Adaptive Control Methodology Research Articles

In‐mold rheology and automated process control for injection molding of recycled polypropylene

AbstractManufacturing plastic parts with secondary feedstocks has risen to the forefront of importance in recent years. However, the variation in molecular weight and rheology of secondary feedstock can lead to inconsistent part quality. This work evaluates the effectiveness of a novel closed‐loop adaptive process control system that adjusts nozzle pressure in response to in‐mold pressure data. Five different recycled polypropylene blends, with a broad distribution of flow properties, were evaluated to determine the effectiveness of the control system at reducing processing variation. The experimental results show that the process control strategy reduced the variation within the mold, as seen by in‐mold pressure curves and calculated in‐mold viscosity values. Additionally, the parameters that control the automated process adjustments were investigated, showing the importance of optimization. The analysis of the correlation between in‐mold rheology and mechanical properties showed a slight variation in the mechanical properties and parts weight with a coefficient of variation of under 5%. Overall, the results demonstrate the ability of pressure‐controlled molding and automated viscosity adjustment to reduce the variability when molding a secondary feedstock.Highlights Pressure‐controlled injection molding of recycled polypropylene. Automated closed‐loop adaptive process control methodology. Methodology resulted in a reduction in pressure variation during molding. Changes in mechanical properties and in‐mold viscosity were investigated. Results show the potential of pressure‐controlled molding at reducing variation.

Motion Coordination of Multiple Autonomous Mobile Robots under Hard and Soft Constraints

This paper presents a distributed approach to the motion control problem for a platoon of unicycle robots moving through an unknown environment filled with static obstacles under multiple hard and soft operational constraints. Each robot has an onboard camera to determine its relative position in relation to its predecessor and proximity sensors to detect and avoid nearby obstascles. Moreover, no robot apart from the leader can independently localize itself within the given workspace. To overcome this limitation, we propose a novel distributed control protocol for each robot of the fleet, utilizing the Adaptive Performance Control (APC) methodology. By utilizing the APC approach to address input constraints via the on-line modification of the error specifications, we ensure that each follower effectively tracks its predecessor without encountering collisions with obstacles, while simultaneously maintaining visual contact with its preceding robot, thus ensuring the inter-robot visual connectivity. Finally, extensive simulation results are presented to demonstrate the effectiveness of the presented control system along with a real-time experiment conducted on an actual robotic system to validate the feasibility of the proposed approach in real-world scenarios.

Conditional adaptive time series compensation and control design for multi-axial real-time hybrid simulation

The structural performance of critical infrastructure during extreme events requires testing to understand the complex dynamics. Shake table testing of buildings to evaluate structural integrity is expensive and requires special facilities that can allow for the construction of large-scale test specimens. An attractive alternative is a cyber-physical testing technique known as Real-Time Hybrid Simulation (RTHS), where a large-scale structure is decomposed into physical and numerical substructures. A transfer system creates the interface between physical and numerical substructures. The challenge occurs when using multiple actuators connected with a coupler (i.e., transfer system) to create translation and rotation at the interface. Tracking control strategies aim to reduce time delay errors to create the desired displacements that account for the complex dynamics. This paper proposes two adaptive control methodologies for multi-axial real-time hybrid simulations that improve capabilities for a higher degree of coupling, boundary, complexity, and noise reduction. One control method integrates the feedback proportional derivative integrator (PID) control with a conditional adaptive time series (CATS) compensation and inverse decoupler. The second proposed control method is based on a coupled Model Predictive Control (MPC) with the CATS compensation. The performance of the proposed methods is evaluated using the virtual multi-axial benchmark control problem consisting of a steel frame as the experimental substructure. The transfer system consists of a coupler that connects two hydraulic actuators generating the translation and rotation acting at the joint. Through sensitivity analysis, parameters were tuned for the decoupler components, CATS compensation, and the control design for PID, LQG, and MPC. Comparative results among different control methods are evaluated based on performance criteria, including critical factors such as reduction in the time delay of bothactuators. The research findings in this paper improve the tracking control systems for the multi-axial RTHS of building structures subjected to earthquake loading. It provides insight into the robustness of the proposed tracking control methods in addressing uncertainty and improves the understanding of multiple output controllers that could be used in future cyber-physical testing of civil infrastructure subjected to natural hazards.

MRAC with SMC Applied to Lateral Control of a Fixed-Wing MAV

Abstract: This paper presents a PD control law with adaptive gains with the MIT (Massachusetts Institute Technology) rule with different sliding modes; that is, the MIT rule has been designed with is known in the literature with first order sliding mode, second order sliding mode and high order sliding mode (HOSM) to obtain a better gain scheduling taking advantage the sliding modes techniques-the PD control law with adaptive gains that is designed for the lateral dynamics of a fixed-wing MAV. To apply the methodology of the model reference adaptive control (MRAC), sometimes called model reference adaptive system (MRAS), to the adaptive gains of the PD control, a sliding manifold is proposed considering the output of the lateral dynamics and with the output of the reference model.

Robust adaptive backstepping neural networks fault tolerant control for mobile manipulator UAV with multiple uncertainties

The present study outlines the development of an Adaptive Backstepping Radial Basis Function Neural Networks Fault Tolerant Control (ABRBFNNFTC) methodology. The aforementioned methodology is employed to address the challenge of achieving trajectory following in the context of a Mobile Manipulator Unmanned Aerial Vehicle (MMUAV) when subjected to the effects of actuator faults and parametric uncertainties. The utilization of an adaptive radial basis function neural networks (RBFNNs) controller is employed for the purpose of approximating an unidentified nonlinear backstepping controller that relies on the precise model of the MMUAV. The Lyapunov direct method is utilized to establish the stability analysis of the entire system. The closed-loop system guarantees the Uniformly Ultimately Bounded (UUB) stability of all signals. The control methodology put forth ensures the achievement of a prescribed trajectory, and mitigates the impact of uncertainties and actuator faults. The efficiency of the proposed ABRBFNNFTC scheme is demonstrated through the presentation of extensive simulation studies.

Assessment methodology for dynamic occupancy adaptive HVAC control in subway stations integrating passenger flow simulation into building energy modeling

Heating, ventilation, and air conditioning (HVAC) systems in subway stations are often pre-scheduled to non-discriminatorily control large areas, ignoring uneven and dynamic thermal demand. Although building energy modeling (BEM) contributes to optimizing HVAC design and operation, it is limited in investigating dynamic occupancy adaptive HVAC control strategies in large open subway stations. This research formalized an integrated thermal control methodology (ITCM) that integrates passenger flow simulation and BEM to assess the dynamic occupancy adaptive HVAC control strategies for subway stations. To evaluate the applicability and effectiveness of ITCM, we developed a prototype coupling AnyLogic and EnergyPlus and carried out a simulation-based case study using a Hong Kong subway station. Based on the case study, we identified that ITCM enables subway station development and management teams to assess the comprehensive dynamic occupancy adaptive HVAC control strategies (e.g., decomposed thermal zones considering the layouts of diffusers and variable air volume boxes) in a timely and consistent manner. In addition, the case study results show that adaptive control with ceiling diffusers could save between 1.8% and 24.4% of cooling energy without considerably sacrificing thermal comfort.

A robust adaptive formation control methodology for networked multi-UAV systems with applications to cooperative payload transportation

This paper develops a new robust adaptive formation control methodology for networked multi-UAV systems to solve the cooperative payload transportation problem. This methodology offers a simple yet effective technique for object transportation relying on the formation tracking principle in the presence of bounded exogenous disturbances. Compared to existing techniques, the proposed method resorts to the σ-modification approach to resolve the parameter drift phenomenon. The ultimate boundedness of the formation tracking error dynamics is established by utilising Lyapunov theory. In addition, the proposed scheme does not involve any multi-body dynamics problem, nor does it require any reference model, disturbance filter/estimator or any prior knowledge of the disturbances. As a result, it reduces the overall complexity of the formation control scheme. The paper includes extensive simulation case studies accompanied by lab-based experimental validation results conducted on a group of nano quadcopter UAVs to demonstrate the feasibility and performance of the controller. The paper also projects a potential application of the proposed scheme in cooperative payload transportation missions.

Operation state-based adaptive control in response to production disturbances of energy-efficient serial production lines

The energy-efficient control has emerged as one of the most promising measures to reduce the unproductive energy consumption of machines during production disruptions. Diverse production disturbances (PDs) are the fundamental cause of production interruptions that result in unproductive energy consumption. In this scenario, the diversity and randomness of PDs necessitate elaborative consideration in energy-efficient controls. This consideration is crucial to the effectiveness of energy-efficient controls, yet it has been generally overlooked. To this end, this paper presents an adaptive energy-efficient control (AEC) methodology to generate adaptive control decisions when machines encounter diverse PDs in serial production lines. As a first step towards AEC, a PD identification method driven by Markov Logic Network is introduced to identify ongoing PDs. To then estimate the system performances in terms of energy savings and throughputs, a system dynamics model for serial production lines is established based on max-plus algebra. Finally, an AEC policy is formulated to optimize unproductive energy consumption on a nearly global scale while maintaining throughputs. A case study was performed on a die-casting production line in order to validate the effectiveness of the methodology. The results indicated a 96.1% F1 score for PD identifications. Based on this, the AEC policy resulted in a 61.12% reduction in unproductive energy consumption, with a corresponding decrease in throughputs of only 0.65%. It was confirmed that the AEC methodology achieved greater energy savings while minimizing throughput losses compared to conventional energy-efficient control methods.

Reinforcement learning based adaptive control for uncertain mechanical systems with asymptotic tracking

This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation. The considered system can depict the behavior of a large class of engineering systems, such as vehicular systems, robot manipulators and satellites. All these systems are often characterized by highly nonlinear characteristics, heavy modeling uncertainties and unknown perturbations, therefore, accurate-model-based nonlinear control approaches become unavailable. Motivated by the challenge, a reinforcement learning (RL) adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical dynamics. The approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error (RISE) control approach. Different from a classical RISE control law, a tanh(·) function is utilized instead of a sign(·) function to acquire a more smooth control signal. The developed controller requires very little prior knowledge of the dynamic model, is robust to unknown dynamics and exogenous disturbances, and can achieve asymptotic output tracking. Eventually, co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom (3-DOF) manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.

Fault-tolerant practical tracking control for uncertain robotic system with general reference trajectory and output constraint

This paper is devoted to the fault-tolerant tracking control for a class of uncertain robotic systems under time-varying output constraints. Notably, both actuator fault and the disturbances are present while all the dynamic matrices are not necessarily to be parameterized by unknown parameters or have known nominal parts, and moreover, the reference trajectories as well as the output constraints functions are not necessarily twice continuously differentiable without any time derivatives of them being available for feedback. These remarkable characteristics greatly relax the corresponding assumptions of the related literature and in turn to bring the ineffectiveness of the traditional schemes on this topic. For this, a powerful adaptive control methodology is established by incorporating adaptive dynamic compensation technique into the backstepping framework based on Barrier Lyapunov functions. Then, an adaptive state feedback controller with the smart choices of adaptive law and virtual controls is designed which guarantees that all the states of the closed-loop system are bounded and the system output practically tracks the reference trajectory while not violates the output constraints.

Adaptive shoot-through duty ratio control methodology of stand-alone quasi Z-source inverter

This paper presents an adaptive shoot-through duty ratio control methodology for a stand-alone three-phase quasi-Z-source inverter (qZSI). In practice, variable active and reactive load powers must be met by a qZSI-based stand-alone system. In this context, existing shoot-through control schemes for qZSI adjust the capacitor voltage or DC-link voltage at a fixed reference value. This causes extra voltage stresses on switches, high distortions, and an operating range reduction of the power inverter under variable load demands. On the contrary, the proposed shoot-through control scheme adjusts the shoot-through duty ratio adaptively based on load voltage feedback to improve performances. In this logic, the controllable shoot-through duty ratio facilitates various improved features in comparison to conventional schemes under load power variations. These features include reduced voltage stress across the switches, reduced distortions, and an extended operating range. The suggested proportional-integral (PI)-based scheme has a single control loop with a single measured quantity, i.e., sensing of load voltage only. The proposed concept has been verified via both simulation and experimental studies.

Adaptive Deadbeat Predictive Control for PMSM-based solar-powered electric vehicles with enhanced stator resistance compensation

This article introduces an innovative approach called Adaptive Deadbeat Predictive Control (ADPC), specifically designed to regulate the operation of a Permanent Magnet Synchronous Motor (PMSM) within a solar-powered electric vehicle (SEV). Considering the influence of factors such as magnetic saturation, material aging, and temperature fluctuations, discrepancies may arise between actual and nominal parameter values. Notably, the stator inductance, permanent magnet flux linkage, and stator resistance are crucial variables in this context. The article conducts an in-depth theoretical examination of the consequences of these parameter mismatches on the overall stability of the system. To mitigate the effects of stator resistance variability, an adaptive control methodology is proposed. This strategy hinges on real-time control adaptation, achieved by continuously estimating the resistance value through diligent consideration of the actual winding temperature. The proposed Adaptive Deadbeat Predictive Control framework is seamlessly implemented and simulated within the MATLAB Simulink environment. The simulation results eloquently validate the efficacy of the proposed control approach, showcasing its adeptness in mitigating the system’s sensitivity to stator resistance deviations, thereby fortifying the overall control resilience.

