Biaxial motion control systems are widely employed in manufacturing industries, where improving throughput and reducing machine costs are critical goals. To achieve these, lightweight materials are increasingly being used in structural components, though this often introduces higher flexibility in the machine links, leading to position-dependent precision loss in the end-effector. This article addresses the challenge of maintaining contouring accuracy in such systems by proposing a novel contouring error-bounded control algorithm for biaxial switched linear systems. The algorithm employs model predictive control (MPC) to enforce state, input, and contouring error constraints across different system modes, even when mode switching is not known in advance. While the exact switching signal is unknown, the controller assumes knowledge of the minimum dwell time the system remains in each mode. The proposed algorithm guarantees recursive feasibility and ensures closed-loop system stability. The effectiveness of the method is validated through a high-fidelity simulation of a dual-drive industrial laser machine, demonstrating that the contouring error is consistently maintained within the specified tolerance.

Advances in the control of autonomous systems have accompanied an expansion in the potential applications for autonomous robotic swarms. The success of applications involving humans depends on the quality of interaction with the swarm, particularly the trust that the commander places in the swarm. Absent from the literature is the design of commander trust dynamics that incorporate asymmetric responses to swarm performance. This paper focuses on developing an estimated trust model that employs a switched linear system structure. The identified model is used in a model-based observer that eliminates the need for self-reported trust measurements. Results from a recent user study with 51 participants illustrate considerations during the estimation of population parameters for such nonlinear model-based observers. It is anticipated that such a trust observer can be used to augment communication interfaces for human-swarm interactions in complex environments, leading to better performing systems incorporating humans in the loop.

Constraint handling during tracking operations is at the core of many real-world control implementations and is well understood when dynamic models of the underlying system exist, yet becomes more challenging when data-driven models are used to describe the nonlinear system at hand. We seek to combine the nonlinear modeling capabilities of a wide class of neural networks with the constraint-handling guarantees of model predictive control in a rigorous and online computationally tractable framework. The class of networks considered can be captured using Koopman operators, and are integrated into a Koopman-based predictive tracking control (KPTC) for nonlinear systems to track piecewise constant references. The effect of model mismatch between original nonlinear dynamics and its trained Koopman linear model is handled by using a constraint-tightening approach in the proposed KPTC controller. By choosing two Lyapunov functions, we prove that the solution is recursively feasible and input-to-state stable to a neighborhood of both online and offline optimal reachable steady outputs in the presence of bounded modeling errors under certain assumptions. The proposed approach has the advantage relative to existing model-based tracking approaches of enabling data-driven models to be utilized with explicit guarantees, while using efficient quadratic program solvers in online implementations. We demonstrate the proposed approach initially in simulations, and then experimentally to the problem of reference tracking by an autonomous ground vehicle.

We consider the problem of collaborative bearing estimation using a method with historic roots in set theoretic estimation techniques. We refer to this method as the Convex Combination Ellipsoid (CCE) method and show that it provides a less conservative covariance estimate than the well known Covariance Intersection (CI) method. The CCE method does not introduce additional uncertainty that was not already present in the prior estimates. Using our proposed approach for collaborative bearing estimation, the nonlinearity of the bearing measurement is captured as an uncertainty ellipsoid thereby avoiding the need for linearization or approximation via sampling procedures. Simulations are undertaken to evaluate the relative performance of the collaborative bearing estimation solution using the proposed (CCE) and typical (CI) methods.

With advances in image processing and machine learning, it is now feasible to incorporate semantic information into the problem of simultaneous localisation and mapping (SLAM). Previously, SLAM was carried out using lower level geometric features (points, lines, and planes) which are often view-point dependent and error prone in visually repetitive environments. Semantic information can improve the ability to recognise previously visited locations, as well as maintain sparser maps for long term SLAM applications. However, SLAM in repetitive environments has the critical problem of assigning measurements to the landmarks which generated them. In this paper, we use k-best assignment enumeration to compute marginal assignment probabilities for each measurement landmark pair, in real time. We present numerical studies on the KITTI dataset to demonstrate the effectiveness and speed of the proposed framework.

Synergies have been adopted in prosthetic limb applications to reduce the complexity of design, but typically involve a single synergy setting for a population and ignore individual preference or adaptation capacity. However, personalization of the synergy setting is necessary for the effective operation of the prosthetic device. Two major challenges hinder the personalization of synergies in human-prosthesis interfaces (HPIs). The first is related to the process of human motor adaptation and the second to the variation in motor learning dynamics of individuals. In this paper, a systematic personalization of kinematic synergies for HPIs using online measurements from each individual is proposed. The task of reaching using the upper limb is described by an objective function and the interface is parameterized by a kinematic synergy. Consequently, personalizing the interface for a given individual can be formulated as finding an optimal personalized parameter. A structure to model the observed motor behavior that allows for the personalized traits of motor preference and motor learning is proposed, and subsequently used in an online optimization scheme to identify the synergies for an individual. The knowledge of the common features contained in the model enables online adaptation of the HPI to happen concurrently to human motor adaptation without the need to retune the personalization algorithm for each individual. Human-in-the-loop experimental results with able-bodied subjects, performed in a virtual reality environment to emulate amputation and prosthesis use, show that the proposed personalization algorithm was effective in obtaining optimal synergies with a fast uniform convergence speed across a group of individuals.

In this note, we study the stability of the error dynamics of an observer designed to estimate only the slow states of a singularly perturbed system. The observer is designed on the basis of the reduced (slow) model. We have recently reported semi-global practical results for this problem. Our previous work can be used to state local and regional convergence of the estimation error, but we cannot conclude global results from it. We seek to prove a stronger (global) result under stronger (global) assumptions in this manuscript. Moreover, we focus on proving the robustness of an observer with respect to singular perturbations and with respect to the measurement noise.

