Nonlinear Control Strategy Research Articles

IMPACTS OF QUANTIFYING SOCIAL DISTANCING MEASURES ON MPC PERFORMANCE FOR SIR-TYPE SYSTEMS

Currently, there has been a sharp increase in epidemic control research as a result of recent epidemic outbreaks. Several strategies aiming to minimize the Epidemic Final Size and/or to keep the Infected Peak Prevalence under a specific value were proposed. However, not many strategies focused on analyzing the impact of applying quantified measures instead of continuous control action. This analysis is a crucial aspect since policymakers design their non-pharmaceutical intervention based on a discrete scale of intensity, from mask-wearing to hard lockdown. In this work, we present a quantized-input non-linear Model Predictive Control strategy to manage non-pharmaceutical interventions during an epidemic outbreak. The impact of quantifying the social distancing measure is computed through several simulations based on a COVID-19 epidemic model and considering different quantization levels of the non-pharmaceutical intervention. Finally, the control performance in each quantization level is evaluated with the computation of four epidemic indices.

Predictive control of reactor network model using machine learning for hydrogen-rich gas and biochar poly-generation by biomass waste gasification in supercritical water

Supercritical water gasification (SCWG) technology can convert biomass into hydrogen rich gas and biochar. Fluidized bed reactor is promising for the industrialization of this technology, and the reactor dynamic performance study is of great significance for its scaling up. However, current simulation studies mainly focus on steady-state analysis using Computational Fluid Dynamics (CFD) software, it is difficult to conduct dynamic study due to its high computational costs. To this end, a reactor network model (RNM) that accounts for the flow, heat transfer, and kinetic dynamics for fluidized bed reactor is firstly developed based on the partitioning theory, which can reduce the computation time of dynamic simulation from several days to seconds. Additionally, extensive open-loop simulations of the RNM are carried out to generate the dataset for the development of a recurrent neural network (RNN) model. Additionally, current studies mainly focus on open-loop simulation, closed loop optimization is absent. To this end, a framework for building a machine learning (ML) model and a ML-based nonlinear predictive control scheme is developed. Model predictive control (MPC) schemes based on the RNN model is used to optimize the SCWG process to achieve multiple objectives, such as maximizing hydrogen yield and carbon yield. The open loop simulation results demonstrate that H2 yield decreases from 0.00022 to 0.0002 kg s−1 with temperature reducing from 700 to 600 °C. To increase H2 yield to the setpoint, MPC increases temperature and mass flowrate to 708 °C and 417.57 kg h−1. Additionally, MPC increases carbon yield and minimizing CO2 yield by decreasing temperature to 475 °C and increasing mass flowrate to 407.38 kg h−1.

Wing Kinematics-Based Flight Control Strategy in Insect-Inspired Flight Systems: Deep Reinforcement Learning Gives Solutions and Inspires Controller Design in Flapping MAVs.

Flying insects exhibit outperforming stability and control via continuous wing flapping even under severe disturbances in various conditions of wind gust and turbulence. While conventional linear proportional derivative (PD)-based controllers are widely employed in insect-inspired flight systems, they usually fail to deal with large perturbation conditions in terms of the 6-DoF nonlinear control strategy. Here we propose a novel wing kinematics-based controller, which is optimized based on deep reinforcement learning (DRL) to stabilize bumblebee hovering under large perturbations. A high-fidelity Open AI Gym environment is established through coupling a CFD data-driven aerodynamic model and a 6-DoF flight dynamic model. The control policy with an action space of 4 is optimized using the off-policy Soft Actor-Critic (SAC) algorithm with automating entropy adjustment, which is verified to be of feasibility and robustness to achieve fast stabilization of the bumblebee hovering flight under full 6-DoF large disturbances. The 6-DoF wing kinematics-based DRL control strategy may provide an efficient autonomous controller design for bioinspired flapping-wing micro air vehicles.

