Search Results (32)

This paper presents a model predictive controller (MPC) based on dynamic models generated using the Particle Swarm Optimisation method for accurate motion control of a pneumatic artificial muscle (PAM) for application in rehabilitation robotics. The physical compliance and lightweight nature of PAMs make them desirable for use in the field but also introduce nonlinear dynamic properties which are difficult to accurately model and control. As well as the MPC, three other control systems were examined for a comparative study: a particle-swarm optimised proportional-integral-derivative controller (PSO-PID), an iterative learning controller (ILC), and classical PID control. A series of different waveforms were used as setpoints for each controller, including addition of external loading and simulated disturbance, for a system consisting of a single PAM. Based on the displacement error measured for each experiment, the PID controller performed worst with the largest error values and an issue with oscillating about the setpoint. PSO-PID performed better but still poorly compared with the other intelligent controllers, as well as still exhibiting oscillation, which is undesirable in any human–robot interaction as it can heavily impact the comfort and safety of the system. ILC performed well with rapid convergence to steady-state and low-error values, as well as mitigation of loads and disturbance; however, it performed poorly under changing frequency of input. MPC generally performed the best of the controllers tested here, with the lowest error values and a rapid response to changes in setpoint, as well as no required learning period due to the predictive algorithm. Full article

In practice, most industrial robot manipulators use PID (Proportional + Integral + Derivative) controllers, thanks to their simplicity and adequate performance under certain conditions. Normally, this type of controller has a good performance in tasks where the robot moves freely, performing movements without contact with its environment. However, complications arise in applications such as the AFP (Automated Fiber Placement) process, where a high degree of precision and repeatability is required in the control of parameters such as position and compression force for the production of composite parts. The control of these parameters is a major challenge in terms of quality and productivity of the final product, mainly due to the complex geometry of the part and the type of tooling with which the AFP system is equipped. In the last decades, several control system approaches have been proposed in the literature, such as classical, adaptive or sliding mode control theory based methodologies. Nevertheless, such strategies present difficulties to change their dynamics since their design consider only some set of disturbances. This article presents a novel intelligent type control algorithm based on back-propagation neural networks (BP-NNs) combined with classical PID/PI control schemes for force/position control in manipulator robots. The PID/PI controllers are responsible for the main control action, while the BP-NNs contributes with its ability to estimate and compensate online the dynamic variations of the AFP process. It is proven that the proposed control achieves both, stability in the Lyapunov sense for the desired interaction force between the end-effector and the environment, and position trajectory tracking for the robot tip in Cartesian space. The performance and efficiency of the proposed control is evaluated by numerical simulations in MATLAB-Simulink environment, obtaining as results that the errors for the desired force and the tracking of complex trajectories are reduced to a range below 5% in root mean square error (RMSE). Full article

An advanced driver-assistance system (ADAS) is critical to driver–vehicle-interaction systems. Driving behaviour modelling and control significantly improves the global performance of ADASs. A haptic assistive system assists the driver by providing a specific torque on the steering wheel according to the driving–vehicle–road profile to improve the steering control. However, the main problem is designing a compensator dealing with the high-level uncertainties in different driving scenarios with haptic driver assistance, where different personalities and diverse perceptions of drivers are considered. These differences can lead to poor driving performance if not properly accounted for. This paper focuses on designing a data-driven model-free compensator considering various driving behaviours with a haptic feedback system. A backpropagation neural network (BPNN) models driving behaviour based on real driving data (speed, acceleration, vehicle orientation, and current steering angle). Then, the genetic algorithm (GA) optimises the integral time absolute error (ITEA) function to produce the best multiple PID compensation parameters for various driving behaviours (such as speeding/braking, lane-keeping and turning), which are then utilised by the fuzzy logic to provide different driving commands. An experiment was conducted with five participants in a driving simulator. During the second experiment, seven participants drove in the simulator to evaluate the robustness of the proposed combined GA proportional-integral-derivative (PID) offline, and the fuzzy-PID controller applied online. The third experiment was conducted to validate the proposed data-driven controller. The experiment and simulation results evaluated the ITAE of the lateral displacement and yaw angle during various driving behaviours. The results validated the proposed method by significantly enhancing the driving performance. Full article

