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Safety and stability considerations play a crucial role in the development of learning based strategies for control design of systems that require high levels of safety. Safe reinforcement learning (RL) based approaches traditionally seek learning of the control laws that are optimal with respect to system performance whilst ensuring system stability and safety. In this article, an off‐policy safe RL based approach is proposed for nonlinear systems affine in control in continuous time. In this novel work, safety and stability are guaranteed during initialization and exploration phases by adjusting the control input with the solution of a quadratic programming problem combining both input to state stable‐control Lyapunov function and robust control barrier function (R‐CBF) conditions. Moreover, the safety of the learned policy is assured by augmenting the cost function with a CBF to maintain safety and optimize performance simultaneously. Novel mathematically rigorous proofs are provided to establish the stability and safety guarantees, offering a sound theoretical foundation for the approach. To demonstrate the effectiveness of the algorithm, two examples are presented: engine surge and stall dynamics, and an unstable nonlinear system. [ABSTRACT FROM AUTHOR]

Injection molding is a critical component of modern industrial operations, and achieving fast and stable control of injection molding machines (IMMs) is essential for producing high-quality plastic products. This paper focuses on solving an optimal tracking control problem of the injection velocity that arises in a typical nonlinear IMM. To this end, an efficient optimal robust controller is proposed and designed. The nonlinear injection velocity servo system is first approximately linearized at iteration points using the first-order Taylor expansion approach. Then, at each time node in the optimization process, the relevant algebraic Riccati equation is introduced, and the solution is used to construct an optimal robust feedback controller. Furthermore, a rigorous Lyapunov theorem analysis is employed to demonstrate the global stability properties of the proposed feedback controller. The results from numerical simulations show that the proposed optimal robust control strategy can successfully and rapidly achieve the best tracking of the intended injection velocity trajectory within a given time. [ABSTRACT FROM AUTHOR]

This paper demonstrates a multiple-input multiple-output (MIMO) controller design framework and a controller switching algorithm for MIMO controllers in their state-space form, which together achieve robust, efficient control of turbocharged lean-burn engines over a wide operating space. The controller design framework requires a linearized plant model, and uses the μ -synthesis and DK-iteration algorithms while considering state and output uncertainties and actuator bandwidths to synthesize a robust H ∞ controller. A controller switching methodology using slow-fast controller decomposition and also incorporating hysteresis at switching points is utilized to smoothly transfer control authority between several MIMO controllers. The approach is applied to a high-fidelity truth-reference GT-Power engine model for a lean-burn natural gas-fueled engine to evaluate the closed-loop controller performance. The multi-tracking control problem targets engine speed, differential pressure across throttle as well as air-to-fuel ratio to achieve satisfactory engine performance and emissions without compressor surge. The engine response obtained using the robust MIMO controller is compared with that obtained using a state-of-the-art benchmark controller to evaluate the additional benefits of the MIMO controller. [ABSTRACT FROM AUTHOR]

This paper introduces a model-based robust adaptive input-output control framework for a family of robotic systems that include under-actuation, nonholonomic, and constrained properties. The proposed control framework can provide fast and effective controller generation for modular robotic systems (MRS) with interchangeable subsystems. The controller was first derived based on the general properties of nonholonomic robotic dynamic models while considering under-actuation and constraints. Then the adaptive control technique is introduced to overcome the effects such as inertia and force uncertainties. Robust augmentation is implemented via inverse optimality theory, which is verified with respect to a meaningful cost function. A simulation study on an aerial manipulator system with model uncertainty and disturbance was provided to demonstrate the characteristics and effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

