Control Policies Research Articles

Are educational aspirations associated with susceptibility to smoking, e-cigarette use, and smokeless tobacco use in adolescence?

The early socio-economic differences in smoking build on the interplay between individual-, family-, peer-, and school-related factors. The present study aimed to add knowledge to this by examining susceptibility to smoking (S-SM), electronic cigarette (e-cigarette) use (S-EC), and smokeless tobacco (snus) use (S-SN) by educational aspirations in a country with advanced tobacco control policies. National cross-sectional School Health Promotion study survey was conducted among 8.-9. grade students (av. 15-year-olds) in 2017 with no prior smoking (n = 47589), e-cigarette use (n = 49382), or snus use (n = 53335). Gender-stratified, age-adjusted multilevel logistic regression analyses with S-SM, S-EC, and S-SN were considered as outcomes and student- and school-level (aggregated) factors were considered as independent variables. The highest prevalence was observed for S-EC (girls 29%, boys 35%), followed by S-SM (16%, 15%) and S-SN (10%, 16%). Compared to those planning for general upper secondary education, S-SM was the highest for those without educational aspirations (OR = 1.20, 95% CI = 1.04-1.40), S-EC for those planning for vocational education [1.15 (1.05-1.25)], and S-SN for those planning for extra year/discontinuation [1.65 (1.04-2.60)] among girls. Among boys, both S-SM [1.37 (1.23-1.52)] and S-EC [1.19 (1.09-1.29)] were the highest among those planning for vocational education, with no clear associations with S-SN. Current other tobacco/e-cigarette use [OR range 1.27-8.87], positive attitude towards product use in one's age group [3.55-6.63], and liking school [0.58-0.68] consistently associated with susceptibility. Students not planning for academically oriented education had higher susceptibility to different nicotine products. High S-EC warrants monitoring to strengthen policy evaluation and prevention.

Risk factors of pulmonary tuberculosis in Indonesia: A case-control study in a high disease prevalence region.

Tuberculosis (TB) remains one of the most widespread infectious diseases in the world, with high mortality and morbidity rates, contributing to a significant disease burden, particularly in developing countries. The aim of this study was to determine the risk factors of TB among the population aged 17 years (late adolescent) and above in one of the high TB prevalence countries, Indonesia. A case-control study was conducted in Banda Aceh, Indonesia, involving newly diagnosed pulmonary TB patients who visited one of the eleven community health centers in Banda Aceh. The controls were individuals matched by age and sex who lived near the cases and had no TB infection. A total of 16 risk factors were assessed. Hierarchical multivariable logistic regression models were used to examine the association between the risk factors and the occurrence of TB. A total of 196 cases and 196 controls were included in the study. Multivariable logistic regression analysis identified 11 independent predictors for the occurrence of TB after controlling possible confounders. Patients who lived in houses with insufficient light exposure were 77 times more likely to develop TB compared to those living in houses with sufficient light exposure (adjusted odds ratio (AOR): 77.69; 95%CI: 27.09, 222.79). The study also showed that people who had close contact with TB patients (AOR: 25.39; 95%CI: 2.10, 306.52), had poor knowledge of TB (AOR: 24.2; 95%CI: 6.89, 85.17), had comorbidities (AOR: 4.49; 95%CI: 1.35, 14.89), insufficient food utilization (AOR: 3.41; 95%CI: 1.51, 7.71), negative preventive behavior (AOR: 3.39; 95%CI: 1.49, 7.72), low education level (AOR: 3.08; 95%CI: 1.26, 7.55), and insufficient housing humidity (AOR: 2.89; 95%CI: 1.18, 7.12) were associated with increased the odds of having TB. Being employed, having income above minimum wage, and having good nutritional intake were protective factors for developing TB. In conclusion, the determinants of TB among people aged 17 years and older are a clear indication of the need for TB prevention programs targeting not only individual risk factors but also environmental risk factors, particularly providing adequate housing conditions. This study provided useful information that might help to develop and adopt effective policies for TB control in Indonesia.

