ASUBOPTIMAL DESIGN OF H∞DECENTRALIZED STATE FEEDBACK CONTROL FOR INTERCONNEC TED SYSTEMS

In the paper, the problem of H∞ decentralized state feedback control for largescale systems is described. An algorithm is proposed which uses the method of a feasible direction matrix. The algorithm only requires the solution of an algebraic Riccati equation (ARE) and makes the H∞norm of the closedloop transfer function matrix from disturbance inputs to controlled outputs less than a given constant which ensure the stability of the overall controlled system at each iteration. The given example shows that the convergence of the algorithm is satisfactory.

Intelligent optimal control for lead-zinc sintering process state

The intelligent integrated predictive model of synthetical permeability was established using the fuzzy classifier to combine the time sequence predictive model with the craftwork parameter predictive model. Then, the estimation model of burn-through point(BTP) based on pipe stress point(PSP) method and the predictive model of BTP were proposed. The optimal control of permeability and heat states was implemented by using the fuzzy expert controller with self-studying mechanism. The application of the intelligent control technique suppresses 17% of the fluctuation of synthetical permeability and 12% of the fluctuation of BTP, stabilizes the output and quality of sinter and settles the basis for the optimization of output and quality of sintering process.

NECESSARY CONDITIONS FOR OPTIMAL CONTROLS OF SEMILINEAR ELLIPTIC VARIATIONAL INEQUALITIES INVOLVING STATE CONSTRAINT

This paper deals with maximum principle for some optimal control problem governed by some elliptic variational inequalities. Some state constraints are discussed. The basic techniques used here are based on those in [1] and a new penalty functional defined in this paper.

OPTIMAL CONTROL OF HYPERBOLIC H-HEMIVARIATIONAL INEQUALITIES WITH STATE CONSTRAINTS

The optimal control problems of hyperbolic H-hemivariational inequalities with the state constraints and nonnomotone multivalued mapping term are considered.The optimal solutions are obtained.In addition,their approximating problems are also studied.

OPTIMAL CONTROL OF PARABOLIC VARIATIONAL INEQUALITIES WITH STATE CONSTRAINT

The optimal control problem of parabolic variational inequalities with the state constraint and nonlinear, discontinuous nonmonotone multivalued mapping term and its approximating problem are studied, which generalizes some obtained results.

SEQUENTIAL QUADRATIC PROGRAMMING METHODS FOR OPTIMAL CONTROL PROBLEMS WITH STATE CONSTRAINTS

A kind of direct methods is presented for the solution of optimal control problems with state constraints. These methods are sequential quadratic programming methods. At every iteration a quadratic programming which is obtained by quadratic approximation to Lagrangian function and linear approximations to constraints is solved to get a search direction for a merit function. The merit function is formulated by augmenting the Lagrangian function with a penalty term. A line search is carried out along the search direction to determine a step length such that the merit function is decreased. The methods presented in this paper include continuous sequential quadratic programming methods and discreate sequential quadratic programming methods.

Optimal state feedback control of brushless direct-current motor drive systems based on Lyapunov stability criterion

This paper develops a unified methodology for a real-time speed control of brushless direct-current motor drive systems in the presence of measurement noise and load torque disturbance. First, the mathematical model and hardware structure of system is established. Next, an optimal state feed back controller using the Kalman filter state estimation technique is derived. This is followed by an adaptive control algorithm to compensate for the effects of noise and disturbance. Those two algorithms working together can provide a very-high-speed regulation and dynamic response over a wide range of operating conditions. Simulated responses are presented to highlight the effectiveness of the proposed control strategy.

Necessary Conditions for Optimal Control of Diffusions with Hard Terminal State Restrictions

Necessary conditions are proved for certain problems of optimal control of diffusions where hard end constraints occur. The main results apply to several dimensional problems, where some of the state equations involve Brownian motions, but not the equations corresponding to states being hard restricted at the terminal time. The necessary conditions are stated in terms of weak variations. Two versions of necessary conditions are given, one version involving solutions of variational equations, the other one involving first order adjoint equations.

