Control Performance Research Articles

A self-sustaining effect induced by iron sulfide generation and reuse in pyrite-woodchip mixotrophic bioretention systems: An experimental and modeling study

Dual electron donor bioretention systems have emerged as a popular strategy to enhance dissolved nitrogen removal from stormwater runoff. Pyrite-woodchip mixotrophic bioretention systems showed a promoted and stabilized removal of dissolved nutrients under complex rainfall conditions, but the sulfate reduction process that can induce iron sulfide generation and reuse was overlooked. In this study, experiments and models were applied to investigate the effects of filler configuration and dissolved organic carbon (DOC) dissolution rate on treatment performance and iron sulfide generation in pyrite-woodchip bioretention systems. Key parameters govern that DOC dissolution and microbe-mediated processes were obtained by experiments. The water quality models that integrate one-dimensional constant flow, sorption and microbial transformation kinetics were used to predict the performance of bioretention systems. Results showed that the mixotrophic bioretention system with woodchip mixed in the vadose zone and pyrite in the saturated zone achieves a better performance in both nitrogen removal efficiency and by-product control. Comparably, woodchip and pyrite mixed in the saturated zone could encounter a high secondary pollution risk. The sensitivity coefficients of oxic/anoxic DOC dissolution rates to total nitrogen removal are 0.36 and -2.43 respectively. Iron sulfide generation was affected by DOC distribution and the competition between heterotrophic denitrifiers, autotrophic denitrifiers, and sulfate-reducing bacteria (SRB). DOC accumulation has an antagonistic effect on iron production and sulfate reduction. Extra DOC accumulation favors sulfate reduction while high DOC concentration inhibits pyrite-based denitrification and reduces Fe(III) production. The recycling of iron sulfide can improve the robustness and sustainability of bioretention systems.

Deep Koopman-operator-based model predictive control for free-floating space robots with disturbance observer

This paper investigates the optimal tracking control problem of free-floating space robots in the presence of non-ideal factors, such as model uncertainties and external disturbances. To address this issue, we first leverage the deep Koopman operator, facilitated with a deep neural network, to establish an offline formulation of a global linearization model for a micro-nano free-floating space robot. Based on the estimated linearization model, we then employ the model predictive control method for online optimization control, achieving a significantly reduced computational burden. Additionally, a decay factor is integrated into the model predictive control optimization objective function to balance the precision of joint angles tracking with the suppression of base satellite attitude disturbance. To further address the modeling inaccuracies and external disturbances, we incorporate a disturbance observer based on a radial basis function neural network for online compensation within the model predictive control framework. This augmentation enhances tracking precision and robustness. Several groups of simulation results are carried out to demonstrate the effectiveness of the proposed method, showing its capability of energy consumption, disturbance rejection and enhanced robustness. These highlight the potential of the proposed method to improve the control performance of free-floating space robots, even in the absence of a precise dynamic model.

Associations between white and grey matter damage and gait impairment in cerebral amyloid angiopathy

BackgroundCerebral amyloid angiopathy (CAA) is associated with white matter damage and neurodegeneration. Gait is impaired in CAA; however, the neural basis of this impairment is unclear. Research questionAre gait impairments in patients with CAA associated with altered cerebral white matter diffusivity and/or atrophy of cortical and subcortical grey matter. MethodsParticipants with CAA (n=29), Alzheimer’s disease (AD; n=16), and normal controls (n=47) were included. Gait was assessed using a 6 m walkway with parameters categorized into rhythm, pace, postural control, and variability domains. The dual-task cost (DTC) of gait speed was calculated for counting backwards, animal fluency, and serial sevens tasks. Whole-brain white matter disruption was quantified using the peak width of skeletonized mean diffusivity (PSMD), and thickness and volume of select cortical, subcortical, and cerebellar regions were quantified using FreeSurfer. ResultsIn CAA participants, associations were found between PSMD and pace (standardized parameter estimate (β), 95 % confidence interval (CI): 0.17, 0.03–0.32), and medial orbital frontal cortical thickness and counting backwards DTC (parameter estimate (PE), 95 % CI: −5.7 %/SD, −0.24 to −11.23). Across all groups, including CAA, associations were found between PSMD and pace, variability, counting backwards DTC, and animal fluency DTC; between frontal cortical thickness and pace, counting backwards DTC, and animal fluency DTC; between cortical regions affected by AD (inferior parietal cortex, inferior and middle temporal gyrus) and counting backwards DTC; and between thalamus volume and postural control. SignificanceReduced white matter structural integrity and grey matter loss is associated with poor overall gait performance in CAA, AD, and normal controls.