Long-term trends and probability distribution functions of air pollutant concentrations in the megacity of São Paulo

Air quality conditions in many urban areas have improved in the last decades as a consequence of air pollution control policies and regulations. Mitigation strategies have been successful in reducing the concentration of primary pollutants like inhalable particulate matter (PM10), but the control of secondary pollutants like tropospheric ozone (O3) is still challenging in megacities like the Metropolitan Area of São Paulo (MASP) in Brazil. To support the development of effective mitigation strategies, it is crucial to characterize the statistical behavior of air pollutant concentrations and its long-term evolution. Probability Density Functions (PDF) can be useful to model site-specific air quality conditions, providing estimates for the frequency of extreme pollution events and exceedance of air quality standards. The current study aims to characterize which PDF model better fits and characterizes the variability of PM10 and O3 concentrations in the MASP. For that, daily maximum moving average concentrations were analyzed between 2000 and 2023, characterizing the long-term trends and the frequency of exceedance of air quality standards. PM10 concentrations followed a lognormal PDF, with an expected value of 31 ± 15 µg.m-3. O3 followed a Gamma PDF, with an expected value of 68 ± 25 µg.m-3. A consistent long-term decrease was observed for PM10 (-1.04 ± 0.09 µg.m-3.yr-1), while O3 showed an increasing trend of 0.51 ± 0.04 µg.m-3.yr-1 in the summer. In recent years (2021-2023), the probability of exceedance of the World Health Organization guideline was 17.4 and 11.0%, respectively, for PM10 and O3. In 2020, a statistically significant increase in O3 expected values was observed, possibly associated with changes in the emission patterns of precursors due to the mobility restrictions imposed by the COVID-19 pandemic.

Explainable data-driven Q-learning control for a class of discrete-time linear autonomous systems

Explaining what a reinforcement learning (RL) control agent learns play a crucial role in the safety critical control domain. Most of the approaches in the state-of-the-art focused on imitation learning methods that uncover the hidden reward function of a given control policy. However, these approaches do not uncover what the RL agent learns effectively from the agent-environment interaction. The policy learned by the RL agent depends in how good the state transition mapping is inferred from the data. When the state transition mapping is wrongly inferred implies that the RL agent is not learning properly. This can compromise the safety of the surrounding environment and the agent itself. In this paper, we aim to uncover the elements learned by data-driven RL control agents in a special class of discrete-time linear autonomous systems. Here, the approach aims to add a new explainable dimension to data-driven control approaches to increase their trust and safe deployment. We focus on the classical data-driven Q-learning algorithm and propose an explainable Q-learning (XQL) algorithm that can be further expanded to other data-driven RL control agents. Simulation experiments are conducted to observe the effectiveness of the proposed approach under different scenarios using several discrete-time models of autonomous platforms.

Patient safety and healthcare quality of U.S. laboratory developed tests (LDTs) in the AI/ML era of precision medicine.

This policy brief summarizes current U.S. regulatory considerations for ensuring patient safety and health care quality of genetic/genomic test information for precision medicine in the era of artificial intelligence/machine learning (AI/ML). The critical role of innovative and efficient laboratory developed tests (LDTs) in providing accurate diagnostic genetic/genomic information for U.S. patient- and family-centered healthcare decision-making is significant. However, many LDTs are not fully vetted for sufficient analytic and clinical validity via current FDA and CMS regulatory oversight pathways. The U.S. Centers for Disease Control and Prevention's Policy Analytical Framework Tool was used to identify the issue, perform a high-level policy analysis, and develop overview recommendations for a bipartisan healthcare policy reform strategy acceptable to diverse precision and systems medicine stakeholders.

Farmer‐led badger vaccination in Cornwall: Epidemiological patterns and social perspectives