The design of multivariable process control systems is specially complicated when there are strong interactions between the different control loops, and even more with multiple time delays. This paper proposes an iterative design method of centralized proportional-integral-derivative (PID) controllers for stable linear systems. The methodology is based on the linear parameterization of equivalent loop transfer functions (ELTFs) for centralized control. These functions capture the dynamics of the other loops and, from a prior design, allow solving the design problem at each iteration with linear programming that shapes the Nyquist plot of the ELTFs in the frequency domain, which also avoids the need for approximations. Two optimizations are proposed: (I) maximizing integral gains by fulfilling linear robustness margins in each ELTF and (II) maximizing linear robustness margins by fulfilling minimum bandwidths in each loop. In both optimizations, static decoupling and decoupling at a frequency close to the bandwidth of each loop are included as constraints, which improves the decoupling performance and the procedure convergence. The effectiveness of the method is verified in three simulation examples (square and non-square) and a lab experimental process. The proposed designs achieve a similar or better response when compared to that achieved by other authors. Full article

The thermal vacuum test (TVT) is an important verification process in the development of spacecraft and load. There are often multiple temperature points on the device under test (DUT) that require control. The interaction among multiple channels poses a challenge for temperature control in the TVT. To solve this problem, a multi-channel Smith proportional–integral–derivative (PID) controller based on a grouping neural network (Grouping-NN) is proposed. Firstly, the mathematical derivation for a typical multi-channel temperature control model of the TVT is carried out. Then, the multi-channel interaction system is identified using a Grouping-NN to predict the output temperature of each channel by grouping the hidden layer neurons according to the number of channels. Finally, two Grouping-NNs are utilized to update the Smith predictor, and the time-delay error is fed back to the PID controller, which is used to optimize the control effect of the multi-channel interaction system under high time delay. The proposal is compared with the traditional PID controller and Smith predictor-based PID controller through simulation. The simulation results show that the proposed method has better suppression of overshooting. In addition, the algorithm is verified by controlling the temperature of six channels in a practical thermal vacuum test. Full article

Efficient control schemes for ill-conditioned systems, such as the high-purity distillation column, can be challenging and costly to design and implement. In this paper, we propose a distributed control scheme that utilizes well-designed excitation signals to identify the system. Unlike traditional systems, we found that a summation of correlated and uncorrelated signals can yield better excitation of the plant. Our proposed distributed model predictive control (MPC) scheme uses a shifted input sequence to address loop interactions and reduce the computational load. This approach deviates from traditional schemes that use iteration, which can increase complexity and computational load. We initially tested the proposed method on the linear model of a highly coupled 2 × 2 process and compared its performance with decentralized proportional-integral-derivative (PID) controllers and centralized MPC. Our results show improved performance over PID controllers and similar results to centralized MPC. Furthermore, we compared the performance of the proposed approach with a centralized MPC on a nonlinear model of a distillation column. The results for the second study also demonstrated comparable performance between the two controllers with the decentralised control slightly outperforming the centralised MPC in some cases. These findings are promising and may be of interest to practitioners that are more comfortable with tuning decentralised loops. Full article

In this paper, a new socially assistive robot (SARs) called HBS-1.2 is presented, which uses 6-ply twisted and coiled polymer (TCP) artificial muscles in its hand to perform physical tasks. The utilization of 6-ply TCP artificial muscles in a humanoid robot hand is a pioneering advancement, offering cost effective, lightweight, and compact solution for SARs. The robot is designed to provide safer human–robot interaction (HRI) while performing physical tasks. The paper explains the procedures for fabrication and testing of the 6-ply TCP artificial muscles, along with improving the actuation response by using a Proportional-Integral-Derivative (PID) control method. Notably, the robot successfully performed a vision-based pick and place experiment, showing its potential for use in homecare and other settings to assist patients who suffer from neurological diseases like Alzheimer’s disease. The study also found an optimal light intensity range between 34 to 108 lumens/m², which ensures minimal variation in calculated distance with 95% confidence intervals for robust performance from the vison system. The findings of this study have important implications for the development of affordable and accessible robotic systems to support elderly patients with dementia, and future research should focus on further improving the use of TCP actuators in robotics. Full article

