Distributed model predictive control based on adaptive sampling mechanism

In this work,an adaptive sampling control strategy for distributed predictive control is proposed.According to the proposed method,the sampling rate of each subsystem of the accused object is determined based on the periodic detection of its dynamic behavior and calculations made using a correlation function.Then,the optimal sampling interval within the period is obtained and sent to the corresponding sub-prediction controller,and the sampling interval of the controller is changed accordingly before the next sampling period begins.In the next control period,the adaptive sampling mechanism recalculates the sampling rate of each subsystem’s measurable output variable according to both the abovementioned method and the change in the dynamic behavior of the entire system,and this process is repeated.Such an adaptive sampling interval selection based on an autocorrelation function that measures dynamic behavior can dynamically optimize the selection of sampling rate according to the real-time change in the dynamic behavior of the controlled object.It can also accurately capture dynamic changes,meaning that each sub-prediction controller can more accurately calculate the optimal control quantity at the next moment,significantly improving the performance of distributed model predictive control(DMPC).A comparison demonstrates that the proposed adaptive sampling DMPC algorithm has better tracking performance than the traditional DMPC algorithm.

AN EXTRACTION ALGORITHM FOR A SET OF ELEMENTARY SIPHONS BASED ON MIXED-INTEGER PROGRAMMING

Elementary siphons are useful in the development of a deadlock prevention policy for a discrete event system modeled with Petri nets. This paper proposes an algorithm to iteratively extract a set of elementary siphons in a class of Petri nets, called system of simple sequential processes with resources （S3pR）. At each iteration, by a mixed-integer programming （MIP） method, the proposed algorithm finds a maximal unmarked siphon, classifies the places in it, extracts an elementary siphon from the classified places, and adds a new constraint in order to extract the next elementary siphon. This algorithm iteratively executes until no new unmarked siphons can be found. It finally obtains a unique set of elementary siphons and avoids a complete siphon enumeration. A theoretical analysis and examples are given to demonstrate its efficiency and practical potentials.

Control of upper limb rehabilitation robot based on active disturbance rejection control

The upper limb rehabilitation robot technology integrates rehabilitation medicine,human anatomy,mechanics,computer science,robotics,and many other disciplines.Its main function is to drive the affected limb to carry out rehabilitation training to restore the condition of patients with upper limb dyskinesia,which plays a great role in improving the quality of life.In this study,to resolve the problems of slow convergence speed and poor tracking accuracy due to the interference of patient spasms with the trajectory-tracking control of the upper limb rehabilitation robot,a novel algorithm based on active disturbance rejection control(ADRC)is adopted,and the convergence of its main structure is proved by the time-domain analysis method.First,this ADRC algorithm can obtain better trajectory-tracking performance due to its non-linear extended observer and non-linear feedback mechanism,even if the model suffers a strong disturbance or receives inaccurate information.Second,the non-linear tracking differentiator can guarantee to gain quick convergence speed.To validate this algorithm,a model of three degrees of freedom upper limb rehabilitation robot is established using MATLAB R2019b and three situations including strong spasm and weak spasm are carried out to prove the effectiveness and reliability of the control algorithm designed.

Adaptive neural tracking control for upper limb rehabilitation robot with output constraints

The authors investigate the trajectory tracking control problem of an upper limb reha-bilitation robot system with unknown dynamics.To address the system's uncertainties and improve the tracking accuracy of the rehabilitation robot,an adaptive neural full-state feedback control is proposed.The neural network is utilised to approximate the dy-namics that are not fully modelled and adapt to the interaction between the upper limb rehabilitation robot and the patient.By incorporating a high-gain observer,unmeasurable state information is integrated into the output feedback control.Taking into consider-ation the issue of joint position constraints during the actual rehabilitation training process,an adaptive neural full-state and output feedback control scheme with output constraint is further designed.From the perspective of safety in human–robot interaction during rehabilitation training,log-type barrier Lyapunov function is introduced in the output constraint controller to ensure that the output remains within the predefined constraint region.The stability of the closed-loop system is proved by Lyapunov stability theory.The effectiveness of the proposed control scheme is validated by applying it to an upper limb rehabilitation robot through simulations.

Robust model predictive tracking control for the wheeled mobile robot with boundary uncertain based on linear matrix inequalities

In this study,a robust model predictive controller is designed for the trajectory tracking problem of non-holonomic constrained wheeled mobile robot based on an elliptic invariant set approach.The controller is based on a time-varying error model of robot kinematics and uses linear matrix inequalities to solve the robust tracking problem taking uncertainties into account.The uncertainties are modelled by linear fractional transform form to contain both parameter perturbations and external disturbances.The control strategy consists of a feedforward term that drives the centre of the ellipse to the reference point and a feedback term that converges the uncertain system state error to the equilibrium point.The strategy stabilises the nominal system and ensures that all states of the uncertain system remain within the ellipsoid at each step,thus achieving robust stability of the uncertain system.Finally,the robustness of the algorithm and its resistance to disturbances are verified by simulation and experiment.

Model predictive control of grid-connected PV power generation system considering optimal MPPT control of PV modules

Because of system constraints caused by the external environment and grid faults,the conventional maximum power point tracking(MPPT)and inverter control methods of a PV power generation system cannot achieve optimal power output.They can also lead to misjudgments and poor dynamic performance.To address these issues,this paper proposes a new MPPT method of PV modules based on model predictive control(MPC)and a finite control set model predictive current control(FCS-MPCC)of an inverter.Using the identification model of PV arrays,the module-based MPC controller is designed,and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature.An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors,the optimal voltage vector is selected according to the optimal value function,and the corresponding optimal switching state is applied to power semiconductor devices of the inverter.The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified,and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink.The results show that MPC has better tracking performance under constraints,and the system has faster and more accurate dynamic response and flexibility than conventional PI control.

Dynamic parameter identification of upperlimb rehabilitation robot system based on variable parameter particle swarm optimisation

To solve the problem of uncertain parameters in dynamic modelling of upper-limb rehabilitation robots,a dynamic parameter identification method based on variable parameters particle swarm optimisation(PSO)is developed.Based on the dynamic model of the system,the algorithm changes the inertia parameter and learning law of the basic PSO algorithm from the fixed-parameter to the function that changes with the number of iterations.It solves the problems of small search space in the early stage and slow convergence speed in the later stage of the basic PSO algorithm,which greatly improves its identification accuracy.Finally,through the comparison and analysis of the simulation results,compared with those of the least square(LS)and unmodified PSO identification algorithms,a great improvement in the identification accuracy of the algorithm is achieved.The control effect in the actual control system is also much better than those of the LS and PSO algorithms.