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.     

FBFN-based adaptive repetitive control of nonlinearly parameterized systems

An adaptive repetitive control scheme is presented for a class of nonlinearly parameterized systems based on the fuzzy basis function network （FBFN）. The parameters of the fuzzy rules are tuned with adaptive schemes. To attenuate chattering effectively, the discontinuous control term is approximated by an adaptive PI control structure. The bound of the discontinuous control term is assumed to be unknown and estimated by an adaptive mechanism. Based on the Lyapunov stability theory, an adaptive repetitive control law is proposed to guarantee the closed-loop stability and the tracking performance. By means of FBFNs, which avoid the nonlinear parameterization from entering into the adaptive repetitive control, the controller singularity problem is solved. The proposed approach does not require an exact structure of the system dynamics, and the proposed controller is utilized to control a model of permanent-magnet linear synchronous motor subject to significant disturbances and parameter uncertainties. The simulation results demonstrate the effectiveness of the proposed method.     

面向飞推一体化的飞行器建模方法

The airbreathing hypersonic vehicle (ABHV) is a typical vehicle which has multidimensional nonlinear restrictions and intricate coupling. Due to the strong coupling interactions and multidimensional nonlinear restrictionists,the flight powered by scramjet is a critical phase of reference trajectory, guidance and control designing. Therefore, the development of a reasonable ABHV model is inevitable. The existing models of integrated aircraft scramjet are difficult for control design directly，and also can not be used with a numeric model together. To achieve the goal of exhuming the potential applications and expanding flight feasibility of ABHV,and improving the efficiency and robustness of ABHV from the overall level, based on the working mechanism and numerical model of scramjet, the performance and protected model of scramjet is researched, and the control oriented model of ABHV is developed.     

OPTIMAL MAINTENANCE AND REPLACEMENT OF EXTRACTION MACHINERY

This paper considers a problem of optimal preventive maintenance and replacement schedule of equipment devoted to extracting resources from known deposits. Typical examples are oil drills, mine shovels, etc. At most one replacement of the existing machinery by a new one is allowed. The problem is formulated as an optimal control problem subject to the state constraint that the remaining deposit at any given time is nonnegative. We show that the optimal preventive maintenance, production rates, and the replacement and salvage times of the existing machinery and the new one, if required, can be obtained by solving sequentially a series of free-end-point optimal control problems. Moreover, an algorithm based on this result is developed and used to solve two illustrative examples.     

Minimum eigenvalue based adaptive fault compensation for hypersonic vehicles

The attitude tracking control problem is addressed for hypersonic vehicles under actuator faults that may cause an uncertain time-varying control gain matrix. An adaptive compensation scheme is developed to ensure system stability and asymptotic tracking properties, including a kinematic control signal and a dynamic control signal. To deal with the uncertainties of the control gain matrix, a new positive definite one is constructed. The minimum eigenvalue of such a new control gain matrix is est...     

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.     

STABILIZATION OF A KIND OF PREY-PREDATOR MODEL WITH HOLLING FUNCTIONAL RESPONSE

The stabilization problem of a kind of prey-predator model with Holling functional responseis investigated.By approximate linearization approach,a feedback control law stabilizing the closed-loop system is obtained.On the other hand.by exact linearization approach,a suitable change ofcoordinates in the state space and a feedback control law render the complex nonlinear system to bea linear controllable one such that the positive equilibrium point of the closed-loop system is globallyasymptotically stable.     

Research of robust adaptive trajectory linearization control based on T-S fuzzy system

A robust adaptive trajectory linearization control （RATLC） algorithm for a class of nonlinear systems with uncertainty and disturbance based on the T-S fuzzy system is presented. The unknown disturbance and uncertainty are estimated by the T-S fuzzy system, and a robust adaptive control law is designed by the Lyapunov theory. Irrespective of whether the dimensions of the system and the rules of the fuzzy system are large or small, there is only one parameter adjusting on line. Uniformly ultimately boundedness of all signals of the composite closed-loop system are proved by theory analysis. Finally, a numerical example is studied based on the proposed method. The simulation results demonstrate the effectiveness and robustness of the control scheme.     