Performance and mechanism of adaptive pitch control in a floating vertical axis wind turbine with a surge motion

Floating vertical axis wind turbines are bearing complex aerodynamic fatigue loads due to the movement of the platform and the reciprocating variations in the angle of attack and the blade relative wind speed. To explore the effect of the surge motion of the platform and verify the performance and acting mechanism of adaptive pitch control on reducing the fatigue loads and improving the power coefficient, a floating vertical axis wind turbine with three motion forms (non-surge, pure surge, and surge-adaptive pitch) is investigated in the condition of a low tip speed ratio with a high incoming wind speed. The results show that the surge motion enhances the fatigue loads, with the power coefficient less affected. The adaptive pitch control reduces the enhanced fatigue loads and improves the power coefficient by nearly twice. Vorticity and pressure distributions verify that the fluctuation ranges of the angle of attack and the blade relative wind speed are expanded by the surge motion, and the angle of attack is reduced by the adaptive pitch control especially in the upwind section, with the flow separation mitigated. However, the effects of the surge motion and the pitch variation are also influenced by the shedding vortex and the flow separation reattachment. For the upwind part of the blade aerodynamic forces, with the surge amplitude increase or the surge period decrease, the global amplitudes are increased gradually by the surge motion and then reduced by the pitch control, with the load reduction performance enhanced gradually.

CoplayingVR: Understanding User Experience in Shared Control in Virtual Reality

Shared control of avatars in virtual reality is emerging as a promising method to enhance collaborative interactions. While the literature investigates shared control as an input modality to understand the design of functional experiences, the user experience associated with shared control remains underexplored. In this paper, we investigate the user experience of shared control using a networked VR environment, "CoplayingVR". Through a user study comprised of three gaming tasks in VR with 48 participants, we collect both quantitative and qualitative data on the user experience and performance of shared control. Our findings reveal that the shared control mode significantly improved user experience factors and brought novelty to the play. Furthermore, shared control significantly enhanced the task efficiency for novice users. Finally, we discuss how our findings inform the implications for research and practice of shared control for the growing body of research in VR.

On multi-stage deformation and gradual energy absorption of 3D printed multi-cell tubes with varying cross-section

Thin-walled structures are widely used in passive safety systems due to their excellent lightweight and controllable progressive deformation performance. While designing to improve the energy absorption performance of thin-walled structures, it is also crucial to consider reducing the damage received by protected objects during accidental collisions. In this work, stepwise and continuous graded multi-cell tubes with varying cross-sections were proposed based on the fused deposition modeling technique to achieve a gradual energy absorption process. Compression responses and energy absorption characteristics of the graded multi-cell tubes were investigated by quasi-static compression tests. Experimental results showed that the stepwise graded multi-cell tubes produced obvious multi-stage deformation and energy absorption during axial compression. The specific energy absorption presented an upward trend with the increased number of stepwise segments. For the same ranges of cross-section variations, continuous graded multi-cell tubes exhibited more plastic hinges and better energy absorption performance due to the absence of discontinuous interfaces. In addition, continuous graded multi-cell tubes showed continuous folding deformation with the gradually decreasing folding wavelength. Thus, it exhibited a steadily increasing trend in crushing force and realized the gradual energy absorption mode.

Thermo-pressed blend films of poly(lactic acid)/poly(butylene adipate-co-terephthalate) with polylimonene for sustainable active food packaging

Poly(lactic acid) (PLA) and poly(butylene adipate-co-terephthalate) (PBAT) blends provide sustainable packaging solutions renowned for their robustness and biodegradability. Herein, we used a commercial PLA/PBAT blend (Ecovio® - E) to develop films by thermo-pressing, incorporating polylimonene (PLM) and comparing them with limonene (LIM) at 5% and 10% additive concentrations. Remarkably, E/PLM10% film exhibited an 84.9% reduction in Staphylococcus aureus count and a 62% decrease in Aspergillus niger count, surpassing LIM and PLA/PBAT. Additionally, PLM substantially improved the antioxidant activity of the films, achieving more than 40% DPPH scavenging and providing light-blocking capability, with E/PLM5% virtually halting UV-A transmission. Mechanical assessments showed that, although higher PLM levels reduced tensile strength, E/PLM5% matched control performance. FT-IR analysis confirmed PLM presence without chemical alterations, and minimal changes in crystallinity were observed via DSC and XRD, while TGA confirmed the attractive thermal stability of the films. Expanding on these findings, we evaluated film efficacy in cherry tomato preservation, including unpackaged controls. E/PLM5%-packaged tomatoes exhibited superior visual quality and fungal inhibition, proving a promising option for active food packaging due to their antimicrobial effectiveness, antioxidant activity, and UV-light protection. Therefore, this study represents significant progress in advanced packaging development, providing prolonged food preservation, sustainability, and retained performance even when prepared in simulated industrial conditions.