Today, we are witnessing an increasing trend in the number of soft pneumatic actuator solutions in industrial environments, especially due to their human-safe interaction capabilities. An interesting solution in this frame is a vacuum pneumatic muscle actuator (PMA) with a bellow structure, which is characterized by a high contraction ratio and the ability to generate high forces considering its relatively small dimensions. Moreover, such a solution is generally very cost-effective since can be developed by using easily accessible, off-the-shelf components combined with additive manufacturing procedures. The presented research analyzes the precision positioning performances of a newly developed cost-effective bellow PMA in a closed-loop setting, by utilizing a Proportional-Integral-Derivative (PID) controller and a Linear Quadratic Regulator (LQR). In a first instance, the system identification was performed and a numerical model of the PMA was developed. It was experimentally shown that the actuator is characterized by nonlinear dynamical behavior. Based on the numerical model, a PID controller was developed as a benchmark. In the next phase, an LQR that involves a nonlinear pregain term was built. The point-to-point positioning experimental results showed that both controllers allow fast responses without overshoot within the whole working range. On the other hand, it was discovered that the LQR with the corresponding nonlinear pregain term allows an error of a few tens of micrometers to be achieved across the entire working range of the muscle. Additionally, two different experimental pneumatic solutions for indirect and direct vacuum control were analyzed with the aim of investigating the PMA response time and comparing their energy consumption. This research contributes to the future development of the pneumatically driven mechatronics systems used for precise position control. Full article

During the last decade, soft robotic systems, such as actuators and grippers, have been employed in various commercial applications. Due to the need to integrate robotic mechanisms into devices operating alongside humans, soft robotic systems concentrate increased scientific interest in tasks with intense human–robot interaction, especially for human-exoskeleton applications. Human exoskeletons are usually utilized for assistance and rehabilitation of patients with mobility disabilities and neurological disorders. Towards this direction, a fully functional soft robotic hand exoskeleton system was designed and developed, utilizing innovative air-pressurized soft actuators fabricated via additive manufacturing technologies. The CE-certified system consists of a control glove that copies the motion from the healthy hand and passes the fingers configuration to the exoskeleton applied on the affected hand, which consists of a soft exoskeleton glove (SEG) controlled with the assistance of one-axis flex sensors, micro-valves, and a proportional integral derivative (PID) controller. Each finger of the SEG moves independently due to the finger-dedicated motion control system. Furthermore, the real-time monitoring and control of the fabricated SEG are conducted via the developed software. In addition, the efficiency of the exoskeleton system was investigated through an experimental validation procedure with the involvement of healthy participants (control group) and patients, which evaluated the efficiency of the system, including safety, ergonomics, and comfort in its usage. Full article

Even as human–robot interactions become increasingly common, conventional small Unmanned Aircraft Systems (sUAS), typically multicopters, can still be unsafe for deployment in an indoor environment in close proximity to humans without significant safety precautions. This is due to their fast-spinning propellers, and lack of a fail-safe mechanism in the event of a loss of power. A blimp, a non-rigid airship filled with lighter-than-air gases is inherently safer as it ’floats’ in the air and is generally incapable of high-speed motion. The Spherical Indoor Coandă Effect Drone (SpICED), is a novel, safe spherical blimp design propelled by closed impellers utilizing the Coandă effect. Unlike a multicopter or conventional propeller blimp, the closed impellers reduce safety risks to the surrounding people and objects, allowing for SpICED to be operated in close proximity with humans and opening up the possibility of novel human–drone interactions. The design implements multiple closed-impeller rotors as propulsion units to accelerate airflow along the the surface of the spherical blimp and produce thrust by utilising the Coandă effect. A cube configuration with eight uni-directional propulsion units is presented, together with the closed-loop Proportional–Integral–Derivative (PID) controllers, and custom control mixing algorithm for position and attitude control in all three axes. A physical prototype of the propulsion unit and blimp sUAS was constructed to experimentally validate the dynamic behavior and controls in a motion-captured environment, with the experimental results compared to the side-tetra configuration with four bi-directional propulsion units as presented in our previously published conference paper. An up to 40% reduction in trajectory control error was observed in the new cube configuration, which is also capable of motion control in all six Degrees of Freedom (DoF) with additional pitch and roll control when compared to the side-tetra configuration. Full article

The use of bodyweight unloading force control on a treadmill with therapist manual assistance for gait training imposes constraints on natural walking. It influences the patient’s training effect for a full range of natural walks. This study presents a prototype and a safety controller for a mobile rehabilitation robot (MRR). The prototype integrates an autonomous mobile bodyweight support system (AMBSS) with a lower-limb exoskeleton system (LES) to simultaneously achieve natural over-ground gait training and motion relearning. Human-centered rehabilitation robots must guarantee the safety of patients in the presence of significant tracking errors. It is difficult for traditional stiff controllers to ensure safety and excellent tracking accuracy concurrently, because they cannot explicitly guarantee smooth, safe, and overdamped motions without overshoot. This paper integrated a linear extended state observer (LESO) into proxy-based sliding mode control (ILESO-PSMC) to overcome this problem. The LESO was used to observe the system’s unknown states and total disturbance simultaneously, ensuring that the “proxy” tracks the reference target accurately and avoids the unsafe control of the MRR. Based on the Lyapunov theorem to prove the closed-loop system stability, the proposed safety control strategy has three advantages: (1) it provides an accurate and safe control without worsening tracking performance during regular operation, (2) it guarantees safe recoveries and overdamped properties after abnormal events, and (3) it need not identify the system model and measure unknown system states as well as external disturbance, which is quite difficult for human–robot interaction (HRI) systems. The results demonstrate the feasibility of the proposed ILESO-PSMC for MRR. The experimental comparison also indicates better safety performance for the ILESO-PSMC than for the conventional proportional–integral–derivative (PID) control. Full article