Featured Application: Precise and accurate lower limb rehabilitation in the form of locomotion assistance and gait training through robust control of robotic exoskeletons. Rehabilitation in the form of locomotion assistance and gait training through robotic exoskeletons requires both precision and accuracy to achieve effective results. The essential challenge is to ensure robust tracking of the reference signal, i.e., of the gait or locomotion. This paper presents the design of model-based (MB) and model-free (MF) robust control strategies to achieve desired performance and robustness in terms of transient behavior and steady-state/tracking error, implementable to the locomotion assistance and gait training by exoskeletons. The dynamic responses of the exoskeleton system were investigated with both the control strategies. The study was carried out with a variety of reference signals and performance was evaluated to identify the best suited approach for rehabilitation exoskeletons. In case of the model-based control, a mathematical model of the system was developed using a bond graph modeling technique and a lead compensated H-infinity reference gain controller was designed to ensure robust tracking performance. In the model-free control strategy, however, the system function is approximated using radial basis function neural networks (RBFNNs) and an adaptive proportional-derivative RBFNN controller was designed to achieve the desired results with minimum tracking error. Both strategies make the system robust and stable. However, the MF control strategy is faster for all reference inputs as compared to the MB control strategy i.e., faster to approach the peak value and settle, and rapidly approaches the zero steady-state/tracking error. The rise time in the case of a sinusoidal input for model-free control is 0.4 s faster than the rise time in model-based control. Similarly, the settling time is 3.9 s faster in the case of model-free control, which is a prominent difference and can provide better rehabilitation results. [ABSTRACT FROM AUTHOR]

Reactivity controlled compression ignition (RCCI) is a highly efficient and clean combustion concept, which enables the use of a wide range of renewable fuels. Consequently, this promising dual fuel combustion concept is of great interest for realizing climate neutral future transport. RCCI is very sensitive for operating conditions and requires advanced control strategies to guarantee stable and safe operation. For real-world RCCI implementation, we face control challenges related to transients and varying ambient conditions. Currently, a multivariable air–fuel path controller that can guarantee robust RCCI engine operation is lacking. In this work, we present a RCCI engine controller, which combines static decoupling and a diagonal MIMO feedback controller. For control design, a frequency domain-based approach is presented, which explicitly deals with cylinder-to-cylinder variations using data-driven, cylinder-individual combustion models. This approach enables a systematic trade-off between fast and robust performance and gives clear design criteria for stable operation. The performance of the developed multivariable engine controller is demonstrated on a six-cylinder diesel-E85 RCCI engine. From experimental results, it is concluded that the RCCI engine controller accurately tracks the five desired combustion and air path parameters, simultaneously. For the studied transient cycle, this results in 12.8% reduction in NO x emissions and peak in-cylinder pressure rise rates are reduced by 3.8 bar/deg CA. Compared to open-loop control, the stable and safe operating range is increased from 25 ° C up to 35 ° C intake manifold temperature and maximal load range is increased by 14.7% up to BMEP = 14.8 bar. [ABSTRACT FROM AUTHOR]

In this paper, an experimental procedure used efficiently and successfully for teaching in practical classes of digital control course for undergraduate students who are attending the fifth year in Industrial Electrical Engineering at Federal Institute of Education, Sciences and Technology, in Brazil, is presented. The adopted methodology is characterized by the following steps: modeling of the dynamic system to be controlled from experimental data, model-based digital controller design according to pre-established performance specifications and implementation of the designed digital controller. The experiments are performed on a data acquisition platform, in real time, based on high performance virtual/electronics instrumentation, at Laboratory of Computational Intelligence Applied to Technology. Results from an experimental activity, developed by students in the digital control course, illustrate the main practical aspects that must be taken into account in design and implementation of real time digital PI/PID control. [ABSTRACT FROM AUTHOR]