Decentralized optimal voltage control for wind farm with deep learning-based data-driven modeling

The random fluctuations of wind energy and external grid voltage disturbances can both lead to serious voltage fluctuations and voltage deviations in the wind farm (WF). Voltage/reactive power control is an effective way to improve the voltage stability of WF. The existing research is based on complex physical models with prior parameter information, and the accuracy and calculation speed of the WF model are difficult to guarantee. To address this issue, this paper explores a decentralized optimal voltage control method for WF with deep learning-based (DL) data-driven modeling (DL-DOVC). A hybrid convolutional neural network (CNN)-Transformer architecture is proposed to establish the data-driven model of WF, leveraging its enhanced capabilities for extracting time series correlation, to learn complex patterns and dynamics from time series data. Then, we develop a fully decentralized voltage control method for WF to regulate the terminal bus voltage of wind turbines (WTs) within a feasible range. A DL-based data-driven predictive controller is specially designed to solve the established data-driven voltage optimization problem, it eliminates the necessity for frequent manual model maintenance and is suitable for real-time application. Additionally, the difficulty of WF decentralized optimal voltage control arises from the inability of local controllers to fully consider the impact of all non-local WTs on the local WT states. By designing the DL-based auxiliary state-feedback controller, the effects of non-local WTs are implicitly considered in the auxiliary feedback control law. A WF with 32*6.25 MW WTs is used to test the proposed DL-DOVC method. Simulation results show that the proposed DL-based model can efficiently learn and predict the WF dynamics under different operating scenarios. The near-global optimal voltage control performance is achieved only with local measurements by employing the DL-DOVC method.

High-efficiency adaptive temperature control for thermoelectric system based on the OBPPID strategy

Thermoelectric cooler (TEC) is widely used for temperature control in optoelectronics, machinery, biomedicine, and other fields. However, the unstable temperature control over wide ranges and different gradients restricts the efficiency of TEC. To highlight the neural network’s effectiveness in controlling TEC systems, the optimized BPPID (OBPPID) strategy is proposed. In the OBPPID strategy, the factors s̃ and G are decided through formula derivation and the particle swarm optimization (PSO) algorithm, respectively contributing to the backward adjustment and forward propagation of BPPID. The OBPPID solves three issues that the BPPID faces: gradient vanishing of network weights, over-reliance of initial network weights and range constraint of control parameters. The OBPPID and BPPID have been simulated 1000 times to evaluate control performance. The results show that, with random initial network weights, the OBPPID strategy reduces the cost variability of the BPPID strategy from 92 to 17, achieving an 81.52% reduction. Physical tests have confirmed that in the temperature control over wide range and the temperature gradients of 5°C, 20°C, and 80°C, the OBPPID strategy can adaptively adjust the control parameters and provide higher efficiency than PID, BPPID and fuzzy PID strategies for TEC system.

How vertical drainage positions of extensive green roofs affect the runoff control performance

The capacity of extensive green roofs (EGRs) in runoff reduction and pollutants control significantly decreased with increasing rainfall intensity. This is attributed to most of EGRs are the lower-drainage method and use a thinner substrate layer because of the structural load-bearing limits of buildings. Existing solutions depend on increasing the rainwater storage by thickening the substrate layer of EGRs, or replacing the substrate materials. We propose to enhance the water retention capacity of EGRs by adjusting vertical drainage positions and moving the lower-drainage at the bottom to upper-drainage over the substrate layer. To test the runoff control performance of EGRs with the upper-drainage method, we designed three EGR models with two different (upper- and lower-) drainage methods based on the common EGR’s structure in practice and carried out total 48 experiments under different rainfall intensities with an artificial rainfall simulator. The experimental design also included adjusting structural factors of the EGR to further explore the influences of roof slope, status of vegetation growth (height and canopy cover ratio), and model size. The average runoff retention rate for the upper-drainage EGR was 61.82 %, which was much higher than the 23.94 % for the lower-drainage EGR. The concentrations of TN and TP in runoff with the upper-drainage were 2.83 and 0.18 mg/L, which were much lower than the 8.47 and 2.10 mg/L for the lower-drainage EGR. EGRs with upper-drainage overall performed better in runoff control than EGRs with lower-drainage. Vegetation height and model size did not significantly affect the rainwater control performance for different drainage methods, while the change of slope affected the rainwater interception of the EGRs with upper-drainage method.

