Optimal switching policy for performance enhancement of distributed parameter systems based on event-driven control

This paper aims to improve the performance of a class of distributed parameter systems for the optimal switching of actuators and controllers based on event-driven control. It is assumed that in the available multiple actuators, only one actuator can receive the control signal and be activated over an unfixed time interval, and the other actuators keep dormant. After incorporating a state observer into the event generator, the event-driven control loop and the minimum inter-event time are ultimately bounded. Based on the event-driven state feedback control, the time intervals of unfixed length can be obtained. The optimal switching policy is based on finite horizon linear quadratic optimal control at the beginning of each time subinterval. A simulation example demonstrate the effectiveness of the proposed policy.

L^(p)-solutions of Multi-dimensional Oblique Reflected BSDEs and Optimal Switching Problem on Finite or Infinite Time Horizon

In this paper,we study mulit-dimensional oblique reflected backward stochastic differential equations(RBSDEs)in a more general framework over finite or infinite time horizon,corresponding to the pricing problem for a type of real option.We prove that the equation can be solved uniquely in L^(p)(1<p≤2)-space,when the generators are uniformly continuous but each component taking values independently.Furthermore,if the generator of this equation fulfills the infinite time version of Lipschitzian continuity,we can also conclude that the solution to the oblique RBSDE exists and is unique,despite the fact that the values of some generator components may affect one another.

Vehicle adaptive cruise control design with optimal switching between throttle and brake

For vehicle adaptive cruise control （ACC） systems, the switching performance between throttle and brake determines the driving comfort, fuel consumption and service lives of vehicle mechanical components. In this paper, an ACC algorithm with the optimal switching control between throttle and brake is designed in model predictive control （MPC） framework. By introducing the binary integer variables, the dynamics of throttle and brake are integrated in one model expression for the controller design. Then the ACC algorithm is designed to satisfy not only safe car following, but also the optimal switching between throttle and brake, which leads to an online mixed integer quadratic programming solved by the nested two-loop method. The simulation results show that the proposed ACC algorithm meets the requirements of safe car following, outperforms the traditional algorithms by performing smoother responses, reducing the switching times between throttle and brake, and therefore improves driving comfort and fuel efficiency significantly.

A study of optimal switching problem in limited-cycle with multiple periods

In a social system or production line,the restrictions of the cost and the due-time exist in each period.Generally,whether these restrictions are satisfied is dependent not only on the risks of this period,but also on the risks generated beforehand.We consider controlling the production process by switching the processing rate to a faster one at a given period.This paper deals with the optimal switching period to minimize the total expected cost of the production process.We first propose the optimal switching period model,and then the mathematic formulation of the total expectation is presented.Finally,the policy of optimal switching period is investigated in detail by numerical experiments.

Data-driven optimal switching and control of switched systems

In this paper,optimal switching and control approaches are investigated for switched systems with infinite-horizon cost functions and unknown continuous-time subsystems.At first,for switched systems with autonomous subsystems,the optimal solution based on the finite-horizon HJB equation is proposed and a data-driven optimal switching algorithm is designed.Then,for the switched systems with subsystem inputs,a data-driven optimal control approach based on the finite-horizon HJB equation is proposed.The data-driven approaches approximate the optimal solutions online by means of the system state data instead of the subsystem models.Moreover,the convergence of the two approaches is analyzed.Finally,the validity of the two approaches is demonstrated by simulation examples.

A Study of Optimum Switching Problem for Production Systems Considering Efficiency, Delivery Time and Green Evaluation

This paper aims to derive the optimal switching strategy for production system considering efficiency, delivery time and green evaluation. Nowadays more and more manufacturing and logistics systems not only pursue better work efficiency, but also focus on green energy evaluation issues. Cost reduction and shortening of delivery time are always important management issues in pursuit of efficiency and optimization of the entire production system because of global production competition. In a market situation where customer needs change in various ways, in particular, due to inadequate quality, changes in the local environment, natural disasters and so on. Therefore, prompt planning of management measures such as switching work processes and changing production methods has become an important issue. On the other hand, since the Paris Agreement came into effect, the construction of an environment-friendly production system has been required as an approach to environmental problems such as global warming. In this paper, we propose an optimum switching model of production systems considering efficiency, delivery time and green evaluation using a green evaluation index (GEC: Green Energy Coefficient). We also discuss the optimal switching strategy by numerical observation.