• The magnetic-flux diffusion equation is combined with a plasma-energy balance equation to obtain a control-oriented response model for control design. • The electron temperature, plasma resistivity, and lower-hybrid current drive are modeled by following an uncertainty-based approach. • The problem of designing a model-based controller for simultaneous q-profile and plasma-energy regulation is formulated as an optimization problem. • The tradeoff between two competing objectives, namely the tracking error and the control effort, is optimally solved during the control design process. • The tracking performance of the proposed controller, which is robust against the model uncertainties, is successfully tested in disturbance-rejection nonlinear simulations. Integrated control of the toroidal current density profile, or alternatively the q -profile, and plasma stored energy is essential to achieve advanced plasma scenarios characterized by high plasma confinement, magnetohydrodynamics stability, and noninductively driven plasma current. The q -profile evolution is closely related to the evolution of the poloidal magnetic flux profile, whose dynamics is modeled by a nonlinear partial differential equation (PDE) referred to as the magnetic-flux diffusion equation (MDE). The MDE prediction depends heavily on the chosen models for the electron temperature, plasma resistivity, and non-inductive current drives. To aid control synthesis, control-oriented models for these plasma quantities are necessary to make the problem tractable. However, a relatively large deviation between the predictions by these control-oriented models and experimental data is not uncommon. For this reason, the electron temperature, plasma resistivity, and non-inductive current drives are modeled for control synthesis in this work as the product of an "uncertain" reference profile and a nonlinear function of the different auxiliary heating and current-drive (H&CD) source powers and the total plasma current. The uncertainties are quantified in such a way that the family of models arising from the modeling process is able to capture the q -profile and plasma stored energy dynamics from a typical EAST shot. A control-oriented nonlinear PDE model is developed by combining the MDE with the "uncertain" models for the electron temperature, plasma resistivity, and non-inductive current drives. This model is then rewritten into a control framework to design a controller that is robust against the modeled uncertainties. The resulting controller utilizes EAST's H&CD powers and total plasma current to regulate the q profile and plasma stored energy even when mismatches between modeled and actual dynamics are present. The effectiveness of the controller is demonstrated through nonlinear simulations. [ABSTRACT FROM AUTHOR]

Abstract Model-based optimal control (MBOC) is a promising method to reap the potential of the waterside free cooling system in data center. While the theoretical energy savings are impressive, uncertainties in the model, sensors, and actuators could hinder this control strategy's practical application. This study quantified the impact of various uncertainties on the supervisory control method in two steps. First, sensitivity analysis was conducted using the Morris method and the one-at-a-time method to identify the influential impact of uncertain elements. Next, Monte-Carlo simulation was designed to perform an uncertainty study and evaluate the robustness and energy efficiency of the control method. A virtual emulator for simulating the proposed novel hybrid cooling system in data centers in combination with different supervisory control methods was developed. Full-year simulations indicate that the impact of uncertainty on the control performance of the MBOC strategy is greater than that of conventional control strategy; under various uncertainties, the energy consumption and operation mode prediction error rate of the MBOC method were increased by 43.6% and 99.2%, respectively. The research suggests that, if MBOC is adopted for the hybrid cooling system control, more efforts should be placed on reducing the uncertainties from various sources. Highlights • A model-based optimal supervisory control strategy for hybrid cooling plant using lake water source was developed. • The impacts of model uncertainties, sensor errors and execution errors on control performance were quantified. • The key impacts for control performance were identified based on the Morris and the one-at-a-time method. • Monte-Carlo simulation was used to evaluate the robustness and energy efficiency of the control method. [ABSTRACT FROM AUTHOR]

Clutch-to-clutch shifts are ubiquitous in automatic transmissions, motivating the need for formal and robust methods for controlling these shifts. Limited sensing in production transmissions poses a severe hurdle for feedback control of these gearshifts. In the current study, nonlinear estimation methods are developed to compensate for limited sensing, and enable model-based closed loop control of the torque and inertia phases of shifts by manipulation of clutch pressures. During the torque phase, the offgoing clutch is controlled to emulate a one-way clutch, which ensures smooth coordination of the two clutches and reduced overall variation in the output shaft torque during the gearshift. During the inertia phase, the oncoming clutch is controlled to ensure smooth engagement at lock-up, resulting in reduction of shock and subsequent driveline oscillations. Controller performance is evaluated through numerical simulation of the proposed observer based controller on an experimentally validated high order model of a stepped production automatic transmission. The results show that shift control objectives were met by the proposed estimation and control strategy in the presence of appreciable model uncertainty and speed sensor noise, thus validating the robustness and practical effectiveness of the controller. Also, the proposed model-based controller was shown to be effective in controlling gearshifts at different power-levels (at different throttle openings), which establishes effectiveness of the same over a wide range of operating conditions. [ABSTRACT FROM AUTHOR]