Amphiphilic silica monomers induced superhydrophilic and flexible silica aerogels for radiative cooling and atmospheric water harvesting

Silica aerogels have attracted extensive attention owing to their exceptional porous structures and crucial applications for sustainable environments. However, the inherent fragility and complex manufacturing process of silica aerogels present a challenge for their prolonged applications. In this study, an amphiphilic silica monomer, triethoxysilane end-capped poly(ethylene oxide) (PEO-TES), is designed and synthesized, which can co-gelled with methyltrimethoxysilane in water and gives birth to flexible and superhydrophilic silica aerogels via ambient pressure drying (APD). PEO-TES plays a dual role as both co-monomers and surfactants, effectively stabilizing the sol–gel transition, adjusting the porous structure of hydrogels, and imparting superhydrophilicity. The silica aerogel has low density (0.147 g cm−3), zero water contact angle, high elasticity (80 % compression), high solar reflectance (0.888), and high IR emissivity (0.967). These features make them robust all-day passive radiative cooling materials with 10.5 °C sub-ambient cooling capacity and atmospheric water harvesting capacities for both vapor (a water harvesting rate of 0.041 kg m−2h−1) and fog (harvesting capacity of 2.9 g g−1). The strategy provides a simple APD method for synthesizing flexible and superhydrophilic silica aerogels with controllable porous structures, passive cooling, and water harvesting performance, which may help address global climate change and water scarcity challenges.

An efficient low-delay polyphase implementation method for active noise control systems

When implementing active noise control systems on signal processing hardware, the time delay introduced by electronic components (especially components requiring additional lowpass filters or introducing fixed-sample-size delays) may adversely affect the noise control performance. One common approach to reducing this delay is to use a high sampling rate, but this increases the computation significantly when implementing the ANC filters in real time. In the current work, a polyphase-structure-based filter design method is developed for active noise control systems that can reduce the computation load for real-time filter implementation but do not introduce an additional time delay. Although the computation reduction capability of a polyphase filter structure is well known for multi-rate systems, the traditional use of such multi-rate systems requires additional anti-aliasing and reconstruction filters which introduces an additional time delay. Thus, in delay-sensitive applications, such as active noise control, this method was previously applied only on the filter adaption phase, instead of directly on the real-time filtering process. In this article, a filter decomposition method using the minimum-phase technique is proposed to decompose an ANC filter into two multiplicative causal filters both of which have lowpass frequency response shapes at high frequencies such that the polyphase structure can be applied directly to the two multiplicative causal control filters without introducing additional anti-aliasing and reconstruction filters. Results show that, compared with various traditional low sampling rate implementations, the proposed method can significantly improve the noise control performance. Compared with the non-polyphase high-sampling rate method, the real-time computations that increase with the sampling rate are improved from quadratically to linearly.

A multi-objective co-optimization method of controller parameters for the overall system of small pressurized water reactor

Small pressurized water reactors (SPWRs) normally have complicated and varying working conditions, and become an important direction for nuclear energy development. The overall system of SPWR, including the reactor coolant average temperature control system, the feedwater control system of the once-through steam generator (OTSG), the steam turbine speed control system and the steam dump control system, is used to guarantee the safe and stable operation. The overall system of SPWR is designed with multiple PI controllers. To accomplish intelligent self-tuning of PI controller parameters, reduce the dependence on human engineering experience, and improve the control performance of the overall system, a multi-objective co-optimization method(MMOCO) with PI controller parameters for overall system of SPWR is proposed. Firstly, based on the coordinated control theory and PI control algorithm, the overall system of SPWR is established. Then, a MMOCO for the overall system of SPWR is designed with the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). Finally, to compare the performance of the controller parameters derived from the MMOCO and the controller parameters obtained via the engineering tuning method, step load decrease transients are selected. The results show that the ability to control the overall system of SPWR is effectively improved after applying the MMOCO.