A Maximum Principle for Smooth Infinite Horizon Optimal Control Problems with State Constraints and with Terminal Constraints at Infinity

Necessary conditions for optimality are proved for smooth infinite horizon optimal control problems with unilateral state constraints (pathwise constraints) and with terminal conditions on the states at the infinite horizon. The aim of the paper is to obtain strong necessary conditions including transversality conditions at infinity, which in many cases lead to a set of candidates for optimality containing only a few elements, similar to what is the case in finite horizon problems. However, strong growth conditions are needed for the results to hold.

Iterative Solution of Mesh Constrained Optimal Control Problems with Two-Level Mesh Approximations of Parabolic State Equation

We consider a linear-quadratical optimal control problem of a system governed by parabolic equation with distributed in right-hand side control and control and state constraints. We construct a mesh approximation of this problem using different two-level approximations of the state equation, ADI and fractional steps approximations in time among others. Iterative solution methods are investigated for all constructed approximations of the optimal control problem. Their implementation can be carried out in parallel manner.

Study on optimal state estimation strategy with dual distributed controllers based on Kalman filtering

Considering dual distributed controllers, a design of optimal state estimation strategy is studied for the wireless sensor and actuator network(WSAN). In particular, the optimal linear quadratic(LQ) control strategy with estimated plant state is formulated as a non-cooperative game with network-induced delays. Then, using the Kalman filter approach, an optimal estimation of the plant state is obtained based on the information fusion of the distributed controllers. Finally, an optimal state estimation strategy is derived as a linear function of the current estimated plant state and the last control strategy of multiple controllers. The effectiveness of the proposed closed-loop control strategy is verified by the simulation experiments.

Comparison of differential evolution, particle swarm optimization,quantum-behaved particle swarm optimization, and quantum evolutionary algorithm for preparation of quantum states

Four intelligent optimization algorithms are compared by searching for control pulses to achieve the preparation of target quantum states for closed and open quantum systems, which include differential evolution(DE), particle swarm optimization(PSO), quantum-behaved particle swarm optimization(QPSO), and quantum evolutionary algorithm(QEA).We compare their control performance and point out their differences. By sampling and learning for uncertain quantum systems, the robustness of control pulses found by these four algorithms is also demonstrated and compared. The resulting research shows that the QPSO nearly outperforms the other three algorithms for all the performance criteria considered.This conclusion provides an important reference for solving complex quantum control problems by optimization algorithms and makes the QPSO be a powerful optimization tool.

SECOND-ORDER OPTIMALITY CONDITIONS FOR OPTIMAL CONTROL PROBLEMS GOVERNED BY 3-DIMENSIONAL NEVIER-STOKES EQUATIONS

This article is concerned with second-order necessary and sufficient optimality conditions for optimal control problems governed by 3-dimensional Navier-Stokes equations. The periodic state constraint is considered.

Output-Feedback Based Simplified Optimized Backstepping Control for Strict-Feedback Systems with Input and State Constraints

In this paper,an adaptive neural-network(NN)output feedback optimal control problem is studied for a class of strict-feedback nonlinear systems with unknown internal dynamics,input saturation and state constraints.Neural networks are used to approximate unknown internal dynamics and an adaptive NN state observer is developed to estimate immeasurable states.Under the framework of the backstepping design,by employing the actor-critic architecture and constructing the tan-type Barrier Lyapunov function(BLF),the virtual and actual optimal controllers are developed.In order to accomplish optimal control effectively,a simplified reinforcement learning(RL)algorithm is designed by deriving the updating laws from the negative gradient of a simple positive function,instead of employing existing optimal control methods.In addition,to ensure that all the signals in the closed-loop system are bounded and the output can follow the reference signal within a bounded error,all state variables are confined within their compact sets all times.Finally,a simulation example is given to illustrate the effectiveness of the proposed control strategy.