Many industrial domains are characterized by Multiple-Input-Multiple-Output (MIMO) systems for which an explicit relationship capturing the nontrivial trade-off between the competing objectives is not available. Human experts have the ability to implicitly learn such a relationship, which in turn enables them to tune the corresponding controller to achieve the desirable closed-loop performance. However, as the complexity of the MIMO system and/or the controller increase, so does the tuning time and the associated tuning cost. To reduce the tuning cost, a framework is proposed in which a machine learning method for approximating the human-learned cost function along with an optimization algorithm for optimizing it, and consequently tuning the controller, are employed. In this work the focus is on the tuning of Model Predictive Controllers (MPCs), given both the interest in their implementations across many industrial domains and the associated high degrees of freedom present in the corresponding tuning process. To demonstrate the proposed approach, simulation results for the tuning of an air path MPC controller in a diesel engine are presented.

In this paper, we develop a robust Economic Model Predictive Control (EMPC) formulation without terminal constraints for a class of time varying systems. We draw on recent research into EMPC for nominal time varying systems, and for uncertain systems which are optimally operated at steady state. A suitable notion of robust optimal operation and robust dissipativity for time varying systems is introduced. These, as well as turnpike arguments, are used to assess the controller's open loop performance. A numerical example is used to demonstrate the theoretical results herein.

The manipulation of objects is an inherent capability of dexterous human hands. However, state-of-the-art prostheses are limited to only forming grasps and gestures due to the low information transfer rate in the human-machine interface. Additionally, in the prosthetic setting the hand is not privy to such global knowledge including the object's shape, weight, friction properties, etc. Existing techniques from robotics that can manipulate objects in this prosthetic setting require compensation terms which are counterproductive to the manipulation task. The incorporation of tactile sensing leads to a simpler control formulation and enables relaxation of several restrictive assumptions inherent in the robotics applications. In this work a novel control system is proposed that incorporates tactile sensing for object manipulation and optimal distribution of contact forces for prosthetic hands. Simulation results demonstrate the ability of the proposed control system to manipulate an object, while minimizing the use of frictional forces during manipulation.

The benefits of model predictive control (MPC) have been well established; however its application to reference tracking on digital servo drives (DSDs), which typically have very fast update rates, is limited by the computational power of present-day processors. This paper presents a novel MPC formulation, which provides a mechanism to trade-off online computation effort with tracking performance, while maintaining stability. This is achieved by introducing a trajectory horizon, which is distinct from the prediction and control horizons typically encountered in MPC formulations. It is shown that increasing the trajectory horizon inherently leads to improved tracking; however larger horizon lengths also have the unwanted effect of increasing online computation. The proposed MPC formulation is compatible with recently developed explicit MPC solutions, and hence the burden of online optimization is avoided. The new approach is successfully implemented on an industrial machine tool DSD, and in terms of tracking accuracy, is shown to outperform the incumbent approach of cascaded PID control.

Fuel economy of parallel hybrid electric vehicles is affected by both the torque split ratio and the vehicle velocity. To optimally schedule both variables, information about the surrounding traffic is necessary, but may be made available through telemetry. Consequently, in this paper, a nonlinear model predictive control algorithm is proposed for the vehicle control system to maximise fuel economy while satisfying constraints on battery state of charge, relative position and vehicle performance. Different scenarios are considered including allowing and disallowing overtaking; various hard and soft constraints; and computational aspects of the solution. The optimal control signal vector was found to be characterised by smooth changes in velocity and increases in the motor to engine power ratio as the vehicle accelerates. It was found that using feedforward information about traffic flow in the range of five to fifteen seconds has the potential for significant fuel savings over two urban drive cycles.

In radial basis function (RBF) networks, placement of centers is said to have a significant effect on the performance of the network. Supervised learning of center locations in some applications show that they are superior to the networks whose centers are located using unsupervised methods. But such networks can take the same training time as that of sigmoid networks. The increased time needed for supervised learning offsets the training time of regular RBF networks. One way to overcome this may be to train the network with a set of centers selected by unsupervised methods and then to fine tune the locations of centers. This can be done by first evaluating whether moving the centers would decrease the error and then, depending on the required level of accuracy, changing the center locations. This paper provides new results on bounds for the gradient and Hessian of the error considered first as a function of the independent set of parameters, namely the centers, widths, and weights; and then as a function of centers and widths where the linear weights are now functions of the basis function parameters for networks of fixed size. Moreover, bounds for the Hessian are also provided along a line beginning at the initial set of parameters. Using these bounds, it is possible to estimate how much one can reduce the error by changing the centers. Further to that, a step size can be specified to achieve a guaranteed, amount of reduction in error.

This paper proposes using a model predictive control (MPC) incorporating a radial basis function (RBF) network observer for the fuel injection problem. Two new contributions are presented. First, an RBF Network is used as an observer for the volumetric efficiency of the air system. This allows for gradual adaptation of the observer, ensuring the control scheme is capable of maintaining good performance under changing engine conditions brought about by engine wear, variations between individual engines and other similar factors. The other is the rise of model predictive control algorithms to compensate for the fuel pooling effect on the intake manifold walls. Two MPC algorithms are presented which enforce input, and input and state constraints. A comparison between the two constrained MPC algorithms is qualitatively presented, and some conclusions drawn about the necessity of constraints for the fuel injection problem. Simulation and actual engine results are presented that demonstrate the effectiveness of the control scheme.