This paper studies the robustness of model-based event-triggered control systems with respect to the differences between the plant and model matrices. Two types of event conditions, which involve an additional threshold variable and the norm of model states, are investigated, respectively. The tunable parameters in both the event conditions are designed according to the differences between the plant and model matrices. Also, the uncertainties in the plant matrices are considered, and the asymptotic stability can be guaranteed robustly. Moreover, the relationship between the tunable parameters and the model matrices is revealed. Namely, on the one hand, there exists a range of the tunable parameters such that the closed-loop system is asymptotically stable with model matrices in any compact set. On the other hand, if the differences between the plant and model matrices are small enough, the tunable parameters can be set arbitrarily large. Finally, a numerical example is provided to illustrate the efficiency and feasibility of the obtained results. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We present a multi-parametric model predictive controller (mpMPC) for discrete-time linear parameter-varying (LPV) systems based on the solution of the mpMPC problem for discrete-time linear time-invariant (LTI) systems. The control method yields a controller that adapts to parameter changes of the LPV system. This is accomplished by an add-on unit to the implementation of the mpMPC for LTI systems. No modification of the optimal mpMPC solution for LTI systems is needed. The mpMPC for LPV systems is entirely based on simple computational steps performed on-line. This control design method could improve the performance and robustness of a mpMPC for LPV systems with slowly varying parameters. We apply this method to process systems which suffer from slow variation of system parameters due, for example, to aging or degradation. As an illustrative example the reference tracking control problem of the hypnotic depth during intravenous anaesthesia is presented: the time varying system matrix mimics an external disturbance on the hypnotic depth. In this example the presented mpMPC for LPV systems shows a reduction of approximately 60% of the reference tracking error compared to the mpMPC for LTI systems. [ABSTRACT FROM AUTHOR]

A novel model-free predictive mixed-sensitivity H\infty control scheme is proposed and applied to grid-connected solar power generation systems. The predictive sensitivity and the predictive complementary sensitivity are defined based on the predictive model. The model-free predictive mixed-sensitivity H\infty controller is derived from input/output measurements to achieve an optimal predictive mixed-sensitivity performance using a max-min optimization method. Then, a simulation system for solar power generation systems is established using SimPowerSystems. Finally, the simulations are conduced to show the effectiveness of the proposed model-free controller, which outperforms the conventional proportional-integral and model-free linear quadratic Gaussian controllers in the tracking performance and the robustness of solar power generation systems. [ABSTRACT FROM PUBLISHER]

Abstract: Control of the emissions of diesel engines is an upcoming approach for complying with legislation while limiting the calibration effort. In this paper, a controller for engine-out NOx and PM is combined with a controller for the center of main combustion and the indicated mean effective pressure. The inner cascade combustion controller effectively reduces unwanted influences on the combustion. These influences are partly coupled to the outer-loop manipulated variables and partly result from disturbances which commonly appear in diesel engines. Robust stability is analyzed and holds in a wide operating range. Performance of the control structure is demonstrated with experiments. [Copyright &y& Elsevier]

Bayesian and nonlinear least-squares methods of calibration were evaluated and compared for gray-box modeling of a retail building. Gray-box model calibration is one form of system identification and is examined here with perturbations to the simple yet popular European Committee for Standardization (CEN)-ISO thermal network model. The primary objective was to understand whether the computational expense of probabilistic Bayesian techniques is required to provide robustness to signal noise, specifically with regard to lower dimensional problems (physical or semiphysical), where model calibration is preferred over uncertainty quantification. The Bayesian approach allows parameter interactions and trade-offs to be revealed, one form of sensitivity analysis, but its full power for uncertainty quantification cannot be harnessed with gray-box or other simplified models. Surrogate data from a detailed building energy simulation program were used to ensure command over latent variables, whereas a range of signal-to-noise and noise colors were considered in the experimental study. The fidelity to the building zone temperature and thermal load was the basis for comparing results. Utilization of uniform priors showed that both methods performed similarly well. Bayesian calibration outperformed traditional methods on noisy data sets; however, traditional methods were adequate up to an approximately 25% noise level. The thermal gray-box model calibration has the intended application of embedded model predictive control, where speed, accuracy, and robustness are crucial. Traditional methods required approximately 100 times less CPU time and are recommended given the model simplicity, application, and expected system noise levels. [ABSTRACT FROM AUTHOR]