Preparation and characterization of fatty acid ternary eutectic mixture/Nano-SiO2 composite phase change material for building applications

In order to prepare a composite phase change material (PCM) with suitable phase transition temperature, strong temperature control performance, good latent heat properties, and low cost for incorporation into concrete, the goal is to control temperature cracks, and endow the building structure with temperature regulation capabilities through phase change. In this study, capric acid (CA), myristic acid (MA) and stearic acid (SA) were used as PCMs. Firstly, based on theoretical calculations, a ternary eutectic mixture of CA-MA-SA with a mass ratio of 67.16:22.98:9.86 was successfully prepared. Subsequently, a new composite PCM was created using a melt blending technique, incorporating nano-SiO2 as a supporting material. The comprehensive characterization results from FT-IR, SEM, XRD, DSC and TG indicate that nano-SiO2 interacts with CA-MA-SA through its porous structure and capillary action, as well as surface tension, rather than through any chemical interaction, when 65% of CA-MA-SA is adsorbed in nano-silica, the melting temperature and latent heat of the composite PCM are 19.23 °C and 97.82 J/g, respectively. Without any leakage of molten CA-MA-SA. This material exhibits a favorable temperature for phase transition and possesses a high amount of latent heat, making it a promising candidate for use in construction projects. Additionally, the composite PCM shows remarkable thermal stability after 200 phase change cycles, which not only lowers building energy usage but also conserves energy, making it ideal for broad use in civil engineering.

XRL-FlexSBR: Multi-agent reinforcement learning-driven flexible SBR control with explainable performance guarantee under diverse influent conditions

The sequencing batch reactor (SBR) process stands out for its small footprint and operational flexibility. However, the SBR process is highly nonlinear and subject to influent disturbances. In this study, we suggested an explainable multi-agent reinforcement learning (XRL) approach coupled with multi-agent reinforcement learning (MARL) and explainable AI (XAI); then, an XRL-driven flexible SBR control (XRL-FlexSBR) system was developed to conduct multivariate control the SBR process autonomously. Influent big datasets including biochemical oxygen demand (BOD) and total nitrogen (TN) were collected from the wastewater treatment plants (WWTPs) of South Korea. Then, the Gaussian mixture model was utilized to cluster the diverse influent conditions and the SBR mechanistic model was developed. A game abstraction method based on a two-stage attention network (G2ANET), one of MARL algorithms, was employed to manipulate dissolved oxygen (DO) and extra carbon injection (EC) controllers in the SBR process; furthermore, layer-wise relevance propagation (LRP) of explainable AI (XAI) technique was utilized to evaluate a control performance guarantee by G2ANET. The results verified that XRL-FlexSBR can control the DO and EC controllers in the SBR process while reducing the energy consumption by 4.93 % on average and maintaining effluent quality criteria across the diverse influent conditions. Furthermore, XAI explained that the improved control performance of XRL-FlexSBR agents is attributed to their understanding of the mechanism underlying SBR operations without human intervention. Hence the proposed XRL-FlexSBR can flexibly control the SBR to improve sustainability and profitability under varying influent conditions.

Mode-dependent observer-based secured hybrid-driven reliable control for semi-Markov jump cyber–physical systems with DoS attacks

This study focuses on the issue of secured observer based hybrid-triggered quantized reliable control design for semi-Markov jump cyber–physical systems (S-MJCPSs) with general uncertain transition rates, quantization, actuator faults, denial-of-service (DoS) attacks and disturbances. In particular, a mode-dependent observer is developed to estimate the unmeasurable states of the considered system. Further, to minimize unnecessary data transmissions within the network channel, we have implemented a hybrid-triggered mechanism in which, the swap process between the time-triggered and the event-triggered mechanisms is governed by a Bernoulli distribution. Moreover, an observer-based hybrid-triggered quantized reliable controller is devised by utilizing the state estimation information obtained from the observer, which assists the S-MJCPSs to achieve primary intent of this study. Subsequently, the control input signals are quantized before being transmitted into the actuator due to the limited capacity of the data transmission medium. Concurrently, periodic DoS jamming attacks are taken into consideration, which have a significant impact on control performances. A set of adequate criteria is established in the context of linear matrix inequalities by employing the mode-dependent time-varying piecewise Lyapunov-Krasovskii functional and the integral inequalities, guaranteeing that the undertaken system accomplishes the global exponential stability and meet a specific H∞ performance level. Following that, by means of the deduced adequate criteria, the values of the controller and observer gain are computed. At last, the efficacy and applicability of the derived theoretical findings are demonstrated through two practical examples.

