An Intelligent Position-Tracking Controller for Constrained Robotic Manipulators Using Advanced Neural Networks

Nowadays, robots have become a key labor force in industrial manufacturing, exploring missions as well as high-tech service activities. Possessing intelligent robots for such the work is an understandable reason. Adoptions of neural networks for excellent control accuracies of robotic control systems that are restricted in physical constraints are practical challenges. This chapter presents an intelligent control method for position tracking control problems of robotic manipulators with output constraints. The constrained control objectives are transformed to be free variables. A simple yet effective driving control rule is then designed to force the new control objective to a vicinity around zeros. To suppress unexpected systematic dynamics for outstanding control performances, a new neural network is employed with a fast-learning law. A nonlinear disturbance observer is then used to estimate the neural estimation error to result in an asymptotic control outcome. Robustness of the closed loop system is guaranteed by the Lyapunov theory. Effectiveness and feasibility of the advanced control method are validated by comparative simulation

An integrated intelligent nonlinear control method for a pneumatic artificial muscle

This paper proposes an advanced position-tracking control approach, referred to as an integrated intelligent nonlinear controller, for a pneumatic artificial muscle (PAM) system. Due to the existence of uncertain, unknown, and nonlinear terms in the system dynamics, it is difficult to derive an exact mathematical model with robust control performance. To overcome this problem, the main contributions of this paper are as follows. To actively represent the behavior of the PAM system using a grey-box model, neural networks are employed as equivalent internal dynamics of the system model and optimized online by a Lyapunov-based method. To realize the control objective by effectively compensating for the estimation error, an advanced robust controller is developed from the integration of the designed networks, and improvement of the sliding mode and backstepping techniques. The convergences of both the developed model and the closed-loop control system are guaranteed by Lyapunov functions. As a result, the overall control approach is capable of ensuring the system's performance with fast response, high accuracy, and robustness. Real-time experiments are carried out in a PAM system under different conditions to validate the effectiveness of the proposed method

Self-Learning Low-Level Controllers

Humanoid robots are complicated systems both in hardware and software designs. Furthermore, the robots normally work in unstructured environments at which unpredictable disturbances could degrade control performances of whole systems. As a result, simple yet effective controllers are favorite employed in low-level layers. Gain-learning algorithms applied to conventional control frameworks, such as Proportional-Integral-Derivative, Sliding-mode, and Backstepping controllers, could be reasonable solutions. The adaptation ability integrated is adopted to automatically tune proper control gains subject to the optimal control criterion both in transient and steady-state phases. The learning rules could be realized by using analytical nonlinear functions. Their effectiveness and feasibility are carefully discussed by theoretical proofs and experimental discussion

A Nonlinear Sliding Mode Controller of Serial Robot Manipulators with Two-level Gain-learning Ability

This article presents a learning robust controller for high-quality position tracking control of robot manipulators. A basic time-delay estimator is adopted to effectively approximate the system dynamics. A low-level control layer is structured from the control error as an indirect control objective using new nonlinear sliding-mode synthetization. To realize the control objective with desired transient time, a robust sliding mode control signal is then designed based on the obtained estimation results in a high-level control layer. To promptly suppress unpredictable disturbances, adaptation ability is integrated to the controller using two-level gain-learning laws. Reaching gains and sliding gain are automatically tuned for asymptotic control performance. Effectiveness of the designed controller is concretely confirmed by the Lyapunov-based stability criterion, comparative simulations, and real-time experiments

A Precise Neural-disturbance Learning Controller of Constrained Robotic Manipulators

An adaptive robust controller is introduced for high-precision tracking control problems of robotic manipulators with output constraints. A nonlinear function is employed to transform the constrained control objective to new free variables that are then synthesized using a sliding-mode-like function as an indirect control mission. A robust nonlinear control signal is derived to ensure the boundedness of the main control objective without violation of physical output constraints. The control performance is improved by adopting a neural-network model with conditioned nonlinear learning laws to deal with nonlinear uncertainties and disturbances inside the system dynamics. A disturbance-observer-based control signal is additionally properly injected into the neural nonlinear system to eliminate the approximation error for achieving asymptotically tracking control accuracy. Performance of the overall control system is validated by intensive theoretical proofs and comparative simulation results