Abstract: Fast and robust control of combustion phasing is an important challenge for real-time model-based control of Homogenous Charge Compression Ignition (HCCI). In this paper a new discrete Control Oriented Model (COM) for predicting HCCI combustion phasing on a cycle-to-cycle basis is outlined and validated against experimental data from a single cylinder Ricardo engine. The COM has sufficient accuracy for real-time HCCI control and can be implemented in real-time. A Discrete Sliding Mode Controller (DSMC) coupled with a Kalman filter is designed to control combustion phasing by adjusting the ratio of two Primary Reference Fuels (PRFs). The results indicate the DSMC maintains the stability of the engine operation in a wide range of loads and speeds. The DSMC is compared with an empirical Proportional Integral (PI) controller. The results show the SMC outperforms a PI controller particularly in rejecting disturbances while maintaining HCCI combustion phasing in its desired range. [Copyright &y& Elsevier]

Self-excited oscillation is becoming a major issue in low-emission, lean partially premixed combustion systems, and active control has been shown to be a feasible method to suppress such instabilities. A number of robust control methods are employed to obtain a feedback controller and it is observed that the robustness to system uncertainty is significantly better for a low complexity controller in spite of the norms being similar. Moreover, we demonstrate that closed-loop stability for such a complex system can be proved via use of the integral quadratic constraint method. Open- and closed-loop nonlinear simulations are provided. [ABSTRACT FROM PUBLISHER]

Abstract: In atomic force microscopy (AFM) the force between the measurement tip and the sample is controlled in a feedback loop to prevent damage of the tip and sample during imaging, and to convert the measurement of the tip–sample force into an estimate of the sample topography. Dynamical uncertainties of the system limit the achievable control bandwidth and the accuracy of the topography estimation. This paper presents an integrated approach to design a feedback controller and topography estimator, taking into account the dynamical uncertainties of the system. The proposed methodology is experimentally demonstrated on a commercial AFM system, showing a direct trade-off between the control bandwidth and the accuracy of the topography estimation. [Copyright &y& Elsevier]

In this paper, robust adaptive boundary control is developed for a class of flexible string systems under unknown spatiotemporally varying distributed disturbance and time-varying boundary disturbance. The vibrating string is nonuniform since the spatiotemporally varying tension applied to the system. The nonuniform vibrating string system is represented by a nonlinear nonhomogeneous partial differential equation (PDE) and two ordinary differential equations (ODEs). Model-based control is first proposed at the right boundary of the string to suppress the vibration of the flexible nonuniform string system. To compensate for the system parametric uncertainties, robust adaptive boundary control is developed. With the proposed control, the uniformly ultimate boundness of the closed-loop system is demonstrated via Lyapunov's direct method. The state of the nonuniform string system is proven to converge to a small neighborhood of zero by appropriately choosing the design parameters. Simulations are provided to illustrate the effectiveness of the proposed control. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, a control approach to increase the accuracy and precision of a 3RRR parallel robot based on the use of extra sensors in the passive joints is proposed. For that purpose, a redundant dynamic modelling approach and a control scheme based on the redundant data is presented in this paper. The proposed approach, the Extended CTC, introduces redundant data directly in the controller by means of a proper dynamic model, providing better dynamic performance than classical approaches in presence of model parameter uncertainties. The effectiveness of the control law is demonstrated using two approaches. Initially, a sensitivity based analysis is performed in order to analyze the relative robustness of the proposed approach. Secondly, results are validated by a set of simulation experiments in the 3RRR parallel robot in presence of randomly introduced model parameter uncertainties. [Copyright &y& Elsevier]

Homogeneous charge compression ignition (HCCI) is a promising internal combustion engine concept. It holds promise of combining low emission levels with high efficiency. However, as ignition timing in HCCI operation lacks direct actuation and is highly sensitive to operating conditions and disturbances, robust closed-loop control is necessary. To facilitate control design and allow for porting of both models and the resulting controllers between different engines, physics-based mathematical models of HCCI are of interest. This paper presents work on a physical model of HCCI including cylinder wall temperature and evaluates predictive controllers based on linearizations of the model. The model was derived using first principles and formulated on a cycle-to-cycle basis. The resulting model was of second order with two inputs and two outputs. Measurement data including cylinder wall temperature measurements was used for calibration and validation of the model. Predictive control of the combustion phasing was then evaluated experimentally using ethanol as fuel. The control signals were the intake temperature and the inlet valve closing timing. The control performance was evaluated in terms of response time and steady-state output variance. Multi-cylinder control experiments were also carried out. [ABSTRACT FROM PUBLISHER]

