Stock Trading via Feedback Control： Stochastic Model Predictive or Genetic？

We seek a discussion about the most suitable feedback control structure for stock trading under the consideration of proportional transaction costs. Suitability refers to robustness and performance capability. Both are tested by considering different one-step ahead prediction qualities, including the ideal case （perfect price-ahead prediction）, correct prediction of the direction of change in daily stock prices and the worst-case （wrong price rate sign-prediction at all sampling intervals）. Feedback control structures are partitioned into two general classes： stochastic model predictive control （SMPC） and genetic. For the former class, three controllers are discussed, whereby it is distinguished between two Markowitz- and one dynamic hedging-inspired SMPC formulation. For the latter class, five trading algorithms are disucssed, whereby it is distinguished between two different moving average （MA） based strategies, two trading range （TR） based strategies, and one strategy based on historical optimal （HistOpt） trajectories. This paper also gives a preliminary discussion about how modified dynamic hedging-inspired SMPC formulations may serve as alternatives to Markowitz portfolio optimization. The combinations of all of the eight controllers with five different one-step ahead prediction methods are backtested for daily trading of the 30 components of the German stock market index DAX for the time period between November 27, 2015 and November 25, 2016.

Stochastic model predictive braking control for heavy-duty commercial vehicles during uncertain brake pressure and road profile conditions

When heavy-duty commercial vehicles(HDCVs)must engage in emergency braking,uncertain conditions such as the brake pressure and road profile variations will inevitably affect braking control.To minimize these uncertainties,we propose a combined longitudinal and lateral controller method based on stochastic model predictive control(SMPC)that is achieved via Chebyshev–Cantelli inequality.In our method,SMPC calculates braking control inputs based on a finite time prediction that is achieved by solving stochastic programming elements,including chance constraints.To accomplish this,SMPC explicitly describes the probabilistic uncertainties to be used when designing a robust control strategy.The main contribution of this paper is the proposal of a braking control formulation that is robust against probabilistic friction circle uncertainty effects.More specifically,the use of Chebyshev–Cantelli inequality suppresses road profile influences,which have characteristics that are different from the Gaussian distribution,thereby improving both braking robustness and control performance against statistical disturbances.Additionally,since the Kalman filtering(KF)algorithm is used to obtain the expectation and covariance used for calculating deterministic transformed chance constraints,the SMPC is reformulated as a KF embedded deterministic MPC.Herein,the effectiveness of our proposed method is verified via a MATLAB/Simulink and TruckSim co-simulation.

Stochastic Model Predictive Control Approach to Autonomous Vehicle Lane Keeping

In real-world scenarios,the uncertainty of measurements cannot be handled efficiently by traditional model predictive control(MPC).A stochastic MPC(SMPC)method for handling the uncertainty of states in autonomous driving lane-keeping scenarios is presented in this paper.A probabilistic system is constructed by considering the variance of states.The probabilistic problem is then transformed into a solvable deterministic optimization problem in two steps.First,the cost function is separated into mean and variance components.The mean component is calculated online,whereas the variance component can be calculated offline.Second,Cantelli’s inequality is adopted for the deterministic reformulation of constraints.Consequently,the original probabilistic problem is transformed into a quadratic programming problem.To validate the feasibility and effectiveness of the proposed control method,we compared the SMPC controller with a traditional MPC controller in a lane-keeping scenario.The results demonstrate that the SMPC controller is more effective overall and produces smaller steady-state distance errors.

Real-time Energy Management of Low-carbon Ship Microgrid Based on Data-driven Stochastic Model Predictive Control

With increasing restrictions on ship carbon emis-sions,it has become a trend for ships to use zero-carbon energy such as solar to replace traditional fossil energy.However,uncer-tainties of solar energy and load affect safe and stable operation of the ship microgrid.In order to deal with uncertainties and real-time requirements and promote application of ship zero-carbon energy,we propose a real-time energy management strategy based on data-driven stochastic model predictive control.First,we establish a ship photovoltaic and load scenario set consid-ering time-sequential correlation of prediction error through three steps.Three steps include probability prediction,equal probability inverse transformation scenario set generation,and simultaneous backward method scenario set reduction.Second,combined with scenario prediction information and rolling op-timization feedback correction,we propose a stochastic model predictive control energy management strategy.In each scenario,the proposed strategy has the lowest expected operational cost of control output.Then,we train the random forest machine learn-ing regression algorithm to carry out multivariable regression on samples generated by running the stochastic model predictive control.Finally,a low-carbon ship microgrid with photovoltaic is simulated.Simulation results demonstrate the proposed strategy can achieve both real-time application of the strategy,as well as operational cost and carbon emission optimization performance close to stochastic model predictive control.Index Terms-Data-driven stochastic model predictive control,low-carbon ship microgrid,machine learning,real-time energy management,time-sequential correlation.