Abstract In the United Kingdom, the management of bovine tuberculosis (bTB) challenges the coexistence of people and wildlife. Control of this cattle disease is hindered by transmission of its causative agent, Mycobacterium bovis, between cattle and badgers Meles meles. Badger culling has formed an element of bTB control policy for decades, but current government policy envisions expanding badger vaccination. Farming leaders are sceptical, citing concerns that badger vaccination would be impractical and potentially ineffective. We report on a 4‐year badger vaccination initiative in an 11 km2 area which, atypically, was initiated by local farmers, delivered by scientists and conservationists, and co‐funded by all three. Participating landholders cited controversies around culling and a desire to support neighbours as their primary reasons for adopting vaccination. The number of badgers vaccinated per km2 (5.6 km−2 in 2019) exceeded the number culled on nearby land (2.9 km−2 in 2019), and the estimated proportion vaccinated (74%, 95% confidence interval [CI] 40%–137%) exceeded the 30% threshold predicted by models to be necessary to control M. bovis. Farmers were content with how vaccination was delivered, and felt that it built trust with wildlife professionals. The percentage of badgers testing positive for M. bovis declined from 16.0% (95% CI 4.5%–36.1%) at the start of vaccination to 0% (95% CI 0%–9.7%) in the final year. With neither replication nor unvaccinated controls, this small‐scale case study does not demonstrate a causal link between badger vaccination and bTB epidemiology, but it does suggest that larger‐scale evaluation of badger vaccination would be warranted. Farmers reported that their enthusiasm for badger vaccination had increased after participating for 4 years. They considered vaccination to have been effective, and good value for money, and wished to continue with it. Synthesis and applications: Although small‐scale, this case study suggests that badger vaccination can be a technically effective and socially acceptable component of bTB control. A wider rollout of badger vaccination is more likely if it is led by the farming community, rather than by conservationists or government, and is combined with scientific monitoring. Read the free Plain Language Summary for this article on the Journal blog.

Theatre Control Policy or Why After Goran Stefanovski and Dejan Dukovski, No One Is Known Outside the Borders of the Macedonian Theatre? (2010–2021)

To capture the dramatic situation in/of the contemporary drama theatre in Macedonia, I will try to draw certain characteristics of the state of the theatre by referring to several political and social conditions with us, that erode the spirit and matter of everyday life.

Research on Evolution and Recommendations of China’s PM2.5 and O3 Pollution Control Policies under the Carbon Peak and Carbon Neutrality Targets

In recent years, the issue of PM2.5 and O3 pollution in China has gradually become a hot topic in air pollution control. Powerful, precise, and clear policies are important to guide the efficient control of PM2.5 and O3 pollution. However, research on PM2.5 and O3 pollution policies is very limited. Moreover, these reports have failed to identify all the specific characteristics of the PM2.5 and O3 pollution policies in China, leading to inefficient control of PM2.5 and O3 pollution. Therefore, based on the external features and internal structures of relevant policy documents, this paper conducts a detailed bibliometric analysis to elucidate the evolution of PM2.5 and O3 pollution control policies in China. The results reveal the following: (1) The PM2.5 and O3 pollution control policies in China exhibit a characteristic of “top-down cross-level response lag”, transitioning from individual pollutant control to coordinated control of PM2.5 and O3 pollution. (2) The Ministry of Environmental Protection and the Ministry of Ecology and Environment have been the two major policy issuers in China’s government, focusing on “Technology” and “Monitoring”, respectively. However, the aforementioned policies are mainly implemented by local governments, with limited interregional cooperation but an overemphasis on enterprise pollution control and emergency systems. (3) In terms of policy instruments, supply- and environment-oriented policy instruments are predominant, whereas demand-oriented policy instruments are inadequate. Therefore, it is suggested to utilize the synergistic effect of pollution reduction and carbon mitigation to achieve the goal of the “dual carbon” strategy, enhance public participation to strengthen cooperation among diverse stakeholders, strengthen cross-regional cooperation to overcome governance barriers, and reasonably optimize the use of policy instruments to form an effective combination of policy instruments.

Optimal robust online tracking control for space manipulator in task space using off-policy reinforcement learning

This study addresses the demands for adaptability, uncertainty management, and high performance in the control of space manipulators, and the inadequacies in achieving optimal control and handling external uncertainty in task space in previous research. Based on off-policy reinforcement learning, a model-free and time-efficient method for online robust tracking control in task space is devised. To address the complexity of dynamic equations in task space, a mixed-variable approach is adopted to transform the multivariable coupled time-varying problem into a single-variable problem. Subsequently, the optimal control policy is derived with the disturbance convergence, stability, and optimality of the control method being demonstrated. This marks the first instance of achieving robust optimal tracking control in task space for space manipulators. The efficacy and superiority of the presented algorithm are validated through simulation.