An adaptive safety control approach for virtual coupling system with model parametric uncertainties

Virtual coupling attracts extensive attention from the railway communities due to its promising capacity increase and flexibility improvement. Accurate control that guarantees system safety and performance remains a problem that needs to be further studied. This paper investigates the virtual coupling train system (VCTS) safety control problem with model parameter uncertainties using an adaptive nonlinear control strategy based on urban rail transit systems. A safe automatic train control model is introduced, and a novel control framework consisting of two levels is designed to give an overall virtual coupling train control architecture. For the leading train, we deploy an automatic train control system based on dynamic programming. Then, considering parameter uncertainties, we design an adaptive nonlinear controller that ensures safety and control efficiency for the following trains. The proposed control strategy can ensure that a virtually coupled train set tracks the given speed curve and keeps inner distance while guaranteeing the string stability and safety of the platoon, considering parameter uncertainties. The simulation results show that the proposed approach leads to great performance improvement in virtual coupling train operations.

A non-linear model predictive control strategy to minimise mechanical degradation effects of lithium-ion battery

Mechanical stresses are not only developed inside the active material of the composite electrode, but also in the solid electrolyte interface layer (SEI) due to the graphite expansion. Due to diffusion induced stress (DIS) phenomena, stresses in the negative electrode are generated during charging and discharging cycles. This article presents the proposed optimal charging profile explicitly incorporating the effects of mechanical degradation. Non-linear model predictive control approach along with Gauss pseudo-spectral method is used to optimise charging trajectories. It is assumed that active material is not the weakest material but system is modelled based on SEI break and repair effect. The dynamics of battery is represented by a single particle model. The simulated results are compared with benchmark constant current constant voltage (CCCV) strategy. Furthermore, proposed methodology is compared with optimal CCCV using cycle life ageing experimental scenario. It is estimated that stress amplitude decreased by 7% and nominal capacity increased by 11% using the proposed optimal charging profile.

Nonlinear rotor kinetic energy control strategy of DFIG-based wind turbine participating in grid frequency regulation

Large-scale wind power integration reduces the level of inertia in power systems, significantly deteriorating their frequency stability. To address this issue, a nonlinear rotor kinetic release control strategy is designed for wind power grid-connected systems based on an extended state observer (ESO) and nonlinear feedforward state feedback transformation (NFSFT) theories. First, the affine nonlinear system of a DFIG-based wind turbine participating in grid frequency regulation is established. The system is transformed into a basic equivalent linear system based on the NFSFT theory, and a nonlinear control law is obtained. Second, the ESO is used to observe the generalized disturbance composed of the complex parameter operation required by the nonlinear control law, which circumvents the huge computational burden of the nonlinear control law and the unknown uncertainty of the system. Finally, a limiting link and speed control link are added to prevent excessive release of the kinetic energy of the wind turbine rotor and complete the wind turbine speed recovery. A wind power grid-connected system is built using MATLAB / SIMULINK for simulation verification. The results show that the proposed strategy exhibits a better frequency regulation effect than other strategies.

Deep Imitation Learning of Nonlinear Model Predictive Control Laws for a Safe Physical Human–Robot Interaction

This paper proposes motion planning algorithms for industrial manipulators in the presence of human operators based on deep neural networks (DNNs), aimed at imitating the behavior of a nonlinear model predictive control (NMPC) scheme. The proposed DNN solutions retain the safety features of NMPC in terms of speed and separation monitoring, defined according to the guidelines in the ISO/TS 15066 standard. At the same time, they improve the robot performance in terms of task completion time, and of a-posteriori evaluation of the NMPC cost function on experimental data. The reasons for this improvement are the reduced computational delay of running a DNN compared to solving the nonlinear programs associated to NMPC, and the ability to implicitly learn how to predict the human operator's motion from the training set.

Estimation‐Based Event‐Triggered Adaptive Terminal Sliding Mode Control without Pressure Sensors for a Polymer Electrolyte Membrane Fuel Cell Air Feeding System

Appropriate control for a polymer electrolyte membrane fuel cell air feeding system is vulnerable to load current disturbance, parameter uncertainties, measurement noise, faulty sensors, and limited communication resources. Considering these practical issues, this study presents a novel estimation‐based event‐triggered nonlinear control scheme without pressure sensors to regulate the oxygen excess ratio (OER) at its optimal reference. First, to exactly reconstruct the unmeasured OER, a uniform robust exact differentiator and an adaptive high gain observer are developed to estimate the supply manifold pressure and the cathode pressure, respectively, thus redundant sensors and development cost can be saved. Then, a reduced‐order high gain observer is constructed to approximate the unmodeled lumped disturbance consisting of external disturbances and system uncertainties, which helps to decrease the control gain and improve the robustness. Further, an event‐triggered adaptive terminal sliding mode controller with disturbance compensation is proposed to guarantee accurate and fast OER tracking control with integral absolute error (IAE) of 0.447 and average settling time of 0.9 s while avoiding chattering effect and unnecessary communication in the controller‐to‐actuator channel. The comparison results illustrate that the proposed approach achieves superior estimation and control of OER with satisfactory robustness to parameter perturbations (less than 4% deviation in IAE).

