Task-dependent switching of feedback controllers

The separation of distinct motor memories by contextual cues is a well known and well studied phenomenon of feedforward human motor control. However, there is no clear evidence of such context-induced separation in feedback control. Here we test both experimentally and computationally if context-dependent switching of feedback controllers is possible in the human motor system. Specifically, we probe visuomotor feedback responses of our human participants in two different tasks – stop and hit – and under two different schedules. The first, blocked schedule, is used to measure the behaviour of stop and hit controllers in isolation, showing that it can only be described by two independent controllers with two different sets of control gains. The second, mixed schedule, is then used to compare how such behaviour evolves when participants regularly switch from one task to the other. Our results support our hypothesis that there is contextual switching of feedback controllers, further extending the accumulating evidence of shared features between feedforward and feedback control.

A cerebellar internal model calibrates a feedback controller involved in sensorimotor control

Animals must adapt their behavior to survive in a changing environment. Behavioral adaptations can be evoked by two mechanisms: feedback control and internal-model-based control. Feedback controllers can maintain the sensory state of the animal at a desired level under different environmental conditions. In contrast, internal models learn the relationship between the motor output and its sensory consequences and can be used to recalibrate behaviors. Here, we present multiple unpredictable perturbations in visual feedback to larval zebrafish performing the optomotor response and show that they react to these perturbations through a feedback control mechanism. In contrast, if a perturbation is long-lasting, fish adapt their behavior by updating a cerebellum-dependent internal model. We use modelling and functional imaging to show that the neuronal requirements for these mechanisms are met in the larval zebrafish brain. Our results illustrate the role of the cerebellum in encoding internal models and how these can calibrate neuronal circuits involved in reactive behaviors depending on the interactions between animal and environment.

Multi-model structured H-infinity design of a robust and aggressive turbofan jet engine controller

Jet engine control comprises tracking either the fan speed or engine pressure ratio setpoints. Further, safe operation entails maintaining several additional parameters, such as high-pressure turbine temperature, combustor pressure, core shaft acceleration and other ones within prescribed limits. A Min-Max selector that features PI controllers is frequently used to handle these requirements. However, this arrangement is overly conservative in the limits management, which unnecessarily slows down the engine response. To overcome this shortcoming, a new controller that adopts the traditional Min-Max structure in combination with the Ndot control, the Conditionally Active and the Conditioning Technique approaches is developed. PI regulators are replaced by dynamic output feedback controllers, which are designed according to a multi-model structured H-infinity methodology. This approach makes it possible to marry robustness with performance, which are two conflicting objectives. Singular value analysis tools demonstrate the robustness of the resulting design. Linear and nonlinear simulations indicate that the proposed controller optimizes the engine response time under the constraint of keeping a set of parameters within prescribed bounds. The features of the proposed design are lucrative for actual implementation in the industry.

Specification Guided Automated Synthesis of Feedback Controllers

The growing use of complex Cyber-Physical Systems (CPSs) in safety-critical applications has led to the demand for the automatic synthesis of robust feedback controllers that satisfy a given set of formal specifications. Controller synthesis from the high-level specification is an NP-Hard problem. We propose a heuristic-based automated technique that synthesizes feedback controllers guided by Signal Temporal Logic (STL) specifications. Our technique involves rigorous analysis of the traces generated by the closed-loop system, matrix decomposition, and an incremental multi-parameter tuning procedure. In case a controller cannot be found to satisfy all the specifications, we propose a technique for modifying the unsatisfiable specifications so that the controller synthesized for the satisfiable subset of specifications now also satisfies the modified specifications. We demonstrate our technique on eleven controllers used as standard closed-loop control system benchmarks, including complex controllers having multiple independent or nested control loops. Our experimental results establish that the proposed algorithm can automatically solve complex feedback controller synthesis problems within a few minutes.

ROBUST AND FEEDFORWARD CONTROL FOR A NONLINEAR PNEUMATIC SERVOMECHANISM SYSTEM

The actuator of the vane servo unit epitomizes the control operator of force in systems of missile control, where the character of its dynamic and static plays a significant role in the missile behavior. Therefore, improving the dynamic behavior for the vane servo actuator is of main interest for designing control and guidance system. The article describes a new method of analyzing the mathematical model of the nonlinear pneumatic servo with different design parameters and designing a controller with these parameters. The robust control regulates the system with different parameters, and it is the first controller to attempt this technique. A servo actuator of nonlinear and linear simulators was constructed by MATLAB software package. Feedback controllers with PI and PID were designed and tested theoretically. The setting time and the behavior of the dynamic will be improved. The robust feedforward control was applied to the system to improve the stability and zero steady-state error and compare the results with PI and PID controller. Their tests showed that robust control is the best control for stability among the others.