Tube-based MPC strategy for load frequency control of multi-area interconnected power system with HESS

With the rapid development of power generation technology and the increasing demand from power users, multi-area interconnected power systems have become a development trend. This article proposes a new robust tube-based model predictive control strategy (MPC) for multi-area interconnected power systems with hybrid energy storage systems (HESS), based on the application of HESS to optimize the performance of load frequency control in power systems. The proposed strategy is mainly based on a new robustness constraint, which effectively handles uncertain disturbances within interconnected power system by compressing the disturbance invariant set. This strategy effectively alleviates the problems caused by inconsistency in multi-area interconnection modes. In addition, a sufficient stability criterion is derived to ensure the robust stability of the system, even under uncertain disturbances. By modeling a four-area interconnected power system with HESS, the frequency fluctuations of different methods are compared and discussed in each area. Based on the constructed software-in-the-loop setup, the effectiveness and feasibility of the proposed strategy are verified by experiments.

Virtual feedback control of interior road noise based on headrest loudspeakers

This paper analyzes the factors that affect the noise reduction performance of feedback control based on remote microphone technique (RMT) and how these factors can be exploited to improve the control performance. First, simulations were conducted to compare the noise reduction performance of employing feedback control directly at the virtual microphone (located at the ear position) and on the physical microphone (located on the headrest), as well as the RMT-based feedback control system. Then the impact of the delay of the virtual secondary path and the coherence between physical and virtual signals on the noise reduction performance of the RMT-based feedback control system was analyzed. It is found that the noise reduction performance can be improved by reducing the delay of the virtual secondary path or increasing the number of physical microphones. Finally, experiments of a dual-channel control system conducted inside an electric car cabin demonstrate that the feedback control strategy based on RMT achieves a binaural noise reduction of 3.4 dBA when employing 4 physical microphones to estimate sound pressure at the ear positions. This approach achieves similar performance but is more cost-effective than a feedforward system using multiple reference sensors.

Analysis and experimental evaluation of an inertial eddy current damper for vibration control

Electromagnetic damping features non-contact operation, easy adjustability, convenient processing, and high energy consumption. Combined with the damping efficiency enhancement effect of the inertial system, it is conducive to further improving its damping effect. This paper proposes an inertial mass damper, named the Inertial Eddy Current Damper (IECD). The proposed IECD has the advantage of adjustability, meaning that the eddy current damping force can be adjusted by regulating the current in the coil. First, the mechanical model of the IECD was proposed through theoretical methods. Secondly, performance tests were conducted to verify the accuracy of the proposed mechanical model. Finally, numerical models of structures employing various applications of the IECD were established to conduct seismic response analysis. The results show that the proposed mechanical model effectively reflects the actual performance of the IECD. When used for energy dissipation and vibration reduction, the IECD demonstrates superior control performance in terms of displacement and velocity compared to the LVD, and the switch control strategy can address the issue of poor acceleration control performance. Combining the IECD with isolation bearings not only further reduces the seismic response of the superstructure but also enhances the safety of the isolation bearings, preventing excessive deformation.

Nonlinear RISE based integral reinforcement learning algorithms for perturbed Bilateral Teleoperators with variable time delay

In view of unknown Bilateral Teleoperators, the simultaneous satisfaction of not only synchronous control problem between two sides, but also optimal control performance under time-varying delays and external disturbance is a difficult task. Initially, in order to address this challenge, we proposed two control approaches including On-Policy and Off-Policy strategies after employing the sliding variable to reduce the order of dynamic model. Additionally, since the development of the unification between synchronous control problem and optimal control performance, it requires the consideration of discount factor addition to guarantee the bound of the infinite horizon cost function. In order to handle the dynamic uncertainties and the exponential cost function, by fully considering data collection technique in model-free On-Policy and Off-Policy strategies, Reinforcement Learning control schemes are proposed to guarantee the optimality effectiveness. Consequently, the Robust Integral of the Sign of the Error term is integrated into two proposed optimal control frames to improve the synchronous control performance and estimation capability of dynamic uncertainties. Moreover, the convergence of control policies and synchronous control problem of two proposed control frames are rigorously analyzed by Lyapunov stability theory. Finally, simulation results and the comparisons with the existing controller demonstrate the effectiveness of two proposed control frameworks.