Optimal dynamic operation of pumped storage power plants with variable and fixed speed generators

This paper studies the optimal dynamic operation of pumped storage power plants with variable and fixed speed generators. A control strategy for the dynamic operation is proposed based on a detailed physics-based plant model. It combines a stationary optimizer with a nonlinear model predictive control strategy and a nonlinear state observer for non-measurable system quantities. Contrary to earlier works on this topic, all system constraints are systematically taken into account, e.g., the pressure limits over the whole hydraulic system. The physics-based model allows for an easy model and controller parametrization and application to different plant topologies. This work studies the optimal combined operation of multiple (different) generators in the turbine and pump mode. The results show that a synchronous generator with a full-rated converter is the optimal choice for single-unit operation and that a synchronous generator with a converter-fed synchronous generator is the optimal combination of two units. A clear advantage of variable speed units is identified in the dynamic operation. The performance of the combined operation of variable and fixed speed generators is only slightly lower than for two variable speed units since the proposed control strategy allows to partially mitigate the shortcomings of the fixed speed unit by the variable speed unit.

Improved dwarf mongoose optimization algorithm using novel nonlinear control and exploration strategies

The Dwarf Mongoose Optimization Algorithm (DMO) is a popular metaheuristic algorithm utilized to solve real-world problems. However, DMO has limitations such as slow convergence rate, susceptibility to local optima, and poor performance in high-dimensional problems. Aiming at the defects of the DMO, we propose an improved version called IDMO. This paper introduces the best leader and proposes a novel nonlinear control strategy based on the sine function, significantly enhancing the convergence rate while ensuring accuracy. Moreover, we propose a novel exploration strategy to solve the global optimization and high-dimensional problem. To demonstrate the performance of the proposed IDMO, we test on 65 test functions, including CEC2017, CEC2020, CEC2022, and CEC2013 large-scale global optimization. IDMO is compared with three classes of widely recognized algorithms: (1) highly-cited algorithms, namely, GSA, GWO, WOA, and HHO, (2) advanced algorithms, such as CPSOGSA, CSA, AVOA, and SO, (3) high-performance optimizers including L-SHADE, AL-SHADE, LSHADE-SPACMA, and LSHADE-cnEpSin. Moreover, we also compare with the original DMO and variants such as GDNNMOA, CO-DWO, and BDMOSAO. Meanwhile, we apply IDMO to solve 19 engineering design problems from the CEC2020 real-world optimization suite. Experimental results demonstrate that IDMO outperforms other algorithms, exhibiting excellent convergence rate, stability, and accuracy. The effectiveness of IDMO is confirmed by the Friedman mean ranking, showcasing its potential for metaheuristic optimization and real-world engineering design problems.

Double-layer control architecture for motion and torque optimisation of autonomous electric vehicles

Optimising energy autonomy for autonomous electric vehicles is a significant challenge in sustainable and environmentally friendly mobility. To this end, we propose a novel double-layer control architecture designed to drive the longitudinal motion of electric vehicles equipped with a regenerative braking system. The first layer resembles an Adaptive Cruise Control, while the second one is enabled during the braking phase and is devoted to the blending between the motor torque and the hydraulic one. This layer is synthesised as a Nonlinear Model Predictive Control strategy so as to maximise the efficiency of the regenerative system. The control architecture, by combining the two control strategies, allows to reduce the overall energy consumption for electric vehicles, while simultaneously ensuring a safer, more sustainable, and comfortable driving experience. Additionally, this combination is capable of compensating for any efficiency issues arising from external factors, such as different traffic conditions encountered by the vehicle. To evaluate the effectiveness of the proposed control architecture, extensive simulation analysis was carried out using the MiTraS virtual testing platform, considering two realistic driving scenarios. Results confirm the robustness and safety of the proposed control architecture in ensuring the tracking of the desired trajectory while optimising the energy consumption.

