Robust Model Predictive Control of An Input Delayed Functional Electrical Stimulation

Functional electrical stimulation (FES) is an external application of low-level currents to elicit muscle contractions that can potentially restore limb function in persons with spinal cord injury. However, FES often leads to the rapid onset of muscle fatigue, which limits performance of FES-based devices due to reduction in force generation capability. Fatigue is caused by unnatural muscle recruitment and synchronous and repetitive recruitment of muscle fibers. In this situation, overstimulation of the muscle fibers further aggravates the muscle fatigue. Therefore, a motivation exists to use optimal controls that minimize muscle stimulation while providing a desired performance. Model predictive controller (MPC) is one such optimal control method. However, the traditional MPC is dependent on exact model knowledge of the musculoskeletal dynamics and cannot handle modeling uncertainties. Motivated to address modeling uncertainties, robust MPC approach is used to control FES. Moreover, two new robust MPC techniques are studied to address electromechanical delay (EMD) during FES, which often causes performance issues and stability problems. This thesis compares two types of robust MPCs: a Lyapunov-based MPC and a tube- based MPC for controlling knee extension elicited through FES. Lyapunov-based MPC incorporated a contractive constraint that bounds the Lyapunov function of the MPC with a Lyapunov function that was used to derive an EMD compensation control law. The Lyapunov-based MPC was simulated to validate its performance. In the tube-based MPC, the EMD compensation controller was chosen to be the tube that eliminated output of the nominal MPC and the output of the real system. Regulation experiments were performed for the tube-based MPC on a leg extension machine and the controller showed robust performance despite modeling uncertainties

Robust Model Predictive Control of An Input Delayed Functional Electrical Stimulation

Functional electrical stimulation (FES) is an external application of low-level currents to elicit muscle contractions that can potentially restore limb function in persons with spinal cord injury. However, FES often leads to the rapid onset of muscle fatigue, which limits performance of FES-based devices due to reduction in force generation capability. Fatigue is caused by unnatural muscle recruitment and synchronous and repetitive recruitment of muscle fibers. In this situation, overstimulation of the muscle fibers further aggravates the muscle fatigue. Therefore, a motivation exists to use optimal controls that minimize muscle stimulation while providing a desired performance. Model predictive controller (MPC) is one such optimal control method. However, the traditional MPC is dependent on exact model knowledge of the musculoskeletal dynamics and cannot handle modeling uncertainties. Motivated to address modeling uncertainties, robust MPC approach is used to control FES. Moreover, two new robust MPC techniques are studied to address electromechanical delay (EMD) during FES, which often causes performance issues and stability problems. This thesis compares two types of robust MPCs: a Lyapunov-based MPC and a tube- based MPC for controlling knee extension elicited through FES. Lyapunov-based MPC incorporated a contractive constraint that bounds the Lyapunov function of the MPC with a Lyapunov function that was used to derive an EMD compensation control law. The Lyapunov-based MPC was simulated to validate its performance. In the tube-based MPC, the EMD compensation controller was chosen to be the tube that eliminated output of the nominal MPC and the output of the real system. Regulation experiments were performed for the tube-based MPC on a leg extension machine and the controller showed robust performance despite modeling uncertainties

A Hybrid Nonlinear Model Predictive Control and Recurrent Neural Networks for Fault-Tolerant Control of an Autonomous Underwater Vehicle

The operation of Autonomous Unmanned Vehicles (AUVs) that is used for environment protection, risk evaluation and plan determination for emergency, are among the most important and challenging problems. An area that has received much attention for use of AUVs is in underwater applications where much work remains to be done to equip AUVs with systems to steer them accurately and reliably in harsh marine environments. Design of control strategies for AUVs is very challenging as compared to other systems due to their operational environment (ocean). Particularly when hydrodynamic parameters uncertainties are to be integrated into both the controller design as well as AUVs nonlinear dynamics. On the other hand, AUVs like all other mechanical systems are prone to faults. Dealing effectively with faulty situations for mechanical systems is an important consideration since faults can result in abnormal operation or even a failure. Hence, fault tolerant and fault-accommodating methods in the controller design are among active research topics for maintaining the reliability of complex AUV control systems. The objective of this thesis is to develop a nonlinear Model Predictive Control (MPC) that requires solving an online Quadratic Programming (QP) problem by using a Recurrent Neural Network (RNN). Also, an Extended Kalman Filter (EKF) is integrated with the developed scheme to provide the MPC algorithm with the system states estimates as well as a nonlinear prediction. This hybrid control approach utilizes both the mathematical model of the system as well as the adaptive nature of the intelligent technique through neural networks. The reason behind the selection of MPC is to benefit from its main capability in optimization within the current time slots while taking future time slots into consideration. The proposed control method is integrated with EKF which is an appropriate method for state estimation and data reconciliation of nonlinear systems. In order to address the high performance runtime cost of solving the MPC problem (formulated as a quadratic programming problem), an RNN is developed that has a low model complexity as well as good performance in real-time implementation. The proposed method is first developed to control an AUV following a desired trajectory. Since the problem of trajectory tracking and path following of AUVs exhibit nonlinear behavior, the effectiveness of the developed MPC-RNN algorithm is studied in comparison with two other control system methods, namely the linear MPC using Kalman Filter (KF) and the conventional nonlinear MPC using the EKF. In order to guarantee the fault-tolerant features of our proposed control method when faced with severe actuator faults, the developed MPC-RNN scheme is integrated with a dual Extended Kalman Filter that is used for a combined estimation of AUV states and parameters. The actuator faults are defined as the system parameters that are to be estimated online by the dual-EKF. Therefore, the developed Active Fault-Tolerant Control (AFTC) strategy is then applied to an AUV faced with loss of effectiveness (LOE) actuator fault scenarios while following a trajectory. Analysis and discussions regarding the comparison of the proposed AFTC method with Fault-Tolerant Nonlinear Model Predictive Control (FTNMPC) algorithm are presented in this work. The proposed approach to AFTC exploits the advantages of the MPC-RNN algorithm properties as well as accounting explicitly for severe control actuator faults in the nonlinear AUV model with uncertainties that are formulated by the MPC