Adaptive Visual Control for Robotic Manipulators with Consideration of Rigid-Body Dynamics and Joint-Motor Dynamics

A novel cascade visual control scheme is proposed to tailor for electrically driven robotic manipulators that operate under kinematic and dynamic uncertainties, utilizing an uncalibrated stationary camera. The proposed control approach incorporates adaptive weight radial basis function neural networks (RBFNNs) to learn the behaviors of the uncertain dynamics of the robot and the joint actuators. The controllers are designed to nullify the approximation error and mitigate unknown disturbances through an integrated robust adaptive mechanism. A major advantage of the proposed approach is that prior knowledge of the dynamics of the robotic manipulator and its actuator is no longer required. The controller autonomously assimilates the robot and actuator dynamics online, thereby obviating the need for fussy regression matrix derivation and advance dynamic measurement to establish the adaptive dynamic parameter update algorithm. The proposed scheme ensures closed-loop system stability, bounded system states, and the convergence of tracking errors to zero. Simulation results, employing a PUMA manipulator as a testbed, substantiate the viability of the proposed control policy.

Asynchronous iterative Q-learning based tracking control for nonlinear discrete-time multi-agent systems

This paper addresses the tracking control problem of nonlinear discrete-time multi-agent systems (MASs). First, a local neighborhood error system (LNES) is constructed. Then, a novel tracking algorithm based on asynchronous iterative Q-learning (AIQL) is developed, which can transform the tracking problem into the optimal regulation of LNES. The AIQL-based algorithm has two Q values QiA and QiB for each agent i, where QiA is used for improving the control policy and QiB is used for evaluating the value of the control policy. Moreover, the convergence of LNES is given. It is shown that the LNES converges to 0 and the tracking problem is solved. A neural network-based actor-critic framework is used to implement AIQL. The critic network of AIQL is composed of two neural networks, which are used for approximating QiA and QiB respectively. Finally, simulation results are given to verify the performance of the developed algorithm. It is shown that the AIQL-based tracking algorithm has a lower cost value and faster convergence speed than the IQL-based tracking algorithm.

Research and practice of virtualization PLC (vPLC) technology in 5G industrial network

Abstract Under the influence of information technology innovation, the empowerment of 5G and industrial internet in the steel industry has gradually expanded from production assistance to industrial control. In this paper, a deep integration network of 5G and industrial control is constructed, and a virtualization PLC(vPLC) system solution deployed on private cloud servers is proposed. Secondly, IoT protocols and security policies for cloud control are designed. Finally, the proposed scheme was successfully applied to the remote control of equipment in the steelmaking production process of a certain steel enterprise. At the same time, the real-time performance, reliability and stability of the 5G-enabled vPLC system were tested. The result indicated that the 5G industrial network can work stably with a delay of less than 10 ms and 99.99% reliability. The vPLC system ran continuously for 2000 hours and maintained stable control functions. This result can provide a research foundation and technical direction for achieving centralized control and one-stop production management in the future.

The Challenge in 2000−2009 to Phillips-Curve-Based Accounts of UK Economic Policy: Comment on Cristiano

A series of research papers that appeared from 2000 to 2009 made the case that the UK authorities in the 1950s, 1960s, and 1970s eschewed Phillips-curve-based analysis and that, consequently, the UK Great Inflation of the 1970s should not be regarded as resulting from policymakers’ pursuit of a perceived long-run inflation/unemployment trade-off. The position advanced in these 2000−2009 papers was that, instead, UK economic policy until 1979 subscribed to a nonmonetary perspective on inflation. This perspective implied UK authorities’ rejection of Phillips-curve-type trade-off analysis, but it also meant that they misjudged the importance of monetary policy in inflation control, thereby compounding the country's inflation problem. When this series of papers began to appear at the start of the 2000s, the trade-off-centered interpretation of the US Great Inflation was highly prevalent and was also being applied to the UK Great Inflation. By late in the decade, however, the case—as outlined in the 2000−2009 papers—against Phillips-curve-based accounts of historical policy conduct had gained notable acceptance among central bankers and academic researchers who discussed the UK Great Inflation. This article corrects erroneous statements that the challenge to Phillips-curve-based accounts of historical UK policymaker behavior only appeared in research starting in the 2010s.