A novel optimal control strategy for regenerative active suspension system to enhance energy harvesting

The conventional onboard actuators in the active suspension system (ASS) consume a high amount of external energy, which restricts their application in reducing the effects of vehicle body vibration caused by road roughness. To address this limitation, the energy-regenerative active suspension system (RASS) based on the electromagnetic structure has been introduced. This paper presents a new optimal control algorithm for the RASS that directly considers energy regeneration in combination with ride comfort in its performance index. The proposed algorithm can be tuned to direct the working points towards high energy regeneration zones. Meanwhile, it employs a realistic nonlinear model of the suspension system to achieve high performance. By applying proper operating electric circuits, the performance of the RASS under the proposed controller is studied for various road conditions. The obtained results indicate that the proposed control strategy delivers lower acceleration over the human body-sensitive frequency range based on ISO2631-1, despite harvesting energy. For instance, when the vehicle travels on a random road with a velocity of 12 m/s, the proposed nonlinear control strategy with a suitable weighting factor increases the capacitor voltage from the initial value of 20 V to 40 V during a 15 s interval. This shows a 31% increase compared to the case where the weighting factor is taken to be zero, according to the previous strategies. Additionally, the proposed strategy by the nonlinear controller remarkably increases the harvested energy compared with a linear optimal controller.

A voltage-shifting-based state-of-charge balancing control for distributed energy storage systems in islanded DC microgrids

This paper presents a distributed secondary level control strategy for battery energy units (BEUs) parallel in a DC microgrid. The control structure is divided into two layers. The primary control layer is implemented with a droop control. The voltage-shifting term is generated by the secondary control layer, where an information state factor λ is introduced to meet the bus voltage restoration, state-of-charge (SOC) balancing and accurate current sharing. A sigmoid function with a balancing adjustment factor is adopted to improve both SOC balancing accuracy and speed. When each information state factor in the system converges to the average value, accurate current sharing according to the SOC values of BEUs and capacities is attained, and the average output voltage is consistent with the nominal voltage of microgrid. Compared with existing methods, the proposed method has no requirement on a complex control structure and only two variables need to be transferred between adjacent converters, which reduces the information traffic and the communication burden. Moreover, the proposed nonlinear control strategy effectively avoids unnecessary cyclic charge or discharge between BEUs and inhibits circulating currents. The proposed method also has the ability of plug and play, since SOC balancing among BEUs with different capacities is considered, and, in the communication layer, the dynamic average consensus algorithm is utilized to obtain both average SOC and average λ. The performance of the proposed control method is confirmed in simulation and experiment.

A nonlinear optimal control approach for voltage source inverter-fed three-phase PMSMs

A nonlinear optimal control approach for voltage source inverter-fed three-phase PMSMs

PSO based optimized PI controller design for hybrid active power filter

This research study presents the design and simulation of a hybrid active power filter (HAPF) for reducing harmonics. The reference currents have been determined using the synchronous reference frame technique. To achieve its goals, the proposed HAPF employs AI algorithm known as particle swarm optimization (PSO) to fine-tune the proportional-integral PI controller's parameters. With the help of PI-PSO controller the DC link voltage is regulated in the HAPF-inverter. A non-linear current control strategy based on hysteresis employed here to construct the pulse gate by comparing the retrieved reference and real currents necessitated by the HAPF. Simulations were carried out in MATLAB and shown that the proposed method is extremely adaptable and efficient in reducing harmonic currents caused by non-linear loads.

Nonlinear robust adaptive control for bidirectional stabilization system of all-electric tank with unknown actuator backlash compensation and disturbance estimation

Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of all-electric tank, it behaves more drastically in high maneuvering environments. In this work, the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved. By utilizing the smooth adaptive backlash inverse model, a nonlinear robust adaptive feedback control scheme is presented. The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term. Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation, which ensures the system stability. Meanwhile, the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality. Finally, Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash. Contrast co-simulations and experiments illustrate the advantages of the proposed approach.