Contributions à la commande prédictive des systèmes de lois de conservation

La Commande prédictive ou Commande Optimale à Horizon Glissant (COHG) devient de plus en plus populaire dans de nombreuses applications pratiques en raison de ses avantages importants tels que la stabilisation et la prise en compte des contraintes. Elle a été bien étudiée pour des systèmes en dimension finie même dans le cas non linéaire. Cependant, son extension aux systèmes en dimension infinie n'a pas retenu beaucoup d'attention de la part des chercheurs. Ce travail de thèse apporte des contributions à l'application de cette approche aux systèmes de lois de conservation. Nous présentons tout d'abord une preuve de stabilité complète de la COHG pour certaines classes de systèmes en dimension infinie. Ce résultat est ensuite utilisé pour les systèmes hyperboliques 2x2 commandés aux frontières et appliqué à un problème de contrôle de canal d'irrigation. Nous proposons aussi l'extension de cette stratégie au cas de réseaux de systèmes hyperboliques 2x2 en cascade avec une application à un ensemble de canaux d'irrigation connectés. Nous étudions également les avantages de la COHG dans le contexte des systèmes non linéaires et semi-linéaires notamment vis-à-vis des chocs. Toutes les analyses théoriques sont validées par simulation afin d'illustrer l'efficacité de l'approche proposée.The predictive control or Receding Horizon Optimal Control (RHOC) is becoming increasingly popular in many practical applications due to its significant advantages such as the stabilization and constraints handling. It has been well studied for finite dimensional systems even in the nonlinear case. However, its extension to infinite dimensional systems has not received much attention from researchers. This thesis proposes contributions on the application of this approach to systems of conservation laws. We present a complete proof of stability of RHOC for some classes of infinite dimensional systems. This result is then used for 2x2 hyperbolic systems with boundary control, and applied to an irrigation canal. We also propose the extension of this strategy to networks of cascaded 2x2 hyperbolic systems with an application to a set of connected irrigation canals. Furthermore, we study the benefits of RHOC in the context of nonlinear and semi-linear systems in particular with respect to the problem of shocks. All theoretical analyzes are validated by simulation in order to illustrate the effectiveness of the proposed approach.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Multi-Rate Observers for Model-Based Process Monitoring

Very often, critical quantities related to safety, product quality and economic performance of a chemical process cannot be measured on line. In an attempt to overcome the challenges caused by inadequate on-line measurements, state estimation provides an alternative approach to reconstruct the unmeasured state variables by utilizing available on-line measurements and a process model. Chemical processes usually possess strong nonlinearities, and involve different types of measurements. It remains a challenging task to incorporate multiple measurements with different sampling rates and different measurement delays into a unified estimation algorithmic framework. This dissertation seeks to present developments in the field of state estimation by providing the theoretical advances in multi-rate multi-delay observer design. A delay-free multi-rate observer is first designed in linear systems under asynchronous sampling. Sufficient and explicit conditions in terms of maximum sampling period are derived to guarantee exponential stability of the observer, using Lyapunov’s second method. A dead time compensation approach is developed to compensate for the effect of measurement delay. Based on the multi-rate formulation, optimal multi-rate observer design is studied in two classes of linear systems where optimal gain selection is performed by formulating and solving an optimization problem. Then a multi-rate observer is developed in nonlinear systems with asynchronous sampling. The input-to-output stability is established for the estimation errors with respect to measurement errors using the Karafyllis-Jiang vector small-gain theorem. Measurement delay is also accounted for in the observer design using dead time compensation. Both the multi-rate designs in linear and nonlinear systems provide robustness with respect to perturbations in the sampling schedule. Multi-rate multi-delay observer is shown to be effective for process monitoring in polymerization reactors. A series of three polycondensation reactors and an industrial gas-phase polyethylene reactor are used to evaluate the observer performance. Reliable on-line estimates are obtained from the multi-rate multi-delay observer through simulation