Best Tracking Performance under Plant Uncertainty and Control Energy Constraint

This paper has investigated best tracking performance for linear feedback control systems in the case that plant uncertainty and control effort need to be considered simultaneously. Firstly, an average integral square criterion of the tracking error and the plant input energy over a class of additive model errors is defined. Then, utilizing spectral factorization to minimize the performance index, we obtain an optimal controller design method, and furthermore study optimal tracking performance under plant uncertainty and control energy constraint. The results can be used to evaluate optimal average tracking performance and control energy in designing practical control systems.

Complexity scalable control algorithm for intra coding in H.264 under energy constraints

In this paper, the complexity of intra coding is first analyzed so as to achieve a weight of complexity measurement for each intra mode. Then, a new complexity scalable control algorithm for intra coding in H. 264 is proposed, based on the rearrangement of the order of candidate modes and an efficient complexity allocation and control （CAAC） scheme at the macroblock （MB） level. The candidate modes of each MB are rearranged according to the local-edge information. Experimental results show that our proposed algorithm can make an appropriate cut-off point of the candidate modes sequence adaptively according to the current energy condition of a mobile device, so as to adjust the complexity at any level while maximizing the video quality, which can prolong the operational lifetime of the battery with minimum degradation in video quality.

Formation control for networked multiagent systems with a minimum energy constraint

Minimum-energy formation achievement problems for networked multiagent systems are investigated,where information networks with leaderless and leader-follower structures are respectively addressed and information networks are randomly switching.The critical feature of this work is that the energy constraint is minimum in the sense of the linear matrix inequality,but limited-budget control and guaranteed-cost control cannot realize a minimum-energy formation.Firstly,the leaderless minimum-energy formation control problem is converted into an asymp-totic stability one via a nonsingular transformation and state space decomposition,and based on linear matrix inequality techniques,sufficient conditions for analysis and design of leaderless minimum-energy formation achievement are proposed,respectively,which can be solved by the generalized eigenvalue method.Then,main results of minimum-energy formation achievement of leaderless networked multiagent systems are extended leader-follower networked multiagent systems,where the asymmetric property of the leader-follower information network is well dealt with by two nonsingular transformations.Finally,two simulation examples are shown to verify the main results for minimum-energy formation achievements of leaderless and leader-follower networked multiagent systems,respectively.

Virtual Synchronous Generator Adaptive Control of Energy Storage Power Station Based on Physical Constraints

The virtual synchronous generator(VSG)can simulate synchronous machine’s operation mechanism in the control link of an energy storage converter,so that an electrochemical energy storage power station has the ability to actively support the power grid,from passive regulation to active support.Since energy storage is an important physical basis for realizing the inertia and damping characteristics in VSG control,energy storage constraints of the physical characteristics on the system control parameters are analyzed to provide a basis for the system parameter tuning.In a classic VSG control,its virtual inertia and damping coefficient remain unchanged.When the grid load changes greatly,the constant control strategy most likely result in the grid frequency deviation beyond the stable operation standard limitations.To solve this problem,a comprehensive control strategy considering electrified wire netting demand and energy storage unit state of charge(SOC)is proposed,and an adaptive optimization method of VSG parameters under different SOC is given.The energy storage battery can maintain a safe working state at any time and be smoothly disconnected,which can effectively improve the output frequency performance of energy storage system.Simulation results further demonstrated the effectiveness of the VSG control theoretical analysis.

Automatic Anterior Lamina Cribrosa Surface Depth Measurement Based on Active Contour and Energy Constraint

The lamina cribrosa is affected by intraocular pressure, which is the major risk of glaucoma. However, the capability to evaluate the lamina cribrosa in vivo has been limited until recently due to poor image quality and the posterior laminar displacement of glaucomatous eyes. In this study, we propose an automatic method to measure the anterior lamina cribrosa surface depth （ALCSD）, including a method for detecting Bruch＇s membrane opening （BMO） based on k-means and region-based active contour. An anterior lamina cribrosa surface segmentation method based on energy constraint is also proposed. In BMO detection, we initialize the Chan-Vese active contour model by using the segmentation map of the k-means cluster. In the segmentation of anterior lamina cribrosa surface, we utilize the energy function in each A-scan to establish a set of candidates. The points in the set that fail to meet the constraints are removed. Finally, we use the B-spline fitting method to obtain the results. The proposed automatic method can model the posterior laminar displacement by measuring the ALCSD. This method achieves a mean error of 45.34 μm in BMO detection. The mean errors of the anterior lamina cribrosa surface are 94.1% within five pixels and 76.1% within three pixels.