Resilience-oriented Transmission Expansion Planning with Optimal Transmission Switching Under Typhoon Weather

This paper presents resilience-oriented transmission expansion planning(RTEP)with optimal transmission switching(OTS)model under typhoon weather.The proposed model carefully considers the uncertainty of component vulnerability by constructing a typhoon-related box uncertainty set where component failure rate varies within a range closely related with typhoon intensity.Accordingly,a min-max-min model is developed to enhance transmission network resilience,where the upper level minimizes transmission lines investment,the middle level searches for the probability distribution of failure status leading to max worst-case expected load-shedding(WCEL)under typhoon,and the lower level optimizes WCEL by economic dispatch(ED)and OTS.A nested decomposition algorithm based on benders decomposition is developed to solve the model.Case studies of modified IEEE 30-bus and 261-bus system of a Chinese region illustrate that:a)the proposed RTEP method can enhance resilience of transmission network with less investment than widely used RTEP method based on attacker and defender(DAD)model,b)the influence of OTS on RTEP is closely related with contingency severity and system scale and c)the RTEP model can be efficiently solved even in a large-scale system.

THE EFFECTIVENESS OF GENETIC ALGORITHM IN CAPTURING CONDITIONAL NONLINEAR OPTIMAL PERTURBATION WITH PARAMETERIZATION “ON-OFF” SWITCHES INCLUDED BY A MODEL

In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint method from providing correct gradient during the optimization process. To address this problem, the capture of CNOP, when the "on-off" switches are included in models, is treated as non-smooth optimization in this study, and the genetic algorithm (GA) is introduced. After detailed algorithm procedures are formulated using an idealized model with parameterization "on-off" switches in the forcing term, the impacts of "on-off" switches on the capture of CNOP are analyzed, and three numerical experiments are conducted to check the effectiveness of GA in capturing CNOP and to analyze the impacts of different initial populations on the optimization result. The result shows that GA is competent for the capture of CNOP in the context of the idealized model with parameterization ‘on-off’ switches in this study. Finally, the advantages and disadvantages of GA in capturing CNOP are analyzed in detail.

FPGA Based Speed Control of SRM with Optimized Switching Angles by Self Tuning

The electromagnetic torque and speed in Switched Reluctance Motor (SRM) greatly depend on the excitation parametersi.e. turn-on angle, turn-off angle, dwell angle and magnitude of the phase currents of its phases. At lower speeds, a change in the current contributes the torque requirement which can be achieved either by voltage control (pulse width modulation) or instantaneous current control techniques. At high speeds, due to high back EMF, the regulation of current is crucial and achieved with the control of switching angles of phases. This type of control is referred as average torque control, where the torque is averaged over one stroke (2π/Nr). With constant dwell angle, advancing the phase angle influences the current into the phase winding at minimum inductance position. It has more time to get the current out of the phase winding before the rotor reaches the negative inductance slope. To maintain the speed of the motor at different load conditions, the turn-on and turn-off angles are adaptively varied. The change in dwell angle may be required where the turn-on and turn-off angle may not be sufficient to reach the required speed. In this paper, a new algorithm is proposed for self tuning of switching parameters of SRM. The proposed algorithm is simulated in MATLAB-Simulink and experimentally validated with Field Programmable Gated Array (FPGA) using MATLAB- system generator environment.

Load Torque Compensator for Model Predictive Direct Current Control in High Power PMSM Drive Systems

The widely used cascade speed and torque controllers have a limited control performance in most high power applications due to the low switching frequency of power electronic converters and the convenience to avoid speed overshoots and oscillations for lifetime considerations. Model Predictive Direct Current Control （MPDCC） leads to an increase of torque control performance taking into account the discrete nature of inverters but temporary offsets and poor responses to load torque variations are still issues in speed control. A load torque estimator is proposed in this paper in order to further improve dynamic behavior. It compensates the load torque influence on the speed control setting a feed forward torque reference value. The benefits are twice; the speed controller reaches the speed reference value without offsets which would need to be compensated by an integrator and a better response to load torque variations is obtained since they are detected and compensated leading to small speed variations. Moreover, the influence of pararneter errors and disturbances has been analyzed and limited so that they play a minor role in operation.