Predictive Functional Controller with a Similar Proportional Integral Optimal Regulator Structure:Comparison with Traditional Predic-tive Functional Controller and Application to Heavy Oil Coking Equipment

By extending the system's state variables,a novel predictive functional controller has been developed.The structure of this controller is similar to that of classical proportional integral(PI)optimal controller and in-cludes a control block that can perform a feed-forward control of future P-step set points.It considers both the state variables and the output errors in its cost function,which results in enhanced control performance compared with traditional state space predictive functional control(TSSPFC)methods that consider only the predictive output er-rors.The predictive functional controller(PFC)has been compared with TSSPFC in terms of tracking ability,dis-turbance rejection,and also based on its application to heavy oil coking equipment.The results obtained show the effectiveness of the controller.

Mathematical Analysis of Nipah Virus Infections Using Optimal Control Theory

The optimal use of intervention strategies to mitigate the spread of Nipah Virus (NiV) using optimal control technique is studied in this paper. First of all we formulate a dynamic model of NiV infections with variable size population and two control strategies where creating awareness and treatment are considered as controls. We intend to find the optimal combination of these two control strategies that will minimize the cost of the two control measures and as a result the number of infectious individuals will decrease. We establish the existence for the optimal controls and Pontryagin’s maximum principle is used to characterize the optimal controls. The numerical simulation suggests that optimal control technique is much more effective to minimize the infected individuals and the corresponding cost of the two controls. It is also monitored that in the case of high contact rate, controls have to work for longer period of time to get the desired result. Numerical simulation reveals that the spread of Nipah virus can be controlled effectively if we apply control strategy at early stage.

Asymmetric time-varying integral barrier Lyapunov function based adaptive optimal control for nonlinear systems with dynamic state constraints

This paper investigates the issue of adaptive optimal tracking control for nonlinear systems with dynamic state constraints.An asymmetric time-varying integral barrier Lyapunov function(ATIBLF)based integral reinforcement learning(IRL)control algorithm with an actor–critic structure is first proposed.The ATIBLF items are appropriately arranged in every step of the optimized backstepping control design to ensure that the dynamic full-state constraints are never violated.Thus,optimal virtual/actual control in every backstepping subsystem is decomposed with ATIBLF items and also with an adaptive optimized item.Meanwhile,neural networks are used to approximate the gradient value functions.According to the Lyapunov stability theorem,the boundedness of all signals of the closed-loop system is proved,and the proposed control scheme ensures that the system states are within predefined compact sets.Finally,the effectiveness of the proposed control approach is validated by simulations.

OPTIMAL CONTROL PROBLEM FOR PARABOLIC VARIATIONAL INEQUALITIES

This paper deals with the optimal control problems of systems governed by a parabolic variational inequality coupled with a semilinear parabolic differential equations. The maximum principle and some kind of approximate controllability are studied.

Design of Wiener-Hopf optimal controller and its application in terrain following system

state space algorithms are proposed for the three degree of freedom (3DOF) optimal Wiener Hopf controller for a class of systems with time delay feedback sensors and employed in the design of a terrain following control system, with the influence caused by the delay of baric altimeter successfully eliminated.

Multi-objective optimization based optimal setting control for industrial double-stream alumina digestion process

The operation variables,including feed rate of ore slurry,caustic solution and live steams in the double-stream alumina digestion process,determine the product quality,process costs and the environment pollution.Previously,they were set by the technical workers according to the offline analysis results and an empirical formula,which leads to unstable process indices and high consumption frequently.So,a multi-objective optimization model is built to maintain the balance between resource consumptions and process indices by taking technical indices and energy efficiency as objectives,where the key technical indices are predicted based on the digestion kinetics of diaspore.A multi-objective state transition algorithm(MOSTA)is improved to solve the problem,in which a self-adaptive strategy is applied to dynamically adjust the operator factors of the MOSTA and dynamic infeasible threshold is used to handle constraints to enhance searching efficiency and ability of the algorithm.Then a rule based strategy is designed to make the final decision from the Pareto frontiers.The method is integrated into an optimal control system for the industrial digestion process and tested in the actual production.Results show that the proposed method can achieve the technical target while reducing the energy consumption.