Evaluating carbon emissions reduction compliance based on 'dual control' policies of energy consumption and carbon emissions in China

Dual control policies aim to effectively reduce energy consumption and carbon emission by setting targets for total volume and intensity control. With global climate change becoming increasingly serious. China, as a large energy-consuming and carbon-emitting country, faces a huge challenge to reduce carbon emissions. The transition from the “dual control of energy consumption” to the “dual control of carbon emissions” in China is necessary to optimize energy structures, promote energy conservation, reduce carbon emissions, and reach carbon neutrality. This study utilizes multi-regional input-output models to evaluate the results toward both goals from production-based, consumption-based, and income-based perspectives. The findings indicate that “dual control of carbon emissions” is a more precise approach than “dual control of energy consumption”. Some provinces have met the latter goal while still falling short of the former. Provinces having met their production-based energy intensity and carbon emission intensity targets, but having failed their consumption-based and income-based targets, are identified to develop a comprehensive and accurate assessment of these targets. A net outflow of embodied energy and carbon emissions is observed among provinces in less-developed central and southern regions to more-developed eastern and northern regions. Suggestions and policy implications based on these findings include establishing a comprehensive evaluation of energy and carbon intensity, considering both consumption-based and income-based perspectives, as well as facilitating enhanced cooperation among developed and developing provinces.

AoI-aware transmission control in real-time mmwave energy harvesting systems: a risk-sensitive reinforcement learning approach

The evolution of enabling technologies in wireless communications has paved the way for supporting novel applications with more demanding QoS requirements, but at the cost of increasing the complexity of optimizing the digital communication chain. In particular, Millimeter Wave (mmWave) communications provide an abundance of bandwidth, and energy harvesting supplies the network with a continual source of energy to facilitate self-sustainability; however, harnessing these technologies is challenging due to the stochastic dynamics of the mmWave channel as well as the random sporadic nature of the harvested energy. In this paper, we aim at the dynamic optimization of update transmissions in mmWave energy harvesting systems in terms of Age of Information (AoI). AoI has recently been introduced to quantify information freshness and is a more stringent QoS metric compared to conventional delay and throughput. However, most prior art has only addressed average-based AoI metrics, which can be insufficient to capture the occurrence of rare but high-impact freshness violation events in time-critical scenarios. We formulate a control problem that aims to minimize the long-term entropic risk measure of AoI samples by configuring the “sense & transmit” of updates. Due to the high complexity of the exponential cost function, we reformulate the problem with an approximated mean-variance risk measure as the new objective. Under unknown system statistics, we propose a two-timescale model-free risk-sensitive reinforcement learning algorithm to compute a control policy that adapts to the trio of channel, energy, and AoI states. We evaluate the efficiency of the proposed scheme through extensive simulations.

Convergence and Stability of Optimal Regulation via Generalized N-Step Value Gradient Learning.

In this article, the generalized N -step value gradient learning (GNSVGL) algorithm, which takes a long-term prediction parameter λ into account, is developed for infinite horizon discounted near-optimal control of discrete-time nonlinear systems. The proposed GNSVGL algorithm can accelerate the learning process of adaptive dynamic programming (ADP) and has a better performance by learning from more than one future reward. Compared with the traditional N -step value gradient learning (NSVGL) algorithm with zero initial functions, the proposed GNSVGL algorithm is initialized with positive definite functions. Considering different initial cost functions, the convergence analysis of the value-iteration-based algorithm is provided. The stability condition for the iterative control policy is established to determine the value of the iteration index, under which the control law can make the system asymptotically stable. Under such a condition, if the system is asymptotically stable at the current iteration, then the iterative control laws after this step are guaranteed to be stabilizing. Two critic neural networks and one action network are constructed to approximate the one-return costate function, the λ -return costate function, and the control law, respectively. It is emphasized that one-return and λ -return critic networks are combined to train the action neural network. Finally, via conducting simulation studies and comparisons, the superiority of the developed algorithm is confirmed.

Rent control and neighborhood income. Evidence from Vienna, Austria.: Rent control and neighborhood income.

Rent control is a highly controversial housing policy. For Vienna, Austria, we show how rent control impacts block-group income distributions as the share of residents with rental contracts subject to a cap increase. Using regression with spatial fixed effects, we find that a 1%-point increase in residents with rental contracts subject to a cap reduces the average net income of the block-group by 52.70 Euro/year. For the typical block-group containing 861 residents, this implies a reduction in total block-group income of 45,375 Euro/year. This suggests increasing numbers of lower income residents are locating in block-groups with rental caps. We further show the effect of capped rents on average income is heterogeneous across and within block-groups, with greater reductions in higher income block-groups compared to lower income block-groups. Our results demonstrate that the rent control policy in Vienna impacts residents’ location choice and the resulting spatial income distribution despite the lack of means testing.

