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Challenges and unpredictability pose significant barriers to maintaining stable operation in rotorcraft unmanned aerial vehicle (UAV) systems. The quadrotor model, as a type of rotorcraft UAV, is currently recognized as an exceptionally adaptable flying machine, serving various purposes in both civilian and military domains. However, it is a complex and highly non-linear system, and its effectiveness may suffer when subjected to external disturbances or uncertainties in its design. Using a technique known as active force control (AFC), novel intelligent control methods for quadrotors were presented in this study in order to enhance their ability to reject disturbances and uncertainties while maintaining system stability. To achieve this, a designed PID controller and an AFC technique were combined in a hybrid way into a single control strategy. To automatically estimate control parameters, the iterative learning algorithm (ILA), artificial neural network, and Adaptive Neuro-Fuzzy Inference System (ANFIS) were utilized, and the proposed control schemes became known as the PID-ILAFC, PID-NNAFC, and PID-ANFISAFC. To assess the effectiveness and resilience of the proposed control approaches, various perturbation representatives, including sinusoid and Drydenturbulence models, were employed along with uncertainties. The performance of the suggested control methods was evaluated using integral square error. Findings reveal an average decrease of over 55% in settling time across most scenarios. Concerning trajectory tracking accuracy, the integrated control strategies demonstrated remarkable efficacy in following the intended paths of the quadrotor, effectively mitigating the effects of applied wind gusts and uncertainties.

Models of rotorcraft are classified into different categories, and today, the twin-rotor helicopter is considered one of the most versatile flying machines nowadays and has attracted many researchers from different disciplines. However, it is a multivariate, highly nonlinear, and strongly coupled model. Also, its performance could be further compromised when it is operated under disturbances or uncertainties. This study presents intelligent control schemes based on a technique called active force control (AFC) employing the iterative learning (IL) algorithm and fuzzy logic. Various types of disturbances including the sinusoidal wave, pulsating, and Dryden wind gust model disturbances have been introduced to test the feasibility of the suggested control schemes. Simulated findings show that the proposed AFC-based schemes are effective against disturbances while maintaining system stability. Results indicate that the PID-ILAFC scheme enhances the performance of the twin-rotor helicopter by approximately 70% for pitching motion and almost 30% for yawing motion, under different disturbances.

Data acquisition (DAQ) is the link between the physical phenomena of any dynamic system and the computer. This device supplies the associated research applications with high demonstration I/O, industry primary innovations, and lower performance gains in software. This paper presents the design of an anti-lock braking device for ground vehicles within the laboratory and low-cost assembly compared to other designs and the superior control performance of the proposed technique. It uses DAQ for calculation and LabVIEW simulation software to analyse, display, and store data in real-time. The experimental set-up with AC motor assembled with the lower cast iron wheel, rubber vehicle wheel, brake pedal, and attached with the hydraulic actuator. The DAQ operated as a link between the computer and the apparatus to demonstrate the performance of the suggested dynamic system. The intelligent active force control technique was merged into the control system and the physical performance of the test rig was presented digitally in LabVIEW software. The results demonstrate the efficiency and favourable reaction of the proposed control approach with the test rig offering superior tracking of 0.21 slip ratio and steady braking on dry roads with a 12.49% reduction in braking distance when compared to other approaches.

Among the various types of rotorcraft unmanned aerial vehicles (UAVs), the quadrotor is currently one of the most versatile flying machines. However, it is an under-actuated, highly non-linear coupling system. It is also sensitive to external disturbances and uncertainties while tracking certain paths, which can affect its performance and may cause undesirable movements that sometimes lead to the failure of the entire system. This work introduces an innovative hybrid control scheme for a quadrotor model to reject different forms of external disturbances while ensuring stability during trajectory tracking. The proposed control structure incorporates an active force control (AFC) strategy with a proportional-integral-derivative (PID) controller, tuned using the genetic algorithm (GA) method, known as the (PIDAFC-GA) scheme. In addition, a sensitivity analysis of the effect of utilising the partial-to-total output of the AFC signal was investigated. The hybrid PID-AFC-GA controller gives superior disturbance rejection efficacy than the other proposed methods.

This paper presents the development of the system identification (SI) for the highly nonlinear piezoelectric patch actuator. The transfer function is determined by using the nonlinear least square (NLS) method after the direct measurements of input-output data are taken from the actuator that is installed on a well-equipped platform. The results were validated to ensure that the transfer function derived fits well with the experimental output.

This paper presents a mechatronic development of a new mobile robot platform for autonomous on-the-road navigation. It uses sensor fusion involving the camera, laser range finder (LRF) and odometry to guide and find the free collision path during navigation. The embedded controller system has been deliberately developed to integrate the mechanical parts with the electronics and software algorithms. Microsoft Visual C# and MATLAB are linked together to perform the road recognition calculation via the proposed algorithms. The main processing program in C# is later extended to MATLAB environment for image, signal processing and calculation based on data from the LRF and camera. The linking between C# and MATLAB is done using COM automation server and the data in the client C# program passes it to MATLAB and vice versa.