Lower bound of tracking performance with finite control energy and channel energy constraint

This paper studies the tracking performance of the single-input single-output （SISO）, finite dimensional, linear and time-invariant （LTI） system over an additive white Gaussian noise （AWGN） channel with finite control energy and channel input energy constraint. A new performance index is proposed which is minimized over all stabilizing two-degree-of-freedom controllers. The explicit expressions of the lower bound of the tracking performance and the minimum of signal-to-noise required are obtained. The results show that the lower bound is correlated to the unstable pole, nonminimum phase zero and the channel scaling factor. Finally, one example is given to validate the conclusions by adopting the special inner-outer factorization.

Energy Conditions and Constraints on the Generalized Non-Local Gravity Model

We study and derive the energy conditions in generalized non-local gravity, which is the modified theory of general relativity obtained by adding a term m2n-2R□-nRto the Einstein-Hilbert action. Moreover, to obtain some insight on the meaning of the energy conditions, we illustrate the evolutions of four energy conditions with the model parameter ε for different n. By analysis we give the constraints on the model parameters ε.

Gini Coefficient-based Task Allocation for Multi-robot Systems With Limited Energy Resources

Nowadays, robots generally have a variety of capabilities, which often form a coalition replacing human to work in dangerous environment, such as rescue, exploration, etc. In these operating conditions, the energy supply of robots usually cannot be guaranteed. If the energy resources of some robots are consumed too fast, the number of the future tasks of the coalition will be affected. This paper will develop a novel task allocation method based on Gini coefficient to make full use of limited energy resources of multi-robot system to maximize the number of tasks. At the same time, considering resources consumption,we incorporate the market-based allocation mechanism into our Gini coefficient-based method and propose a hybrid method,which can flexibly optimize the task completion number and the resource consumption according to the application contexts.Experiments show that the multi-robot system with limited energy resources can accomplish more tasks by the proposed Gini coefficient-based method, and the hybrid method can be dynamically adaptive to changes of the work environment and realize the dual optimization goals.

Novel radar dwell scheduling algorithm based on pulse interleaving

The problem of scheduling radar dwells in multifunction phased array radar systems is addressed. A novel dwell scheduling algorithm is proposed. The whole scheduling process is based on an online pulse interleaving technique. It takes the system timing and energy constraints into account. In order to adapt the dynamic task load, the algorithm considers both the priorities and deadlines of tasks. The simulation results demonstrate that compared with the conventional adaptive dwell scheduling algorithm, the proposed one can improve the task drop rate and system resource utility effectively.

Dwell scheduling algorithm for multifunction phased array radars based on the scheduling gain

A real-time dwell scheduling model, which takes the time and energy constraints into account is founded from the viewpoint of scheduling gain. Scheduling design is turned into a nonlinear programming procedure. The real-time dwell scheduling algorithm based on the scheduling gain is presented with the help of two heuristic rules. The simulation results demonstrate that compared with the conventional adaptive scheduling method, the algorithm proposed not only increases the scheduling gain and the time utility but also decreases the task drop rate.

MIMO radar dwell scheduling based on novel pulse interleaving technique

According to the signal processing characteristic of MIMO radars,an adaptive dwell scheduling algorithm is proposed.It is based on a novel pulse interleaving technique,which makes full use of transmitting,waiting and receiving durations of radar dwells.The utilization of transmitting duration is unique for MIMO radars and is realized through transmitting duration overlapping.Simulation results show that,compared with the conventional scheduling algorithm,the scheduling performance of MIMO radars can be improved effectively by the proposed algorithm,and the scheduling rule can be chosen arbitrarily when using the proposed algorithm.