Abstract: Model-based control of parallel kinematics machines (PKM) relies on computationally efficient formulations in terms of a set of independent joint coordinates. Since PKM models are commonly expressed in terms of actuator or end-effector coordinates the models are not valid at input- or output-singularities, respectively. Moreover input-singularities limit the motion range of PKM. Actuation redundancy is a means to increase the singularity-free range of motion. However, due to the redundancy only a subset of the actuator coordinates constitute independent coordinates. This subset corresponds to the actuator coordinates of the non-redundant PKM, which does generally not constitute proper minimal coordinates for the entire workspace. Hence a redundantly actuated PKM (RA-PKM) controlled by a model-based controller in terms of minimal coordinates would exhibit the same limitations as the non-redundant PKM. One approach to tackle this problem is to switch between different minimal coordinates, i.e., different motion equations are used within the controller. In this contribution a computed torque and augmented PD control scheme in redundant coordinates is proposed, as an alternative to coordinate switching, and applied to the control of redundantly actuated PKM. That is, no minimal coordinates are selected. This novel formulation is numerically robust and does not suffer from input- or output-singularities. Even more the formulation is always valid except at configuration space singularities. For the redundancy resolution within the inverse dynamics the pseudoinverse of a rank deficient matrix is required, for which an explicit formulation is presented. For both controllers exponential trajectory tracking is shown. Experimental results are reported for a planar 2 DOF RA-PKM. [Copyright &y& Elsevier]

The progressive reduction in vehicle emission requirements have forced the automotive industry to invest in research for developing alternative and more efficient control strategies. All control features and resources are permanently active in an electronic control unit (ECU), ensuring the best performance with respect to emissions, fuel economy, driveability and diagnostics, independently from engine working point. In this article, a considerable step forward has been achieved by the common-rail technology which has made possible to vary the injection pressure over the entire engine speed range. As a consequence, the injection of a fixed amount of fuel is more precise and multiple injections in a combustion cycle can be made. In this article, a novel gain scheduling pressure controller for gasoline direct injection (GDI) engine is designed to stabilise the mean fuel pressure into the rail and to track demanded pressure trajectories. By exploiting a simple control-oriented model describing the mean pressure dynamics in the rail, the control structure turns to be simple enough to be effectively implemented in commercial ECUs. Experimental results in a wide range of operating points confirm the effectiveness of the proposed control method to tame efficiently the mean value pressure dynamics of the plant showing a good accuracy and robustness with respect to unavoidable parameters uncertainties, unmodelled dynamics, and hidden coupling terms. [ABSTRACT FROM PUBLISHER]

Selective catalytic reduction (SCR) is a promising diesel aftertreatment technology that enables low nitrogen oxides (NOx) tailpipe emissions with relatively low fuel consumption. Future emission legislation is pushing the boundaries for SCR control systems to achieve high NOx conversion within a tailpipe ammonia (NH3) slip constraint, and to provide robustness to meet in-use compliance requirements. This work presents a new adaptive control strategy that uses an ammonia feedback sensor and an online ammonia storage model. Experimental validation on a 12-liter heavy-duty diesel engine with a 34-liter Zeolite SCR catalyst shows good performance and robustness against urea under- and over-dosage for both the European steady-state and transient test cycles. The new strategy is compared with a NOx sensor-based control strategy with cross-sensitivity compensation. It proved to be superior in terms of transient adaptation and taking an NH3 slip constraint into account. [ABSTRACT FROM PUBLISHER]

Abstract: This paper investigates the formation keeping problem for multiple spacecraft in the framework of networked control systems (NCSs). A continuous-time representation of the NCS is considered for the tracking control of relative translational motion between two spacecraft in a leader–follower formation in the presence of communication constraints and system uncertainties. Model-based control schemes are presented, which employ state feedback (when the relative position and velocity vectors are directly measurable) and output feedback (when velocity measurements are not available), respectively, to guarantee input-to-state stability (ISS) of the system. The stability conditions on network transfer intervals are derived as simple eigenvalue tests of a well-structured test matrix. The results are then extended to include network communication delay. Numerical simulations are presented to demonstrate the effectiveness of the control scheme ensuring high formation keeping precision and robustness to nonlinearities and system uncertainties. The proposed controllers are robust not only to structured uncertainties such as system parameter perturbations but also to unstructured uncertainties such as external disturbances and measurement noises. [Copyright &y& Elsevier]

Actuation redundancy increases and homogenizes the kinematic dexterity and stiffness as well as the force distribution among the actuators of parallel-kinematics machines (PKMs). It also allows for internal prestresses within the PKM without affecting the environment that can potentially be used to account for secondary tasks, such as active stiffness and backlash-avoiding control. However, in the presence of geometric uncertainties, this feature can become a serious problem, since then, control forces may be annihilated, or even some of the intentional prestress components may interfere with the environment. While model uncertainties can generally be tackled with robust-control concepts, actuation redundancy of PKM impedes the use of established robust-control schemes. The effect of such uncertainties and the applicability of standard model-based control schemes are analyzed in this paper. It is shown that geometric uncertainties lead to parasitic perturbation forces that cannot be compensated by adjustment of the controls. An amended version of the augmented PD and computed torque-control scheme is proposed that does not suffer from such effects. [ABSTRACT FROM AUTHOR]

This article presents a novel three-loop control system, which supplements control structures known from the literature as model following control (MFC). The major advantage of the proposed system is its high robustness to process parameter variations; it is much higher than that offered by single-loop or two-loop control systems. Features of the new structure are revealed by a theoretical analysis that has been carried out from the viewpoint of requirements for a force/pose controller of a Staubli RX60 manipulator. This article shows how the proposed control structure responds to such strong process parameter variations and makes a comparison to results yielded by single-loop control structures. [ABSTRACT FROM AUTHOR]

Using a compensator in the structure is one of the simplest ways to achieve efficient control of a non-linear process. Unfortunately, accessing the inverse process model is not a trivial issue. Except for some special cases, it is much easier to determine the forward process model than the inverse one. For this reason, it would be interesting to propose an alternative solution to the well-known feedforward control method. In this paper, a simple multi-loop concept will be introduced. The main idea is based on the natural (but limited) robustness offered by a single PID loop and the ability to scale up the complexity of the forward process model. The proposed structure multiplies a single PID loop including forward models with increasing complexity to calculate the resultant non-linear control value. This new approach produces a comparable performance to the feedforward method but does not require access to the inverse properties of the process. The idea was evaluated in terms of stability and robustness to parameter changes. In addition, a simulation study was carried out using two coupled non-linear processes, i.e., the position control of a robot manipulator with force interaction. The selection of this process was no casual choice. On the one hand, it is extremely complex; however, on the other hand, it provides the possibility to determine both the inverse and the forward dynamic model. This capability was helpful to perform an effective comparison of the proposed solution with the known feedforward structure. [ABSTRACT FROM AUTHOR]

Abstract: A modified generic model controller is developed and tested through a simulation study. The application involves model-based control of a propylene polymerization reactor in which the monomer conversion and melt index of the produced polymer are controlled by manipulating the reactor cooling water flow and the inlet hydrogen concentration. Non-linear control is designed using a simplified non-linear model, in order to demonstrate the robustness of the control approach for modeling errors. Two model parameters are updated online in order to ensure that the controlled process outputs and their predicted values track closely. The controller is the static inverse of the process model with setpoints of the measured process outputs converted to setpoints for some of the state variables. The simulation study shows that the proposed controller has good setpoint tracking and disturbance rejection properties and is superior to the conventional generic model control and Smith predictor control approaches. [Copyright &y& Elsevier]

We report on the design and analysis of a controller that can achieve dynamical self-righting of our hexapedal robot, RHex. Motivated by the initial success of an empirically tuned controller we present a feedback controller based on a saggital plane model of the robot We also extend this controller to develop a hybrid pumping strategy that overcomes actuator torque limitations, resulting in robust flipping behavior over a wide range of surfaces. We present simulations and experiments to validate the model and characterize the performance of the new controller. [ABSTRACT FROM AUTHOR]