In this study, we propose a virtual reality biphasic separator methodology in an immersive industrial environment. It allows the training of students or engineers in process and automatic control. On the other hand, the operating performance of a biphasic separator requires advanced automatic control strategies because this industrial process has multivariable and nonlinear characteristics. In this context, the virtual biphasic separator allows the testing of several control techniques. The methodology, involving the immersive virtualization of the biphasic separator, includes three stages. First, a multivariable mathematical model of the industrial process is obtained. The second stage corresponds to virtualization, in which the 3D modelling of the industrial process is undertaken. Then, the process dynamic is captured by the plant model implemented, in the software Unity. In the third stage, the control strategies are designed. The interaction between the virtual biphasic separator and the control system is implemented using shared variables. Three control strategies are implemented and compared to validate the applicability: a classic control algorithm, namely, the proportional integral derivative (PID) control method, as well as two advanced controllers—a numerical controller and model predictive control (MPC). The results demonstrate the virtual separator’s usability regarding the operating performance of the virtual biphasic separator, considering the control techniques implemented. Full article

Multiloop proportional-integral-derivative (PID) controllers are widely used for controlling multivariable processes due to their understandability, simplicity and other practical advantages. The main difficulty of the methodologies using this approach is the fact that the controllers of different loops interact each other. Thus, the knowledge of the controllers in the other loops is necessary for the evaluation of one loop. This work proposes an iterative design methodology of multiloop PID controllers for stable multivariable systems. The controllers in each step are tuned using single-input single-output (SISO) methods for the corresponding effective open loop process (EOP), which considers the interaction of the other loops closed with the controllers of the previous step. The methodology uses a frequency response matrix representation of the system to avoid process approximations in the case of elements with time delays or complicated EOPs. Consequently, different robustness margins on the frequency domain are proposed as specifications: phase margin, gain margin, phase and gain margin combination, sensitivity margin and linear margin. For each case, a PID tuning method is described and detailed for the iterative methodology. The proposals are exemplified with two simulations systems where the obtained performance is similar or better than that achieved by other authors. Full article

This study proposes the flow and temperature controllers of a cockpit environment control system (ECS) by implementing an optimal simplified swarm optimization (SSO) fuzzy proportional-integral-derivative (PID) control. The ECS model is considered as a multiple-input multiple-output (MIMO) and second-order dynamic system, which is interactive. In this work, we use five methods to design and compare the PID controllers in MATLAB and Simulink, including Ziegler–Nicolas PID tuning, particle swarm optimization (PSO) PID, SSO PID, and the combination of the fuzzy theory with PSO PID and SSO PID, respectively. The main contribution of this study is the pioneering implementation of SSO in a fuzzy PI/PID controller. Moreover, by adding the original gain parameters K_p, K_i, and K_d in the PID controller with delta values, which are calculated by fuzzy logic designer, we can tune the parameters of PID controllers in real time. This makes our control system more accurate, adaptive, and robust. Full article

In this investigation, self-learning salp swarm optimization (SLSSO) based proportional- integral-derivative (PID) controllers are proposed for a Doha reverse osmosis desalination plant. Since the Doha reverse osmosis plant (DROP) is interacting with a two-input-two-output (TITO) system, a decoupler is designed to nullify the interaction dynamics. Once the decoupler is designed properly, two PID controllers are tuned for two non-interacting loops by minimizing the integral-square-error (ISE). The ISEs for two loops are obtained in terms of alpha and beta parameters to simplify the simulation. Thus designed ISEs are minimized using SLSSO algorithm. In order to show the effectiveness of the proposed algorithm, the controller tuning is also accomplished using some state-of-the-art algorithms. Further, statistical analysis is presented to prove the effectiveness of SLSSO. In addition, the time domain specifications are presented for different test cases. The step responses are also shown for fixed and variable reference inputs for two loops. The quantitative and qualitative results presented show the effectiveness of SLSSO for the DROP system. Full article