Best tracking performance for integrator and dead time plant

The optimal tracking performance for integrator and dead time plant in the case where plant uncertainty and control energy constraints are to be considered jointly is inrestigated. Firstly, an average cost function of the tracking error and the plant input energy over a class of stochastic model errors are defined. Then, we obtain an internal model controller design method that minimizes the average performance and further studies optimal tracking performance for integrator and dead time plant in the simultaneous presence of plant uncertainty and control energy constraint. The results can be used to evaluate optimal tracking performance and control energy in practical designs.

Projected Runge-Kutta methods for constrained Hamiltonian systems

Projected Runge-Kutta （R-K） methods for constrained Hamiltonian systems are proposed. Dynamic equations of the systems, which are index-3 differential-algebraic equations （DAEs） in the Heisenberg form, are established under the framework of Lagrangian multipliers. R-K methods combined with the technique of projections are then used to solve the DAEs. The basic idea of projections is to eliminate the constraint violations at the position, velocity, and acceleration levels, and to preserve the total energy of constrained Hamiltonian systems by correcting variables of the position, velocity, acceleration, and energy. Numerical results confirm the validity and show the high precision of the proposed method in preserving three levels of constraints and total energy compared with results reported in the literature.

Adaptive connected hierarchical optimization algorithm for minimum energy spacecraft attitude maneuver path planning

Space object observation requirements and the avoidance of specific attitudes produce pointing constraints that increase the complexity of the attitude maneuver path-planning problem.To deal with this issue,a feasible attitude trajectory generation method is proposed that utilizes a multiresolution technique and local attitude node adjustment to obtain sufficient time and quaternion nodes to satisfy the pointing constraints.These nodes are further used to calculate the continuous attitude trajectory based on quaternion polynomial interpolation and the inverse dynamics method.Then,the characteristic parameters of these nodes are extracted to transform the path-planning problem into a parameter optimization problem aimed at minimizing energy consumption.This problem is solved by an improved hierarchical optimization algorithm,in which an adaptive parameter-tuning mechanism is introduced to improve the performance of the original algorithm.A numerical simulation is performed,and the results confirm the feasibility and effectiveness of the proposed method.

Differences and relations of objectives, constraints, and decision parameters in the optimization of individual heat exchangers and thermal systems

Performance improvement of heat exchangers and the corresponding thermal systems benefits energy conservation, which is a multi-parameters, multi-objectives and multi-levels optimization problem. However, the optimized results of heat exchangers with improper decision parameters or objectives do not contribute and even against thermal system performance improvement. After deducing the inherent overall relations between the decision parameters and designing requirements for a typical heat exchanger network and by applying the Lagrange multiplier method, several different optimization equation sets are derived, the solutions of which offer the optimal decision parameters corresponding to different specific optimization objectives, respectively. Comparison of the optimized results clarifies that it should take the whole system, rather than individual heat exchangers, into account to optimize the fluid heat capacity rates and the heat transfer areas to minimize the total heat transfer area, the total heat capacity rate or the total entropy generation rate, while increasing the heat transfer coefficients of individual heat exchangers with different given heat capacity rates benefits the system performance. Besides, different objectives result in different optimization results due to their different intentions, and thus the optimization objectives should be chosen reasonably based on practical applications, where the inherent overall physical constraints of decision parameters are necessary and essential to be built in advance.

Practical Realization of Optimal Auxiliary Frequency Control Strategy of Wind Turbine Generator

Adding the auxiliary frequency control function to the wind turbine generator(WTG)is a solution to the frequency security problem of the power system caused by the replacement of the synchronous generator(SG)by the WTG.The auxiliary frequency control using rotor kinetic energy is an economical scheme because the WTG still runs at the maximum power point during normal operation.In this paper,the functional optimization model of the auxiliary frequency control strategy of WTG is established.The optimal auxiliary frequency control strategy is obtained by solving the model numerically.As for the practical realization of the control strategy,the coordination of the auxiliary frequency control with the maximum power point tracking(MPPT)control is studied.The practical auxiliary frequency control strategy is modified to adapt to different power disturbances in the system,and the parameter setting method is also proposed.The sensitivity of system frequency to control parameters is studied.Finally,the simulation results verify the effectiveness and practicability of the proposed control strategy.