Machining force regulation provides significant benefits in productivity and part quality. Adaptive techniques have typically been utilized due to the tremendous parameter variations that are found in machining processes. While adaptive controllers provide greater stability as compared to fixed-gain controllers, they have found very little headway in industry due to complexity in design, implementation, and maintenance. Recently, model-based techniques, with and without process compensation (i.e., the ability to directly adjust controller gains given known changes in process parameters), have been explored. This paper provides a comparison of four model-based machining force controllers; namely, linearization, log transform, nonlinear, and robust. These controllers are compared to an adaptive machining force controller in terms of transient performance and stability robustness with respect to parameter variations, and in terms of stability robustness with respect to unmodeled dynamics via simulation and experimental studies. The developed stability analyzes for the model-based controllers provide excellent predictions of the stability boundaries in the parameter space. Thus, stability robustness in terms of both model parameter variation and controller parameter adjustments can be systematically explored. Also, the results demonstrate that the stability robustness of the model-based controllers is insensitive to unmodeled servomechanism dynamics. While each force control approach performed satisfactorily in a laboratory environment, it can be generally concluded that their implementation should be dictated by the economics of the production environment. [Copyright &y& Elsevier]

In this paper a method is presented for deriving the explicit robust model-based optimal control law for constrained linear dynamic systems. The controller is derived off-line via parametric programming before any actual process implementation takes place. The proposed control scheme guarantees feasible operation in the presence of bounded input uncertainties by (i) explicitly incorporating in the controller design stage a set of feasibility constraints and (ii) minimizing the nominal performance, or the expectation of the performance over the uncertainty space. An extension of the method to problems involving target point tracking in the presence of persistent disturbances is also discussed. The general concept is illustrated with two examples. [Copyright &y& Elsevier]

In this work, a method is presented for obtaining the explicit robust model-based tracking control law for constrained dynamic systems. The proposed control scheme guarantees optimal and feasible operation under the presence of unknown bounded input uncertainties by introducing in the controller design stage a set of feasibility constraints and employing an estimation scheme for ensuring robust offset free output tracking. The controller features a simple structure that is derived off-line via parametric programming prior to any process implementation. [Copyright &y& Elsevier]

Parallel structure robots have been receiving growing attention from both academia and industry in recent years. This is due to their advantages over serial structure robots, such as high stiffness, high motion accuracy and a high load–structure ratio. Control of parallel robots, however, produces difficulties to control engineers due to the modelling errors arising from the highly nonlinear and complex structures. This paper proposes a dual-model-based structure for error attenuation in the trajectory-tracking control of a parallel robot manipulator. In this design, a conventional model-based control algorithm employing an estimated robot dynamic model is first implemented in the inner loop of the control structure. Then, in order to reduce the unwanted effects caused by modelling errors, another model-based structure, developed based on the concept of the internal model control, is appended in the outer loop of the control structure as a compensator. A combination of these two model-based components results in a novel dual-model-based structure for parallel robot control. Sensitivity analyses show that the effects due to modelling errors and external disturbances can be significantly reduced by applying this new control structure without relying on a high-gain control solution. The effectiveness of this control design is successfully demonstrated by numerical studies on a planar parallel robot with 2 degrees of freedom. [ABSTRACT FROM AUTHOR]

A new robust control strategy coefficient diagram method (CDM), which is an algebraic approach applied to a polynomial loop in the parameter space, is studied in this paper. Here, a model-based CDM is developed and proposed for uncertain systems. A special diagram called a coefficient diagram, which is used as a vehicle to carry the necessary information and as the criteria of good performance, is designed and modified accordingly. The performance of this novel controller is illustrated through several examples, and numerical results are provided. [ABSTRACT FROM AUTHOR]

This paper discusses the stability properties of a robust nonlinear model predictive control (NMPC) scheme that is based on a multi-stage optimization formulation. The use of a scenario tree to represent the uncertainty makes it possible to formulate a closed-loop robust approach with recourse which improves the open-loop approach in terms of performance and domain of attraction. We show that a straightforward formulation of a multi-stage NMPC scheme does not guarantee Input-to-State stability (ISS) in a deterministic setting, in contrast to the results that one gets using stochastic stability concepts. Since for many applications deterministic stability guarantees are desired, we provide an alternative formulation to achieve deterministic ISS and recursive feasibility guarantees for the case of discrete values of the uncertainty. The design and the performance of the proposed schemes are illustrated by simulations for a highly nonlinear example. [ABSTRACT FROM AUTHOR]