This thesis documents the development of a novel nonlinear controller for servo pneumatic actuators that give good reference tracking at low speed motion, where friction has strong effect to the system behaviors. The design of the nonlinear controller presented in this thesis is based on the formalism of Lyapunov stability theory. The controller is constructed through a dynamical adaptive backstepping-sliding mode control algorithm. The conventional Lyapunov-based control algorithm is often limited by the order of the dynamical system; however, the backstepping design concept allows the control algorithm to be extended to higher order dynamical systems. In addition, the friction is estimated on-line via the Lyapunov-based adaptive laws embedded in the controller; meanwhile, the sliding mode control provides high robustness to the system parameter uncertainties. The simulation results clearly demonstrating the improved system performance (i.e., fast response and the reduced tracking error) are presented. Finally, the integration of the controller with a Lyapunov-based pressure observer reduces the state feedback of the servo pneumatic actuator model to only the piston displacement.

This thesis documents the development of a novel nonlinear controller for servo pneumatic actuators that give good reference tracking at low speed motion, where friction has strong effect to the system behaviors. The design of the nonlinear controller presented in this thesis is based on the formalism of Lyapunov stability theory. The controller is constructed through a dynamical adaptive backstepping-sliding mode control algorithm. The conventional Lyapunov-based control algorithm is often limited by the order of the dynamical system; however, the backstepping design concept allows the control algorithm to be extended to higher order dynamical systems. In addition, the friction is estimated on-line via the Lyapunov-based adaptive laws embedded in the controller; meanwhile, the sliding mode control provides high robustness to the system parameter uncertainties. The simulation results clearly demonstrating the improved system performance (i.e., fast response and the reduced tracking error) are presented. Finally, the integration of the controller with a Lyapunov-based pressure observer reduces the state feedback of the servo pneumatic actuator model to only the piston displacement.

Doctor of Philosophy / Electrical and Computer Engineering / Balasubramaniam Natarajan / A cyber-physical system (CPS) typically consists of networked computational elements that control physical processes. As an integral part of CPS, the widespread deployment of communicable sensors makes the task of monitoring and control quite challenging especially from the viewpoint of scalability and complexity. This research investigates two unique aspects of overcoming such barriers, making a CPS more robust against data explosion and network vulnerabilities. First, the correlated characteristics of high-resolution sensor data are exploited to significantly reduce the fused data volume. Specifically, spatial, temporal and spatiotemporal compressed sensing approaches are applied to sample the measurements in compressed form. Such aggregation can directly be used in centralized static state estimation even for a nonlinear system. This approach results in a remarkable reduction in communication overhead as well as memory/storage requirement. Secondly, an agent based architecture is proposed, where the communicable sensors (identified as agents) also perform local information processing. Based on the local and underdetermined observation space, each agent can monitor only a specific subset of global CPS states, necessitating neighborhood information exchange. In this framework, we propose an agent based static state estimation encompassing local consensus and least square solution. Necessary bounds for the consensus weights are obtained through the maximum eigenvalue based convergence analysis and are verified for a radial power distribution network. The agent based formulation is also applied for a linear dynamical system and the consensus approach is found to exhibit better and more robust performance compared to a diffusion filter. The agent based Kalman consensus filter (AKCF) is further investigated, when the agents can choose between measurements and/or consensus, allowing the economic allocation of sensing and communication tasks as well as the temporary omission of faulty agents. The filter stability is guaranteed by deriving necessary consensus bounds through Lyapunov stability analysis. The states dynamically estimated from AKCF can be used for state-feedback control in a model predictive fashion. The effect of lossy communication is investigated and critical bounds on the link failure rate and the degree of consensus that ensure stability of the agent based control are derived and verified via simulations.