Optimization of Rough Self-Propelled Rotary Turning Parameters in terms of Total Energy Consumption and Surface Roughness

The self-propelled rotary tool turning (SPRT) process is an economic and effective solution for machining difficult-to-cut materials. This work optimized SPRT parameters, including the inclination angle (A), depth of cut (D), feed rate (f), and turning speed (V) to decrease the total energy consumption (TE) and surface roughness (SR). The turning experiments of the hardened AISI 4150 steel were executed to obtain the experimental data, while the regression method was applied to develop the TE and SR correlations. The entropy method and quantum-behaved particle swarm optimization (QPSO) were utilized to select the weights and optimal factors. The results indicated that the optimal A, D, f, and V were 34 deg., 0.40 mm, 0.47 mm/rev., and 177 m/min, respectively, while the TE and SR were saved by 9.7% and 35.4%, respectively. The f and V were found to be the most effective parameters, followed by the D and A. The outcomes provide valuable data to determine optimal SPRT factors for minimizing energy consumption and maximizing machining quality.The optimizing technique could be applied to solve other issues for different SPRT operations

Multi-Response Optimization of the Flat Burnishing Process with a High-Stiffness Tool in terms of Surface Characteristics

In this work, the surface roughness (SR), surface hardness (SH), and the thickness of the affected layer (TL) of the multi-roller flat burnishing process are optimized.The parameter inputs are the tool rotational speed (S), burnishing depth (D), and feed rate (f). The flat burnishing tool having three rollers was utilized to facilitate burnishing trials. The Kriging models of performances are proposed regarding inputs.The CRITIC method and Crow Search Algorithm (CSA) were employed to select weights and optimality. The optimizing outcomes indicated that the optimal values of the S, f, and D were 912 rpm, 150 mm/min, and 0.12 mm, respectively. The improvements in the SR, SH, and TL were 33.3%, 26.9%, and 48.6%, respectively. The SR was primarily influenced by the f, followed by the D and S, respectively. The SH and TL were primarily influenced by the D, followed by the S and f, respectively. The optimal data could be applied to the practical multi-roller burnishing process to improve surface properties for flat surfaces. The Kriging models and CSA could be efficiently utilized to solve complex issues for burnishing operations and other machining processes

Rice farmers' perception and determinants of climate change adaptation measures: a case study in Vietnam

The study used Mann Kendall's and Sen's slope tests to elicit rice farmers' perceptions of climate change due to extreme weather occurrences and compared them to hydro-meteorological data. According to the findings, temperatures increased by 0.4 degrees during the last 35 years. While rainfall has increased, the pattern has been difficult to discern. The test results corroborated farmers' perceptions of increased heat spells, but rainfall frequency and intensity vary and are difficult to anticipate. Three adaptation strategies are frequently employed in the Nong Cong district: adjusting the seasonal calendar to alter transplanting and harvesting timing; increasing fertiliser and pesticide application; and changing variety to short-time kinds. Due to the interdependence of adaption techniques, the study used a multivariate probit model. The regression findings indicated that several relevant variables influence the decision to apply adaption methods. Numerous policy ideas for enhancing adaptation to climate change can be derived from the results of this study. District governments must improve their capacity to forecast weekly weather and train how to adapt production to climate change.Le Phuong Nam (Viet Nam National University of Agriculture (VNUA)), Nguyen Dang Que (National Academy of Public Administration (NAPA)), Nguyen Van Song (Viet Nam National University of Agriculture (VNUA)), Tran Thi Hoang Mai (Vinh University (VU)), Nguyen Thi Minh Phuong (Vinh University (VU)), Nguyen Thi Xuan Huong (Viet Nam National University of Forestry (VNUF)), Nguyen Cong Tiep (Viet Nam National University of Agriculture (VNUA)), Tran Ba Uan (Dien Bien Technical Economic College)Includes bibliographical references

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke