Model-based Control Algorithm Research Articles

Modeling the Soft Bellows of the Bionic Soft Arm

Conventional robots are strong, fast and rigid. Although these properties make them useful for industrial applications, for human machine collaborations they pose a threat. An alternative is provided by soft robots. However, model-based control algorithms for soft robots are not yet at the same level as for conventional robots. Their performance is often limited by their soft materials, which are difficult to model. The goal of this paper is to extend and increase the quality of the existing model for the bionic soft arm while ensuring real-time feasibility. In particular, the focus is on the soft bellows of the manipulator that actuates the robot. In this work, the three-dimensional geometrical problem of the inflating bellows is reduced to a two-dimensional problem that can be efficiently solved in real-time. The solution approximates the nonlinear behavior of the hyperelastic material and is part of a regressor, which then models the driving torque. In comparison to the existing model, the new developed model is able to describe hysteresis effects caused by the material. This increases the overall accuracy of the model. The results are backed up by experimental data.

Semi-active vibration control of building structure by Self Tuned Brain Emotional Learning Based Intelligent Controller

Control algorithms are the most crucial aspects in effective control of civil structures exposed to earthquake forces. Recently, adaptive intelligent control algorithms are evolving to be a viable substitute strategy for conventional model-based control algorithms. One of the most recent developments, known as the Brain Emotional Learning Based Intelligent Controller (BELBIC), has caught the attention of scientists as a model-free adaptive control system. It possesses appealing capabilities for dealing with nonlinearities and uncertainties in control frameworks. The modern semi-actively controlled civil structures have a highly uncertain and nonlinear nature following severe disturbances. As a result, these structures require real-time (online) robust control actions towards changing conditions, which the controllers with rigid settings cannot adapt. This study intends to overcome this issue in two ways: an online self-tuning brain emotional learning-based intelligent controller (ST-BELBIC) is formulated. Then its capabilities in improving the performance of cascaded controller in attenuating seismic vibrations of a three-story scaled building structure are validated. In this case, the central control unit BELBIC is based on sensory inputs (SI) and emotional cues (reward) signals. The main contribution of the proposed controller is a self-attuned version of the standard BELBIC that uses the benefits of a first-order Sugano fuzzy inference system (FIS) to adapt its parameters online. The proposed control methodology can be a promising model-free controller in terms of online tuning, simplicity of configuration, ease of applicability, less operational time, and neutralizing nonlinearities. The simulation affirms that the proposed controller compared with conventional LQR and intelligent Fuzzy tuned PID (FT-PID) controllers shows a superior performance regarding attenuating seismic responses of the building and can also improve the performance of cascaded FT-PID controller.

Attaining Ultraprecision Machining by Feed Drive System Stability Control with Piezoelectric Preloading Actuators

In this paper, a variable preload force control structure utilizing piezoelectric actuators (PEAs) is proposed for the stability control of the feed drive system. Three PEAs are installed between the two nuts to exert preload force on the ball screw, leading to an elimination or substantial reduction of the backlash, which is the main cause of instability of feed drives. This results in better machining precision throughout the operation process. In addition, the force analysis of the whole preload feed drive system is established. Moreover, the hysteresis of the PEAs is determined with reference to the Prandtl–Ishlinskii (P-I) model. Lastly, the P-I model-based feedforward controller is applied to the feed drive system to improve the resultant machining precision. Based on the modeling and experiments, to demonstrate the efficacy and high-performance of the proposed P-I model-based control algorithm against conventional PID control system, comparative experiments are conducted, showing satisfactory results.

Improved Voltage Boundary With Model-Based Control Algorithm for Increased Torque in the Field Weakening Region of Induction Machines

At high speed, electric vehicles (EVs) have limited torque when powered by an induction machine (IM). In IM, the high-speed region is called field weakening, and the region is known for torque limitation. This article proposes a control method that increases the torque without applying discontinuous-modulation techniques, which is commonly used. A bespoke model-based voltage control method has been developed, which enables reaching the hexagonal voltage reference trajectory for the field weakening region. So far, all model-based control methods are constrained by the inscribed voltage circle, which lies within the hexagonal voltage boundary. This restriction limits the available inverter output voltage across the motor windings, which, in turn, restricts the output torque of the drive. To achieve the hexagonal voltage trajectory, this article introduces a new calculation of the d-axis current for the entire speed range in the field weakening region. This calculation is based on the hexagonal voltage boundary equations and the stator voltage vector position. This generates a new reference d-axis current that minimizes the difference between the hexagonal voltage boundary and its inscribed voltage circle. As a result, the proposed d-axis current maximizes the output torque and output power in the field weakening region. The proposed method is presented analytically. Simulation and experimentally validated results are presented to confirm its feasibility and effectiveness.

Research developments in adaptive intelligent vibration control of smart civil structures

Control algorithms are the most critical aspects in the successful control of civil structures subjected to earthquake and wind forces. In recent years, adaptive intelligent control algorithms are emerging as an acceptable substitute method to conventional model-based control algorithms. These algorithms mainly work on the principles of artificial intelligence (AI) and soft computing (SC) methods that make them highly efficient in controlling highly nonlinear, time-varying, and time-delayed complex civil structures. The current research probes to control algorithms, that this article set forth an inclusive state-of-the-art review of adaptive intelligent control (AIC) algorithms for vibration control of smart civil structures. First, a general introduction to adaptive intelligent control is presented along with its advantages over conventional control algorithms. Second, their classification concerning artificial intelligence and soft computing methods is provided that mainly consists of artificial neural network-based controller, brain emotional learning-based intelligent controller, replicator dynamics-based controller, multi-agent system-based controller, support vector machine-based controller, fuzzy logic control, adaptive neuro-fuzzy inference system-based controller, adaptive filters-base controller, and meta-heuristic algorithms-based hybrid controllers. Third, a brief review of these algorithms with their developments on the theory and applications is provided. Fourth, we demonstrate a summarized overview of the cited literature with a brief trend analysis is presented. Finally, this study presents an overview of these innovative AIC methods that can demonstrate future directions. The contribution of this article is the anticipation of detailed and in-depth discussion into the perspective of AI and SC-based AIC method advances that enabled practical applications in attenuating vibration response of smart civil structures. Moreover, the review demonstrates the computing advantages of AIC over conventional controllers that are important in creating the next generation of smart civil structures.

Model-based and model-free designs for an extended continuous-time LQR with exogenous inputs

We present an extended linear quadratic regulator (LQR) design for continuous-time linear time invariant (LTI) systems in the presence of exogenous inputs with a novel feedback control structure. We first propose a model-based solution with cost minimization guarantees for states and inputs using dynamic programming (DP) that out-performs classical LQR with exogenous inputs. The control law consists of a combination of the optimal state feedback and an additional optimal term which is dependent on the exogenous inputs. The control gains for the two components are obtained by solving a set of matrix differential equations. We provide these solutions for both finite horizons and steady state cases. In the second part of the paper, we formulate a reinforcement learning (RL) based algorithm which does not need any model information except the input matrix, and can compute approximate steady-state extended LQR gains using measurements of the states, the control inputs, and the exogenous inputs. Both model-based and data-driven optimal control algorithms are tested with a numerical example under different exogenous inputs showcasing the effectiveness of the designs.

Enhancing the Notch Tensile Strength of GMAW welded AISI 1013 Low Carbon steel with Taguchi Optimization

There is currently no exact dynamic model which predicts hysteresis and creeps in a piezoelectric actuator under varying operating conditions (increasing frequency and amplitude of input, time of operation, temperature effects), and is stable against uncertainties. Thus, research needs to be carried out to predict the hysteresis and creep on the modeling and identification of the non-linear dynamics of a piezoelectric actuator. It would aid the implementation of a model-based control algorithm such as the precise positioning of a nano-positioning.

Modeling and Identification of the Non-linear Dynamics of a Piezoelectric Actuator-A Review

There is currently no exact dynamic model which predicts hysteresis and creeps in a piezoelectric actuator under varying operating conditions (increasing frequency and amplitude of input, time of operation, temperature effects), and is stable against uncertainties. Thus, research needs to be carried out to predict the hysteresis and creep on the modeling and identification of the non-linear dynamics of a piezoelectric actuator. It would aid the implementation of a model-based control algorithm such as the precise positioning of a nano-positioning.

Central Non-Linear Model-Based Predictive Vehicle Dynamics Control

Considering automated driving, vehicle dynamics control systems are also a crucial aspect. Vehicle dynamics control systems serve as an important influence factor on safety and ride comfort. By reducing the driver’s responsibility through partially or fully automated driving functions, the occupants’ perception of safety and ride comfort changes. Both aspects are focused even more and have to be enhanced. In general, research on vehicle dynamics control systems is a field that has already been well researched. With regard to the mentioned aspects, however, a central control structure features sufficient potential by exploiting synergies. Furthermore, a predictive mode of operation can contribute to achieve these objectives, since the vehicle can act in a predictive manner instead of merely reacting. Consequently, this contribution presents a central predictive control system by means of a non-linear model-based predictive control algorithm. In this context, roll, self-steering and pitch behavior are considered as control objectives. The active roll stabilization demonstrates an excellent control quality with a root mean squared error of 7.6953×10−3 rad averaged over both validation maneuvers. Compared to a vehicle utilizing a conventional control approach combined with a skyhook damping, pitching movements are reduced by 19.75%. Furthermore, an understeering behavior is maintained, which corresponds to the self-steering behavior of the passive vehicle. In general, the central predictive control, thus, increases both ride comfort and safety in a holistic way.

Modeling and control of a novel pneumatic two-stage piezoelectric-actuated valve

In manufacturing automation, there is a strong need for highly integrated intelligent components and subsystems. In this context, a novel two-stage pneumatic piezoelectric-actuated valve has recently been developed. It covers a wide range of pneumatic products and functions in one compact component and enables flexible and adaptive plant operation. The valve consists of four poppet main-stage valves that are driven by piezoelectrically actuated pre-stage valves and includes pressure sensors as well as a powerful computational unit. This work presents the systematic development of a model-based position control algorithm including modeling, identification, and controller design. The control concept comprises a hysteresis compensation based on the Prandtl–Ishlinskii operator theory, a flatness-based feedforward controller, and a PI feedback controller with appropriate anti-windup measures. Measurement results from a test bench demonstrate the performance of the proposed concept.

Data-Driven Control Based on the Interval Type-2 Intuition Fuzzy Brain Emotional Learning Network for the Multiple Degree-of-Freedom Rehabilitation Robot

A novel interval type-2 intuition fuzzy brain emotional learning network model (IT2IFBELC) which depends only on the input and output data is proposed for the rehabilitation robot, which is different from model-based control algorithms that require exact dynamic model knowledge of the rehabilitation robot. The proposed model takes advantage of the type-2 intuition fuzzy theory and brain emotional neural network, and this is no rule initially; then, the structure and parameters of IT2IFBELC are tuned online simultaneously by the gradient approach and Lyapunov function. The system input data streams are directly imported into the neural network through an interval type-2 intuition fuzzy inference system (IT2IFIS), and then the results are subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the whole controller is composed of three parts, including the ideal sliding mode controller, the interval type-2 intuition fuzzy brain emotional learning network controller, and a powerful robust compensation controller, and then one Lyapunov function is designed to guarantee the rapid convergence of the control systems. For further illustrating the superiority of this model, several models are studied here for comparison, and the results show that the interval type-2 intuition fuzzy brain emotional learning network model can obtain better satisfactory control performance and be suitable to deal with the influence of the uncertainty of the rehabilitation robot.

Visualization of thermal washout due to spatiotemporally heterogenous perfusion in the application of a model-based control algorithm for MR-HIFU mediated hyperthermia

Purpose This article will report results from the in-vivo application of a previously published model-predictive control algorithm for MR-HIFU hyperthermia. The purpose of the investigation was to test the controller’s in-vivo performance and behavior in the presence of heterogeneous perfusion. Materials and methods Hyperthermia at 42°C was induced and maintained for up to 30 min in a circular section of a thermometry slice in the biceps femoris of German landrace pigs (n=5) using a commercial MR-HIFU system and a recently developed MPC algorithm. The heating power allocation was correlated with heat sink maps and contrast-enhanced MRI images. The temporal change in perfusion was estimated based on the power required to maintain hyperthermia. Results The controller performed well throughout the treatments with an absolute average tracking error of 0.27 ± 0.15 °C and an average difference of 1.25 ± 0.22 °C between and The MPC algorithm allocates additional heating power to sub-volumes with elevated heat sink effects, which are colocalized with blood vessels visible on contrast-enhanced MRI. The perfusion appeared to have increased by at least a factor of ∼1.86 on average. Conclusions The MPC controller generates temperature distributions with a narrow spectrum of voxel temperatures inside the target ROI despite the presence of spatiotemporally heterogeneous perfusion due to the rapid thermometry feedback available with MR-HIFU and the flexible allocation of heating power. The visualization of spatiotemporally heterogeneous perfusion presents new research opportunities for the investigation of stimulated perfusion in hypoxic tumor regions.

Co-robotic harvest-aid platforms: Real-time control of picker lift heights to maximize harvesting efficiency

Harvest-aid platforms are used in modern orchards to improve manual harvesting efficiency, safety, and ergonomics. Typically, workers stand at pre-set heights on a platform’s multi-level deck, and each worker harvests fruits inside a canopy zone that is defined by the lowest and highest reach of the worker’s arms. However, fruit distributions are non-uniform, and worker picking speeds vary, thus generating a mismatch between labor demand (incoming fruit rates) and labor supply (fruit picking rates) in each zone; this mismatch limits platform-based harvesting efficiencies. To alleviate this problem, we transformed a conventional harvesting platform into a collaborative robot (co-robot) platform. As the co-robotic platform travels forward, it estimates the incoming fruit distribution using a vision system, it measures each worker’s picking speed using instrumented picking bags, and controls the heights of hydraulic lifts that move workers up and down. The model-based control algorithm maximizes the machine’s harvesting speed by changing the height at which each worker harvests as a response to incoming fruit load because it matches fruit-picking labor supply and demand. Simulation experiments with pre-recorded fruit distribution data validated the approach and provided efficiency gains under various conditions. Apple-harvesting experiments were also performed in a commercial orchard, where 2307 kg of apples were picked: 1045 kg in variable-height zone harvesting mode, and 1262 kg in fixed zone harvesting mode, with workers at fixed heights that were set by the grower. Variable-height zone harvesting mode throughput was 327.6 kg/h vs. 298.8 kg/h for fixed zone harvesting mode at human-controlled platform moving speed, resulting in an improvement of 9.5%.

Modeling and system identification of transient STOP models of optical systems.

Structural, Thermal, and Optical Performance (STOP) analysis is important for understanding the dynamics and for predicting the performance of a large number of optical systems whose proper functioning is negatively influenced by thermally induced aberrations. Furthermore, STOP models are being used to design and test passive and active methods for the compensation of thermally induced aberrations. However, in many cases and scenarios, the lack of precise knowledge of system parameters and equations governing the dynamics of thermally induced aberrations can significantly deteriorate the prediction accuracy of STOP models. In such cases, STOP models and underlying parameters need to be estimated from the data. To the best of our knowledge, the problem of estimating transient state-space STOP models from the experimental data has not received significant attention. Similarly, little attention has been dedicated to the related problem of obtaining low-dimensional state-space models of thermally induced aberrations that can be used for the design of high-performance model-based control and estimation algorithms. Motivated by this, in this manuscript, we present a numerical proof of principle for estimating low-dimensional state-space models of thermally induced aberrations and for characterizing the transient dynamics. Our approach is based on the COMSOL Multiphysics simulation framework for generating the test data and on a system identification approach. We numerically test our method on a lens system with a temperature-dependent refractive index that is used in high-power laser systems. The dynamics of such a system is complex and described by the coupling of thermal, structural, and ray-tracing models. The approach proposed in this paper can be generalized to other types of optical systems.

Production of Circularly Permuted Caspase-2 for Affinity Fusion-Tag Removal: Cloning, Expression in Escherichia coli, Purification, and Characterization.

Caspase-2 is the most specific protease of all caspases and therefore highly suitable as tag removal enzyme creating an authentic N-terminus of overexpressed tagged proteins of interest. The wild type human caspase-2 is a dimer of heterodimers generated by autocatalytic processing which is required for its enzymatic activity. We designed a circularly permuted caspase-2 (cpCasp2) to overcome the drawback of complex recombinant expression, purification and activation, cpCasp2 was constitutively active and expressed as a single chain protein. A 22 amino acid solubility tag and an optimized fermentation strategy realized with a model-based control algorithm further improved expression in Escherichia coli and 5.3 g/L of cpCasp2 in soluble form were obtained. The generated protease cleaved peptide and protein substrates, regardless of N-terminal amino acid with high activity and specificity. Edman degradation confirmed the correct N-terminal amino acid after tag removal, using Ubiquitin-conjugating enzyme E2 L3 as model substrate. Moreover, the generated enzyme is highly stable at −20 °C for one year and can undergo 25 freeze/thaw cycles without loss of enzyme activity. The generated cpCasp2 possesses all biophysical and biochemical properties required for efficient and economic tag removal and is ready for a platform fusion protein process.

An Adaptive Fast Terminal Sliding Mode Controller of Exercise-Assisted Robotic Arm for Elbow Joint Rehabilitation Featuring Pneumatic Artificial Muscle Actuator

Due to the time-varying nonlinear dynamic, uncertain model and hysteresis characteristics of the pneumatic artificial muscle (PAM) actuator, it is not easy to apply model-based control algorithms for monitoring, as well as controlling, the operation of systems driven by PAM actuators. Hence, the main aim of this work is to propose an intelligent controller named adaptive sliding controller adding compensator (ASC + C) to operate a robotic arm, featuring a pneumatic artificial muscle actuator, which assists rehabilitation exercise of the elbow joint function. The structure of the proposed controller is a combination between the fuzzy logic technique and Proportional Integral Derivative (PID) algorithm. In which, the input of fuzzy logic controller is the sliding surface, meanwhile, its output is the estimated value of the unknown nonlinear function, meaning that the model-based requirement is released. A PID controller works as a compensator with online learning ability and is designed to compensate because of the approximate error and hysteresis characteristic. Additionally, to improve convergence and to obtain stability, a fast terminal sliding manifold is introduced and online learning laws for parameters of the controller are attainted through the stable criterion of Lyapunov. Finally, an experimental apparatus is also fabricated to evaluate control response of the system. The experimental result confirmed strongly the ability of the proposed controller, which indicates that the ASC + C can obtain a steady state tracking error less than 5 degrees and a position response without overshoot.

Parareal with a learned coarse model for robotic manipulation

A key component of many robotics model-based planning and control algorithms is physics predictions, that is, forecasting a sequence of states given an initial state and a sequence of controls. This process is slow and a major computational bottleneck for robotics planning algorithms. Parallel-in-time integration methods can help to leverage parallel computing to accelerate physics predictions and thus planning. The Parareal algorithm iterates between a coarse serial integrator and a fine parallel integrator. A key challenge is to devise a coarse model that is computationally cheap but accurate enough for Parareal to converge quickly. Here, we investigate the use of a deep neural network physics model as a coarse model for Parareal in the context of robotic manipulation. In simulated experiments using the physics engine Mujoco as fine propagator we show that the learned coarse model leads to faster Parareal convergence than a coarse physics-based model. We further show that the learned coarse model allows to apply Parareal to scenarios with multiple objects, where the physics-based coarse model is not applicable. Finally, we conduct experiments on a real robot and show that Parareal predictions are close to real-world physics predictions for robotic pushing of multiple objects. Code (https://doi.org/10.5281/zenodo.3779085) and videos (https://youtu.be/wCh2o1rf-gA) are publicly available.

Dynamic identification of a tracking parallel mechanism

To improve tracking precision against complex external forces in the working environment, dynamic model-based control algorithms are widely used in tracking mechanisms. As a foundation of the real-time dynamic control system, this paper focuses on the complete predicted dynamic modelling and dynamic parameter identification of a 2-DoF over-constrained tracking parallel mechanism. Firstly, the kinematic model is obtained by differential mapping of the topology models based on finite and instantaneous screws (FIS). Referred to virtual work principle, dynamic equation is constructed. To reduce the computation cost in practice, a linear model involving inertial and friction parameters is formulated for dynamic identification. A direct identification method is applied and all the parameters are obtained at the same time. Experiments are performed to show the accuracy and efficiency of the identification results. The contributions of the paper lie in 1) a concise method for constructing velocity and acceleration models of components is provided without tedious analysis 2) complete dynamic model of an over-constrained tracking parallel mechanism is developed 3) practical dynamic identification process is carried out compromising between precision and efficiency.

Hybrid Position/Force Control for Dual-Machine Drilling and Riveting System

The deformation of riveting machine caused by riveting force during rivet formed makes the riveting tool out of positioning, which leads to gapping underneath the rivet manufactured head and insufficient rivet drive head. This paper proposes a hybrid position/force riveting control method for the dual-machine drilling and riveting system to eliminate the negative effects of machine deformation. The cooperative work of two-side machine tool is realized by a hybrid position/force control strategy, which compensates for the force-induced deformation error without an accurate stiffness model of the riveting system. The position of pressing foot relative to the machine which represents the deformation of skin-side machine is obtained for the compensation to the displacement of skin-side actuator. Simultaneously, the advanced force control is adopted for the stringer-side actuator. The dynamics model of the stringer-side actuator in consideration of the machine deformation is established and identified. The disturbance observer (DOB) and feedforward controller are introduced as the model-based control algorithm to achieve the high-performance force control. Also, contrast experiments are conducted to validate the effectiveness of the proposed riveting control method. The results show that the rivet manufactured head can be seated in the countersink during the forming process and the gapping under the head is eliminated. The driven head height tolerance of ±0.1 mm is achieved by accurate force control.

Energy Storage in Paraffin: A PDE Backstepping Experiment

This article proposes a novel control algorithm of a thermal phase-change process and shows its experimental verification using paraffin as a phase-change material (PCM). The core problem is to design a boundary feedback control for the “Stefan system” that describes the time evolution of the temperature profile in the liquid phase, which is associated with the time evolution of a position of liquid-solid phase interface, for the sake of stabilizing the interface position at a chosen set point. First, we design the continuous-time full-state feedback control law by means of the PDE backstepping method, which, in the absence of a demand for accelerated convergence, can also be arrived at by the energy-shaping method, and rigorously prove the stability of the closed-loop system under sufficiently small heat loss. Next, the control law is refined via observer-based output feedback under sampled-data measurements of the surface temperature and the phase interface position so that the control algorithm is practically implementable. Then, we conducted an experiment under a constant input to calibrate unknown parameters involved with the heat loss. Finally, the proposed model-based boundary feedback control algorithm is implemented in the experiment of melting paraffin. The experiment was successful: the convergence of the phase interface to the set point was achieved.