Adaptive time-space resource and waveform control for collocated MIMO radar with simultaneous multi-beam

Collocated multiple input multiple output(MIMO)radar,which has agile multi-beam working mode,can offer enhanced multiple targets tracking(MTT)ability.In detail,it can illuminate different targets simultaneously with multi-beam or one wide beam among multi-beam,providing greater degree of freedom in system resource control.An adaptive time-space resource and waveform control optimization model for the collocated MIMO radar with simultaneous multi-beam is proposed in this paper.The aim of the proposed scheme is to improve the overall tracking accuracy and meanwhile minimize the resource consumption under the guarantee of effective targets detection.A resource and waveform control algorithm which integrates the genetic algorithm(GA)is proposed to solve the optimization problem.The optimal transmitting waveform parameters,system sampling period,sub-array number,binary radar tracking parameterχ_i(t_k),transmitting energy and multi-beam direction vector combination are chosen adaptively,where the first one realizes the waveform control and the latter five realize the timespace resource allocation.Simulation results demonstrate the effectiveness of the proposed control method.     

Stochastic stabilization analysis of networked control systems

Considering the stochastic delay problems existing in networked control systems, a new control mode is proposed for networked control systems whose delay is longer than a sampling period. Under the control mode, the mathematical model of such a system is established. A stochastic stabilization condition for the system is given. The maximum delay can be derived from the stabilization condition.     

Open-loop and closed-loop Dα-type iterative learning control for fractional-order linear multi-agent systems with state-delays

This study focuses on implementing consensus tracking using both open-loop and closed-loop Dα-type iterative learning control(ILC)schemes,for fractional-order multi-agent systems(FOMASs)with state-delays.The desired trajectory is constructed by introducing a virtual leader,and the fixed communication topology is considered and only a subset of followers can access the desired trajectory.For each control scheme,one controller is designed for one agent individually.According to the tracking error between the agent and the virtual leader,and the tracking errors between the agent and neighboring agents during the last iteration(for open-loop scheme)or the current running(for closed-loop scheme),each controller continuously corrects the last control law by a combination of communication weights in the topology to obtain the ideal control law.Through the rigorous analysis,sufficient conditions for both control schemes are established to ensure that all agents can achieve the asymptotically consistent output along the iteration axis within a finite-time interval.Sufficient numerical simulation results demonstrate the effectiveness of the control schemes,and provide some meaningful comparison results.     

Cooperative game theory-based steering law design of a CMG system

Spacecraft require a large-angle manoeuvre when performing agile manoeuvring tasks, therefore a control moment gyroscope(CMG) is employed to provide a strong moment.However, the control of the CMG system easily falls into singularity, which renders the actuator unable to output the required moment. To solve the singularity problem of CMGs, the control law design of a CMG system based on a cooperative game is proposed. First, the cooperative game model is constructed according to the quadratic pr...     

Trajectory tracking theory of quantum systems

The orbit tracking problem of a free-evolutionary target system in closed quantum systems is solved by changing it into the state transferring problem with the help of unitary transformation.The control law designed by the Lyapunov stability theorem employs a carefully constructed virtual mechanical quantity P to ensure the system convergence.The virtual mechanical quantity P is chosen by two approaches according to the forms of limit set,where P = —pf is suitable for regular limit set and a new different P is constructed for irregular one.The proposed tracking control theory is demonstrated on a four-level quantum system by means of numerical simulation experiments.     