Burster Reconstruction Considering Unmeasurable Variables in the Epileptor Model

Epilepsy is one of the most common brain disorders worldwide, affecting millions of people every year. Although significant effort has been put into better understanding it and mitigating its effects, the conventional treatments are not fully effective. Advances in computational neuroscience, using mathematical dynamic models that represent brain activities at different scales, have enabled addressing epilepsy from a more theoretical standpoint. In particular, the recently proposed Epileptor model stands out among these models, because it represents well the main features of seizures, and the results from its simulations have been consistent with experimental observations. In addition, there has been an increasing interest in designing control techniques for Epileptor that might lead to possible realistic feedback controllers in the future. However, such approaches rely on knowing all of the states of the model, which is not the case in practice. The work explored in this letter aims to develop a state observer to estimate Epileptor's unmeasurable variables, as well as reconstruct the respective so-called bursters. Furthermore, an alternative modeling is presented for enhancing the convergence speed of an observer. The results show that the proposed approach is efficient under two main conditions: when the brain is undergoing a seizure and when a transition from the healthy to the epileptiform activity occurs.

Robust Stabilization of Controlled Positive Switched Linear Systems by Output Feedback

This study focuses on the controller synthesis issues for constrained switched linear systems with uncertainties under mode-dependent average dwell time (MDADT) switching strategy. First, output feedback controllers ensure that the closed-loop systems are positive and asymptotically stable. Second, the bounded controllers are acquired based on system states with interval and polytopic uncertainties. Also, the proposed approach can be applied to the systems with the constrained output. Then, the presented conditions can be formulated in terms of linear programming. Finally, illustrative example is provided to show the effectiveness of the theoretical results.

A Methodology for Designing a Nonlinear Feedback Controller via Parametric Optimization: State-Parameterized Nonlinear Programming Control

This paper introduces a new technique for designing nonlinear feedback controllers that can effectively and efficiently control nonlinear and unstable dynamical systems. The technique, called State-Parameterized Nonlinear Programming Control (sp-NLPC), constructs an optimal control strategy that is a function of dynamical system states. This is achieved through an offline parametric optimization process using the predictive parameterized Pareto genetic algorithm (P3GA) and representing the optimized state-varying policy using radial basis function (RBF) metamodeling. The sp-NLPC technique avoids many limitations of alternative methods, such as the need to make strong assumptions about model form (e.g., linearity) and the demands of online optimization processes. The proposed method is benchmarked on the problems of controlling the highly nonlinear and inherently unstable systems: single and double inverted pendulums on a cart. Performance and computational efficiency are compared to several competing control design techniques. Results show that sp-NLPC outperforms and is more efficient than competing methods. The parametric solution strategy for sp-NLPC lends itself to use in Control Co-Design (CCD). Such extensions are discussed as part of future work.

Review on model predictive control: an engineering perspective

Model-based predictive control (MPC) describes a set of advanced control methods, which make use of a process model to predict the future behavior of the controlled system. By solving a—potentially constrained—optimization problem, MPC determines the control law implicitly. This shifts the effort for the design of a controller towards modeling of the to-be-controlled process. Since such models are available in many fields of engineering, the initial hurdle for applying control is deceased with MPC. Its implicit formulation maintains the physical understanding of the system parameters facilitating the tuning of the controller. Model-based predictive control (MPC) can even control systems, which cannot be controlled by conventional feedback controllers. With most of the theory laid out, it is time for a concise summary of it and an application-driven survey. This review article should serve as such. While in the beginnings of MPC, several widely noticed review paper have been published, a comprehensive overview on the latest developments, and on applications, is missing today. This article reviews the current state of the art including theory, historic evolution, and practical considerations to create intuitive understanding. We lay special attention on applications in order to demonstrate what is already possible today. Furthermore, we provide detailed discussion on implantation details in general and strategies to cope with the computational burden—still a major factor in the design of MPC. Besides key methods in the development of MPC, this review points to the future trends emphasizing why they are the next logical steps in MPC.