ARRIVALS TO BE SERVED

On the basic of a type of practical examples we set up a new queueing model with negative customers. By the use of “Supplemental Variables method” and “State transfer analysis”, we get the generating function with negative powers of queue length and the waiting time expressions.     

Application of neural networks for permanent magnet synchronous motor direct torque control

Neural networks require a lot of training to understand the model of a plant or a process. Issues such as learning speed, stability, and weight convergence remain as areas of research and comparison of many training algorithms. The application of neural networks to control interior permanent magnet synchronous motor using direct torque control （DTC） is discussed. A neural network is used to emulate the state selector of the DTC. The neural networks used are the back-propagation and radial basis function. To reduce the training patterns and increase the execution speed of the training process, the inputs of switching table are converted to digital signals, i.e., one bit represent the flux error, one bit the torque error, and three bits the region of stator flux. Computer simulations of the motor and neural-network system using the two approaches are presented and compared. Discussions about the back-propagation and radial basis function as the most promising training techniques are presented, giving its advantages and disadvantages. The system using back-propagation and radial basis function networks controller has quick parallel speed and high torque response.     

Interaction model of artificial fish in virtual environment

Conventional artificial fish has some shortages on the interaction with environment, other fish, and the animator. This article proposes a multi-tier interaction control model of artificial fish, realizes the interaction model through integration of virtual reality technology and Markov sequence, and provides a virtual marine world to describe the interaction between artificial fish and the virtual environment and the interaction between the artificial fish and the animator. Simulation results show that the interaction model owns not only the basic characteristics of virtual biology, but also has high trueness interaction function.