An Adaptive Control Methodology for Spacecraft Attitude Tracking Under Model and Environmental Uncertainties

This study investigates the attitude tracking problem for rigid spacecraft under model and environmental uncertainties. Two different control laws are separately developed and combined. First, based on a nominal attitude dynamic model assuming no uncertainty, the first controller is developed that exactly tracks a prespecified attitude reference trajectory with minimized control cost. The designer can generate this reference to prescribe desired performance specifications such as the maximum convergence time and overshoot. Next, an uncertain attitude dynamic system is considered, and the second controller is designed and added so that the controlled system can successfully track the predesigned reference trajectory with a user-specified tolerance even subject to uncertainty whose bounds are unknown. Compared to existing adaptive control schemes, the proposed approach possesses a very simple structure with a small number of control parameters and is not computationally intensive, making it more attractive for practical implementation. The proposed control laws generate smooth control signals and any information about the uncertainty bound is not needed in its design. Simulation results are provided to demonstrate the practical feasibility of the proposed approach, where reorientation/slew maneuvers of a large spacecraft are considered. The effects of limitations on the control torques are also investigated to show the effectiveness of the control methodology developed herein.

Robust Adaptive Control Strategy for a Bidirectional DC-DC Converter Based on Extremum Seeking and Sliding Mode Control

This paper presents a new control strategy that combines classical control and an optimization scheme to regulate the output voltage of the bidirectional converter under the presence of matched and mismatched disturbances. In detail, a control-oriented modeling method is presented first to capture the system dynamics in a common canonical form, allowing different disturbances to be considered. To estimate and compensate for unknown disturbances, an extended state observer (ESO)-based continuous sliding mode control is then proposed, which can guarantee high tracking precision, fast disturbance rejection, and chattering reduction. Next, an extremum seeking (ES)-based adaptive scheme is introduced to ensure system robustness as well as optimal control effort under different working scenarios. Finally, comparative simulations with classical proportional-integral-derivative (PID) control and constant switching gains are conducted to verify the effectiveness of the proposed adaptive control methodology through three case studies of load resistance variations, buck/boost mode switching, and input voltage variation.

Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation

The present research produces a new technique for the optimum operation of an isolated microgrid (MGD) based on an enhanced block-sparse adaptive Bayesian algorithm (EBSABA). To update the proportional-integral (PI) controller gains online, the suggested approach considers the impact of the actuating error signal as well as its magnitude. To reach a compromise result between the various purposes, the Response Surface Methodology (RSMT) is combined with the sunflower optimization (SFO) and particle swarm optimization (PSO) algorithms. To demonstrate the success of the novel approach, a benchmark MGD is evaluated in three different Incidents: (1) removing the MGD from the utility (islanding mode); (2) load variations under islanding mode; and (3) a three-phase fault under islanding mode. Extensive simulations are run to test the new technique using the PSCAD/EMTDC program. The validity of the proposed optimizer is demonstrated by comparing its results with those obtained using the least mean and square root of exponential method (LMSRE) based adaptive control, SFO, and PSO methodologies. The study demonstrates the superiority of the proposed EBSABA over the LMSRE, SFO, and PSO approaches in the system’s transient reactions.

Automated Vehicle Path Following: A Non-Quadratic-Lyapunov-Function-Based Model Reference Adaptive Control Approach With C∞-Smooth Projection Modification

Adaptive control theory has ushered in a fruitful era for the research and development of intelligent ground vehicle transportation systems. Notably, owing to its intelligence of handling parametric uncertainties via online learning and adaptation, the adaptive control methodology has attracted a great deal of attention in tackling autonomous/automated vehicle control problems. In this paper, we aim to improve the existing adaptive-control-based path-following controllers from two aspects. First, a non-quadratic-Lyapunov-function-based model reference adaptive controller is synthesized to achieve enhanced <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}^{\mathbf {1+\alpha }}$ </tex-math></inline-formula> tracking performance. Second, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{C}^{ \boldsymbol {\infty }}$ </tex-math></inline-formula> -differentiable smooth parameter projection scheme is employed for preventing the disturbance-induced control parameter drift. The stability of the redesigned path-tracking adaptive controller is analyzed. Furthermore, validations and comparative studies are conducted via hardware-in-the-loop experiments.