Dynamics and nonlinear control of space electromagnetic docking

Space electromagnetic docking technology,free of propellant and plume contamination,offers continuous,reversible and synchronous controllability,which is widely applied in the future routine on-orbit servicing missions.Due to the inherent nonlinearities,couplings and uncertainties of an electromagnetic force model,the dynamics and control problems of them are difficult.A new modeling approach for relative motion dynamics with intersatellite force is proposed.To resolve these control problems better,a novel nonlinear control method for soft space electromagnetic docking is proposed,which combines merits of artificial potential function method,Lyapunov theory and extended state observer.In addition,the angular momentum management problem of space electromagnetic docking and approaches of handling it by exploiting the Earth’s magnetic torque are investigated.Finally,nonlinear simulation results demonstrate the feasibility of the dynamic model and the novel nonlinear control method.     

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...     

Novel backstepping design for blended aero and reaction-jet missile autopilot

The advanced missile uses blended control of nero-fin and reaction-jet to improve missile maneuverability. The blended control design, which is multi-inputs and multi-outputs （MIMO）, severe nonlinear, and model uncertain, is much more complex than conventional nero-fin control. A novel nonlinear backstepping control approach is proposed to design the blended autopilot. Missile model is reformed to a new one by state reconstruction technique so that it is easy to be handled by the backstepping method. Then a Lyapunov function is chosen to avoid oscillation caused in normal backstepping way when control parameters are mismatched. In distribution of both inputs, optimal energy logic is proposed. In addition, a fuzzy cerebellar model articulation controller （FCMAC） neural network is used to guarantee controller robustness to uncertainties. Finally, simulation results demonstrate the efficiency and advantages of the proposed method.     

A New Buck-Based Configuration AC Chopper Voltage Conditioner

1. INTaODUCTIONThe AC voltage conditioner is used as one of power electronic devices to control and output AC voltage forpower ranges from a few watts up to megawatts. In the field Of laxge powert this type of device is developed veryquickly. traditional techniques are: 1) Phase-angle control line-commutated voltage conditioner and integralcycle control of thyristors have been traditionally used. Its main disadVantage is retardation of firing angle, whichcauses a lagging power factor at…     

Dynamic pricing under temperature control for perishable foods

Consumers pay more and more attention to the quality of perishable roods, which is mainly affected by storage temperature. This paper presents a dynamic pricing model for perishable foods under temperature control. To maximize the total profit, the optimal price and storage temperature are obtained using Pontryagin＇s maximum principle. A static pricing model is provided to compare with the dynamic one. It is shown by a numerical example that the dynamic policy can make more revenue than the static one. Moreover, the managerial implications are analyzed and the effectiveness of the proposed method is demonstrated.     

Mixed H2／H∞ Optimal Guaranteed Cost Control of Uncertain Linear Systems

The mixed H2/H∞ guaranteed cost control problem via state feedback control laws is considered in this paper for linear systems with norm-bounded parameter uncertainty. Based on the linear matrix inequality (LMI) approach, sufficient conditions are derived for the existence of guaranteed cost controllers whihc guarantee not only a prespecified H∞ disturbance attenuation level on one controlled output for all admissible parameter uncertainties, but also the worst-case H2 performance index on the other controlled output to be no more than a specified bound. Furthermore, a convex optimization problem is formulated to design an optimal H2/H∞ guaranteed cost controller.     

High-order sliding mode attitude controller design for reentry flight

A novel high-order sliding mode control strategy is proposed for the attitude control problem of reentry vehicles in the presence of parametric uncertainties and external disturbances, which results in the robust and accurate tracking of the aerodynamic angle commands with the finite time convergence. The proposed control strategy is developed on the basis of integral sliding mode philosophy, which combines conventional sliding mode control and a linear quadratic regulator over a finite time interval with a free-final-state and allows the finite-time establishment of a high-order sliding mode. Firstly, a second-order sliding mode attitude controller is designed in the proposed high-order siding mode control framework. Then, to address the control chattering problem, a virtual control is introduced in the control design and hence a third-order sliding mode attitude controller is developed, leading to the chattering reduction as well as the control accuracy improvement. Finally, simulation examples are given to illustrate the effectiveness of the theoretical results.