Evaluation of Tobacco Tax Funding to Eradicate Illicit Cigarettes in Indonesia: A Qualitative Approach.

The illicit cigarette trade endangers public health because it increases access to cheaper tobacco products, hence fueling the tobacco epidemic and undermining tobacco control policies. The objective of this study was to evaluate the execution of an illicit cigarette eradication program under the jurisdiction of the local government in Indonesia. We sought to provide insights into the effectiveness of current policies and their impact on the illicit cigarette trade in line with the World Health Organization Framework Convention on Tobacco Control (WHO FCTC) protocol to eliminate illicit trade in tobacco products. We conducted semistructured interviews with key policy-makers and semistructured FGDs with consumers and small- to medium-scale cigarette manufacturers at the district level. We indentified Pasuruan and Kudus as the districts or cities with the highest proportion of DBH CHT, and Jepara and Malang as a district with a highest illicit cigarette incident. We used reflective thematic analysis to identify the important opportunities and challenges facing illicit cigarette eradication programs in the three districts. We identified four opportunities and four challenges related to illicit cigarette eradication program implementation under the local government. The opportunities for illicit cigarette eradication lie in strong central government regulatory and multisectoral authority support, consumer awareness, and local governments' commitment to tobacco supply chain control. The key challenges facing illicit cigarette eradication include ineffective public dissemination programs, rapidly changing regulatory designs, consumers' preferences for illicit products, and a lack of industrial involvement in tobacco supply chain control programs. In addition to significant budget allocation and increasing consumer awareness, local programs to eradicate illicit cigarette production require considerable evaluation to rethink the program's design and external stakeholders' engagement within the local government's scope.

SEMG-driven Hand Dynamics Estimation with Incremental Online Learning on a Parallel Ultra-Low-Power Microcontroller.

Surface electromyography (sEMG) is a State-of-the-Art (SoA) sensing modality for non-invasive human-machine interfaces for consumer, industrial, and rehabilitation use cases. The main limitation of the current sEMG-driven control policies is the sEMG's inherent variability, especially cross-session due to sensor repositioning; this limits the generalization of the Machine/Deep Learning (ML/DL) in charge of the signal-to-command mapping. The other hot front on the ML/DL side of sEMG-driven control is the shift from the classification of fixed hand positions to the regression of hand kinematics and dynamics, promising a more versatile and fluid control. We present an incremental online-training strategy for sEMG-based estimation of simultaneous multi-finger forces, using a small Temporal Convolutional Network suitable for embedded learning-on-device. We validate our method on the HYSER dataset, cross-day. Our incremental online training reaches a cross-day Mean Absolute Error (MAE) of (9.58 ± 3.89)% of the Maximum Voluntary Contraction on HYSER's RANDOM dataset of improvised, non-predefined force sequences, which is the most challenging and closest to real scenarios. This MAE is on par with an accuracy-oriented, non-embeddable offline training exploiting more epochs. Further, we demonstrate that our online training approach can be deployed on the GAP9 ultra-low power microcontroller, obtaining a latency of 1.49 ms and an energy draw of just 40.4 uJ per forward-backward-update step. These results show that our solution fits the requirements for accurate and real-time incremental training-on-device.

Sliding Mode Control Based on Reinforcement Learning for T-S Fuzzy Fractional-Order Multiagent System With Time-Varying Delays.

This article researches the sliding mode control (SMC) for fuzzy fractional-order multiagent system (FOMAS) subject to time-varying delays over directed networks based on reinforcement learning (RL), α ∈ (0,1) . First, since there is information communication between an agent and another agent, a new distributed control policy ξi(t) is introduced so that the sharing of signals is implemented through RL, whose propose is to minimize the error variables with learning. Then, different from the existed papers studying normal fuzzy MASs, a new stability basis of fuzzy FOMASs with time-varying delay terms is presented to guarantee that the states of each agent eventually converge to the smallest possible domain of 0 using Lyapunov-Krasovskii functionals, free weight matrix, and linear matrix inequality (LMI). Furthermore, in order to provide appropriate parameters for SMC, the RL algorithm is combined with SMC strategy, and the constraints on the initial conditions of the control input ui(t) are eliminated, so that the sliding motion satisfy the reachable condition within a finite time. Finally, to illustrate that the proposed protocol is valid, the results of the simulation and numerical examples are presented.