Adaptive Backstepping Control of Uncertain Sandwich-Like Nonlinear Systems With Deadzone Nonlinearity

A systematic differentiator-based adaptive backstepping control methodology is proposed for a class of sandwich-like nonlinear system with unknown state-dependent deadzone nonlinearity and parametric uncertainties. The novelty of our approach is that a high-order sliding mode differentiator is utilized to estimate the nonstrict feedback coupling term resulting from the sandwiched deadzone, and all the outputs of the differentiator are integrated into the backstepping procedure based on Lyapunov functions with flat zone recursively. By this approach, all the unknown parameters are estimated online, the discontinuity of the virtual input caused by bound estimations is avoided. It is shown that the ultimate boundedness of all the closed-loop signals is achieved and the output tracking error converges to a preset set. Simulation is performed to verify the theoretical findings.

Uncalibrated image-based visual servoing approach for translational trajectory tracking with an uncertain robot manipulator

This work presents an adaptive image-based visual servoing approach for uncertain robot manipulators using an eye-to-hand camera configuration, which does not require any calibration procedure and image velocity measurement. A monocular camera provides visual feedback for the robot’s joint control, allowing successful tracking of the translational trajectory, prescribed in image space, for a given target object attached to the robot end-effector. As our contribution, an indirect adaptive control approach is proposed to deal with the uncertain parameters of the camera–robot system and any camera misalignment concerning the robot frame. The indirect adaptive visual servoing approach is then combined with a direct adaptive motion controller using a cascade control framework to consider the uncertain robot dynamics in our stand-alone robot vision system. Lastly, an adaptive model-based state observer is designed to avoid the need for measuring the image feature velocity in the interconnected control loops. The Lyapunov stability theory and the passivity paradigm are synergized to demonstrate the stability properties of the overall closed-loop system. Experimental results, obtained with a 4-DoF robot manipulator carrying out translational trajectory tracking tasks using a commercial webcam, illustrate the performance and effectiveness of the proposed adaptive control methodology.

Direct adaptive control of spacecraft near asteroids

An orbit–attitude, direct model reference adaptive control methodology is developed and applied to spacecraft trajectory tracking in the vicinity of asteroid with dynamical coupling. The asteroid gravitational field is modeled using a polyhedral gravity model, and the asteroid is assumed to be rotating. Orbit–attitude dynamical coupling due to solar radiation pressure and gravity gradient is implemented. Spacecraft and asteroid parameters such as mass, inertia, and gravitational field are assumed to be unknown. A direct adaptive controller with e modification is implemented to track hovering and orbital trajectories in the vicinity of the asteroid. The direct model reference adaptive controller does not require parameter estimation and is robust to model and disturbance uncertainties. The controller is designed separately for orbit and attitude case. A Lyapunov analysis is presented to prove the asymptotic stability of the orbit–attitude adaptive control system. A numerical simulation for asteroid Kleopatra is presented with orbital and hovering tracking maneuvers. The results show that the adaptive controller is able to successfully track trajectories in the vicinity of the asteroid without assuming system parameters.

Fuzzy Adaptive Prescribed Performance Fault-Tolerant Control for HFVs with Fixed-Time Convergence Guarantee

A new fixed-time fuzzy adaptive fault-tolerant control methodology is proposed for the longitudinal dynamics of hypersonic flight vehicles (HFVs) in the presence of actuator faults, uncertain dynamics, and external disturbances. In contrast with the conventional fixed-time control schemes that typically contain the fractional powers of errors in their designs, this work develops a low-complexity control structure in the sense of removing the dependence on the need of abovementioned fractional power terms by means of prescribed performance control (PPC) method. Different from the most existing PPC approaches where the initial conditions of tracking errors are required to be known, the newly proposed prescribed performance function (PPF) can relax such restrictions through choosing properly small initial values of PPF. Fuzzy logic systems (FLSs) are employed to handle unknown dynamics, and minimal learning parameter (MLP) technique is incorporated into the design for the purpose of alleviating computation burden. Closed-loop stability is rigorously proved via Lyapunov stability theory, and simulation results are eventually given to validate the effectiveness of the proposed control strategy.