FXESO based FNMPC path following control for underactuated surface vessels with roll stabilisation

External ocean disturbances result in the roll motion of underactuated surface vessels (USVs), which probably have severe effects. To improve the USV capability of path following and speed maintenance in rudder roll stabilisation (RRS) control, a fixed-time extended state observer (FXESO) based fuzzy nonlinear model predictive control (FNMPC) scheme was proposed. A 4-degrees of freedom (DOF) nonlinear USV model is established for investigation. Subsequently, the FXESO is designed to estimate the velocity states and lumped disturbance terms of the USV model, which are used to simplify the predictive model. Subsequently, the disturbance compensation nonlinear model predictive control (NMPC) with fuzzy rules is designed to handle multi-objective cooperative control problems between path following and RRS. Fuzzy rules are designed to improve the performance of path following and speed maintenance by altering the objective function weights in MPC. The stability of proposed scheme was theoretically proven. The effectiveness of proposed algorithm was verified through simulations and comparisons.

Experimental Performance of a Nonlinear Control Strategy to Regulate Temperature of a High-Temperature Solar Reactor

Abstract Despite the significant potential of solar thermochemical process technology for storing solar energy as solid-state solar fuel, several challenges have made its industrial application difficult. It is important to note that solar energy has a transient nature that causes instability and reduces process efficiency. Therefore, it is crucial to implement a robust control system to regulate the process temperature and tackle the shortage of incoming solar energy during cloudy weather. In our previous works, different model-based control strategies were developed namely a proportional integral derivative controller (PID) with gain scheduling and adaptive model predictive control (MPC). These methods were tested numerically to regulate the temperature inside a high-temperature tubular solar reactor. In this work, the proposed control strategies were experimentally tested under various operation conditions. The controllers were challenged to track different setpoints (500 °C, 1000 °C, and 1450 °C) with different amounts of gas/particle flowrates. Additionally, the flow controller was tested to regulate the reactor temperature under a cloudy weather scenario. The ultimate goal was to produce 5 kg of reduced solar fuel magnesium manganese oxide (MgMn2O4) successfully, and the controllers were able to track the required process temperature and reject disturbances despite the system's strong nonlinearity. The experimental results showed a maximum error in the temperature setpoint of less than 0.5% (6 °C), and the MPC controller demonstrated superior performance in reducing the control effort and rejecting disturbances.

Hybrid model predictive control for premixed natural gas engine as distributed generator

The access to electricity is increasing worldwide with a considerable growing rate per year. Unfortunately, traditional large fossil power technologies based on coal and oil lead to a major concern in environmental pollution, so the distributed power generation utilizing natural gas engine is taken into consideration. In this paper, a hybrid model predictive control (HMPC) strategy is proposed for natural gas engines in distributed power generation. The original fourth-order double-input double-output (DIDO) nonlinear engine model is transformed into two simpler models, one is a first-order model for air–fuel ratio (AFR) control, and a linear model predictive control (LMPC) strategy is designed. The other is a second-order nonlinear model for speed control, and the AFR is used as one of its two inputs to design nonlinear model predictive control (NMPC) strategy. The advantage of this is that each model is simpler than the original fourth-order model, and prediction horizon can be lengthened to achieve better control performance. For the optimization problem of NMPC, a switching mechanism strategy is proposed. It can effectively reduce the number of SQP iterations in both transient and steady-state conditions. Both simulations and experiments verify the feasibility of the HMPC and the effectiveness of the switching mechanism.

Nonlinear model predictive control for efficient and robust airpath management in fuel cell vehicles

The fuel cell airpath multivariable control problem of optimally coordinating the electric compressor motor and the back-pressure valve to achieve efficient and safe conditions, for both steady state and transient operation, has not been completely addressed in the literature. This paper proposes a nonlinear model predictive control strategy, implemented via the Garrett Motion proprietary NMPC toolbox, to regulate the oxygen stoichiometry and the cathode pressure of an automotive fuel cell airpath system, while avoiding compressor surge and air starvation. The controller set-points are optimized, using the nonlinear model, to achieve the maximum system power as a function of the operating stack condition. The effectiveness and robustness of the proposed control strategy have been validated by means of a simulated World harmonized Light-duty vehicles Test Cycle (WLTC), under both state feedback and model parameters uncertainties.