This work presents a simulation study on a novel micro thermal sensor to evaluate a different range of temperature. Micro Electro Mechanical Systems (MEMS) technology is an interesting field in mechanics andmetrology. In this work, a capacitive micro thermal sensor based on tip deflection of Functionally Graded Micro-beam (FGM) was designed. The thermo-electric mechanical equations based on Euler-Bernoulli beam theory were derived and solved using step-by-step linearization method. The increase in temperature was expressed with respect to the changes in the capacitance. The beam deflections were compared with the existing results showing good conformity with the highest error obtained as 4.3% at 65oC.

Patients with hand tremors may find routine activities such as writing and holding objects affected. In response to this problem, an active control technique has been examined in order to lessen the severity of tremors. In this article, an online method of a hybrid proportional–integral control with active force control strategy for tremor attenuation is presented. An intelligent mechanism using iterative learning control is incorporated into the active force control loop to approximate the estimation mass parameter. Experiments were conducted on a dummy hand model placed horizontally in a tremor test rig. When activated by a shaker in the vertical direction, this resembles a postural tremor condition. In the proportional–integral plus active force control, a linear voice coil actuator is used as the main active tremor suppressive element. A sensitivity analysis is presented to investigate the robustness of the proposed controller in a real-time control environment. The findings of this study demonstrate that the intelligent active force control and iterative learning controller show excellent performance in reducing tremor error compared to classic pure proportional, proportional–integral and hybrid proportional–integral plus active force control controllers.

Currently, most of modern sprayers are equipped with suspensions for improving the uniformity of spray application in the field. Therefore, this paper represents the possibility of applying active force control (AFC) technique for the control of a spray boom structure undesired roll movement through a simulation analysis. The dynamic model of the spray boom was firstly defined and an AFC-based scheme controller was designed and simulated in MATLAB environment. Artificial neural network (ANN) is incorporated into the AFC scheme to tune the proportional-derivative (PD) controller gains andcompute the spray boom estimated mass moment of inertia. The training of both ANN with multi layer feed forward structure was done using Levenberg-Marquardt(LM) learning algorithm. To evaluate the AFC-ANN control system robustness, various types of disturbances and farmland terrain profileshave been used to excite the spray boom. The results of the study demonstrated that the AFC-based method offers a simple and effective computation compared to the conventional proportional-integral-derivative (PID) control technique in attenuating the unwanted spray boom roll oscillation or vibration. The AFC-ANN scheme is found to exhibit superior performance for different proposed terrain profilesin comparison to the AFC-PD and pure PD counterparts.

Main steam temperature is one of the most important parameters in coal fired power plant. Main steam temperature is often describe as non-linear and large inertia with long dead time parameters. This paper present main steam temperature modeling method using neural network with Levenberg-Marquardt learning algorithm. The result of the simulation showed that the main steam temperature modeling based on neural network with Levenberg-Marqurdt learning algorithm is able to replicate closely the actual plant behavior. Generator output, main steam flow, main steam pressure and total spraywater flow are proven to be the main parameters affected the behavior of main steam temperature in coal fired power plant.

Distribution pattern of spray boom in fields is affected by several parameters which one of the important reasons is horizontal and vertical vibrations because of unevenness surfaces. Spray boom movements lead to decrease of spread efficiency and crop yield. Generally, active suspension is employed to control and attenuate the vibration of sprayer booms because these suspensions reduce the high frequency vibration of spray booms thanks to irregularities soil. In this research, a proportional-integral-derivative controller with active force control is used to remove undesired rolling of spray boom. Simulation results depict that the proposed scheme is more effective and accurate than PID control only scheme. The AFC based scheme shows the robustness and accuracy compared to the PID controller.

This paper presents a novel approach to control a 3-RRR (revolute-revolute-revolute) planar parallel manipulator applying an active force control (AFC) strategy. A PID-based computed torque controller (CTC) was first designed and developed to demonstrate the basic and stable response of the manipulator in order to follow a prescribed trajectory. Then, the AFC part was incorporated into the control scheme in series with the CTC (AFC-CTC) in a cascade form. Performance of the system was demonstrated by the computer simulation results. By using the AFC method, the design of trajectory tracking controller can be conducted based on a precise model of the system. The overall tracking performance was improved with using AFC scheme in presence of known or unknown disturbances. Results clearly illustrate the robustness and effectiveness of the proposed AFC-based scheme as a robust disturbance rejecter compared to the conventional CT controller.

In this paper, a piezo actuated micro robot with active force control (AFC) capability is modelled and simulated for an in-pipe application. A mathematical model that describes the dynamic characteristics of the micro robot is first presented. The dynamic response of the robot system subjected to different input excitations is then investigated by initially considering a conventional proportional-integral-derivative (PID) controller to perform a trajectory tacking task. Subsequently, a robust AFC-based controller is serially added to the PID controller, the primary aim of which is to reject the unwanted disturbances due to frictional forces in the pipe. The control system is tuned so that an accurate trajectory tracking control is achieved. The performance of the control system under different loading and operating conditions is evaluated through a rigorous simulation study. A sliding mode controller (SMC) was also included to provide another means of comparing the system performances apart from the pure PID control scheme. The obtained results clearly demonstrate the robust trajectory tracking performance of the proposed AFC-based micro robot system in spite of the negative effects of the external disturbances.

A robust control algorithm for tracking a wheeled mobile robot navigating in a pre-planned path while passing through the road’s roundabout environment is presented in this article. The proposed control algorithm is derived from both the kinematic and dynamic modelling of a non-holonomic wheeled mobile robot that is driven by a differential drive system. The road’s roundabout is represented in a grid map and the path of the mobile robot is determined using a novel approach, the so-called laser simulator technique within the roundabout environment according to the respective road rules. The main control scheme is experimented in both simulation and experimental study using the resolved-acceleration control and active force control strategy to enable the robot to strictly follow the predefined path in the presence of disturbances. A fusion of the resolved-acceleration control–active force control controller with Kalman Filter has been used empirically in real time to control the wheeled mobile robot in the road’s roundabout setting with the specific purpose of eliminating the noises. Both the simulation and the experimental results show the capability of the proposed controller to track the robot in the predefined path robustly and cancel the effect of the disturbances.

A novel technique called laser simulator approach for visibility search graph-based path planning has been developed in this article to determine the optimum collision-free path in unknown environment. With such approach, it is possible to apply constraints on the mobile robot trajectory while navigating in complex terrains such as in factories and road environments, as the first work of its kind. The main advantage of this approach is the ability to be used for both global/local path planning in the presence of constraints and obstacles in unknown environments. The principle of the laser simulator approach with all possibilities and cases that could emerge during path planning is explained to determine the path from initial to destination positions in a two-dimensional map. In addition, a comparative study on the laser simulator approach, A* algorithm, Voronoidiagram with fast marching and PointBugalgorithms was performed to show the benefits and drawbacks of the proposed approach. A case study on the utilization of the laser simulator in both global and local path planning has been applied in a road roundabout setting which is regarded as a complex environment for robot path planning. In global path planning, the path is generated within a grid map of the roundabout environment to select the path according to the respective road rules. It is also used to recognize the real roundabout from a sequence of images during local path planning in the real-world system. Results show that the performance of the proposed laser simulator approach in both global and local environments is achieved with low computational and path costs, in which the optimum path from the selected start position to the goal point is tracked accordingly in the presence of the obstacles.

A road feature extraction based mapping system using a sensor fusion technique for mobile robot navigation in road environments is presented in this paper. The online mapping of mobile robot is performed continuously in the road environments to find the road properties that enable the robot to move from a certain start position to pre-determined goal while discovering and detecting the roundabout. The sensors fusion involving laser range finder, camera and odometry which are installed in a new platform, are used to find the path of the robot and localize it within its environments. The local maps are developed using camera and laser range finder to recognize the roads borders parameters such as road width, curbs and roundabout. Results show the capability of the robot with the proposed algorithms to effectively identify the road environments and build a local mapping for road following and roundabout.

Recent developments in open source hardware and software platforms (Android, Arduino, Linux, OpenCV etc.) have enabled rapid development of previously expensive and sophisticated system within a lower budget and flatter learning curves for developers. Using these platform, we designed and developed a Java-based 3D robotic simulation system, with graph database, which is integrated in online and offline modes with an Android-Arduino based rubbish picking remote control car. The combination of the open source hardware and software system created a flexible and expandable platform for further developments in the future, both in the software and hardware areas, in particular in combination with graph database for artificial intelligence, as well as more sophisticated hardware, such as legged or humanoid robots.

A resolved acceleration control (RAC) and proportional-integral active force control (PIAFC) is proposed as an approach for the robust motion control of a mobile manipulator (MM) comprising a differentially driven wheeled mobile platform with a two-link planar arm mounted on top of the platform. The study emphasizes on the integrated kinematic and dynamic control strategy in which the RAC is used to manipulate the kinematic component while the PIAFC is implemented to compensate the dynamic effects including the bounded known/unknown disturbances and uncertainties. The effectivenss and robustness of the proposed scheme are investigated through a rigorous simulation study and later complemented with experimental results obtained through a number of experiments performed on a fully developed working prototype in a laboratory environment. A number of disturbances in the form of vibratory and impact forces are deliberately introduced into the system to evaluate the system performances. The investigation clearly demonstrates the extreme robustness feature of the proposed control scheme compared to other systems considered in the study.