COMPLEXITY ANALYSIS AND COMPUTATION OF THE OPTIMAL HARVESTING FOR ONE-SPECIES POPULATION RESOURCES

The exploitation of renewable resources creates many complex problems for culture, ecology and economics as well. Ascertaining the essentials behind the coraplex problems is very important, in this paper, we mainly study various complex relations appearing in the optimal exploitation process for renewable resources. First, we derive a sufficient condition on the existence of optimal harvesting policies for one-species population resources. Then we present every possible optimal harvesting pattern for such a modeh On the basis of this, we give a computing formula for estimating the optimal harvesting period, optimal transitional period, and optimal recruitment period. The main difference with respect to the previous works in literature is that our optimal harvesting policy is a piece-wise continuous function of time t, at the piecewise point to, which is called switching time. At the switching time we switch the harvesting rate from h to some transitional control u,, then to 0. Clearly this kind of harvesting policy is easier to carry out than those by others, provided that there exists a managing department which can highly supervise the resources.

The uncoupled microbial fed-batch fermentation optimization based on state-dependent switched system

In the actual microbial fermentation process,excessive or insufficient substrate can produce inhibitory effects on cells growth.The artificial substrate feeding rules by past experiences have great blindness to keep substrate concentration in a given appropriate range.This paper considers that alkali feed depends on pH value of the solution and glycerol feed depends on glycerol concentration of the solution in the uncoupled microbial fed-hatch fermentation process,and establishes a state-dependent switched system in which the flow rates of glycerol and alkali,the number of mode switches,the mode sequence and the switching times are prior unknown.To maximize the yield of target product 1,3-Propanediol(1,3-PD),we formulate a switching optimal control problem with the flow rates of glycerol and alkali,the number of mode switches,the mode sequence and the switching times as decision variables,which is a mixed-integer dynamic programming problem.For solving the mixed-integer dynamic programming problem,the control parametrization technique,the time scaling transformation and the embedded system technology are used to obtain an approximate parameter optimization problem.By using a parallel optimization algorithm,we obtain the optimal control strategies.Under the obtained optimal control strategies,the 1,3-PD yield at the terminal time is increased significantly compared with the previous results.

Pareto Efficiency of Finite Horizon Switched Linear Quadratic Differential Games

A switched linear quadratic（LQ） differential game over finite-horizon is investigated in this paper. The switching signal is regarded as a non-conventional player, afterwards the definition of Pareto efficiency is extended to dynamics switching situations to characterize the solutions of this multi-objective problem. Furthermore, the switched differential game is equivalently transformed into a family of parameterized single-objective optimal problems by introducing preference information and auxiliary variables. This transformation reduces the computing complexity such that the Pareto frontier of the switched LQ differential game can be constructed by dynamic programming. Finally, a numerical example is provided to illustrate the effectiveness.

Optimization of Waveguide Structure for Tunable Optical Switch in Si/SiGe System

A new electro-optical device using Si/SiGe-system with two parallel ridge waveguides is proposed for optical switching and the optimization of the structure for a single mode operation is investigated.

An S/H circuit with parasitics optimized for IF-sampling

An IF-sampling S/H is presented,which adopts a flip-around structure,bottom-plate sampling technique and improved input bootstrapped switches.To achieve high sampling linearity over a wide input frequency range,the floating well technique is utilized to optimize the input switches.Besides,techniques of transistor load linearization and layout improvement are proposed to further reduce and linearize the parasitic capacitance.The S/H circuit has been fabricated in 0.18-μm CMOS process as the front-end of a 14 bit,250 MS/s pipeline ADC.For30 MHz input,the measured SFDR/SNDR of the ADC is 94.7 dB/68.5dB,which can remain over 84.3 dB/65.4dB for input frequency up to 400 MHz.The ADC presents excellent dynamic performance at high input frequency,which is mainly attributed to the parasitics optimized S/H circuit.