Engine control becomes increasingly important in newer cars. It is therefore interesting to investigate if a relatively new control method as Model Predictive Control (MPC) can be useful in engine control in the future. One of the advantages of MPC is that it can handle contraints explicitly. In this thesis basics on turbocharged engines and the underlying theory of MPC is presented. Based on a nonlinear mean value engine model, linearized at multiple operating points, we then implement both a linear and a nonlinearMPC strategy and highlight implementation issues. The implemented MPC controllers calculate optimal wastegate position in order to track a requested torque curve and still make sure that the constraints on turbocharger speed and minimum and maximum opening of the wastegate are fulfilled.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24743号 / 情博第831号 / 新制||情||139(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 大塚 敏之, 教授 加納 学, 教授 東 俊一 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

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In this work, a robust decentralized model predictive control regime for a team of cooperating robot systems is designed. Their assumed dynamics are in continuous time and non-linear. The problem involves agents whose dynamics are independent of one-another, and its solution couples their constraints as a means of capturing the cooperative behaviour required. Analytical proofs are given to show that, under the proposed control regime: (a) Subject to initial feasibility, the optimization solved at each step by each agent will always be feasible, irrespective of whether or not disturbances affect the agents. In the former case, recursive feasibility is established through successive restriction of each agent's constraints during the periodic solution to its respective optimization problem. (b) Each (sub)system can be stabilized to a desired configuration, either asymptotically when uncertainty is absent, or within a neighbourhood of it, when uncertainty is present, thus attenuating the affecting disturbance. In this context, disturbances are assumed to be additive and bounded. Simulations verify the efficacy of the proposed method over a range of different operating environments. / I detta arbete, en robust decentraliserad modell prediktiv kontroll regime förett lag av samverkande robotsystem är utformade. Deras antagnat dynamikär i kontinuerlig tid och olinjär. Problemet involverar agenter vars dynamik äroberoende av varandra, och sina lösning kopplar sina begränsningar som ettmedel för att fånga det kooperativa beteendet som krävs. Analytiska bevis gesför att visa att, enligt det föreslagna kontrollsystemet: (a) med förbehåll förförsta genomförbarhet, kommer optimeringen som löses vid varje steg av varjeagent alltid vara möjligt, oavsett huruvida störningar påverkar agenserna ellerinte. I det förre fallet är rekursiv genomförbarhet etablerad genom successivabegränsningar av varje agents inskränkning under den periodiska lösningentill dess respektive optimeringsproblem. (b) Varje (sub) system kan stabiliserastill en önskad konfiguration, antingen asymptotiskt när osäkerhet saknas,eller inom en närhet av det, när osäkerhet är närvarande, således dämparpåverkanstörning. I detta sammanhang antas störningar vara additiv och avgränsas.Simuleringar verifierar effekten av den föreslagna metoden över ettintervall av olika driftsmiljöer.

This study investigated a tracking system to trace unknown signal in the presence oftime delay. A predictive control method is proposed in order to compensate the time delay. Root locus method is applied when designing the controller, parameter setting is carried out through error and trail technique in w-plane. State space equation is derived for the system, with special state chose of tracking error. To analyze the asymptotic stability of the proposed predictive control system, the Lyapunov function is constructed. It is shown that the designed system is asymptotically stable when input signal is rather low frequency signal. In order to illustrate the system performance, simulations are done based on the data profile technique. Signal profiles including acceleration pro le, velocity pro le, and trajectory profile are listed. Based on these profiles, simulations can be carried out and results can be taken as a good estimation for practical performance of the designed predictive control system. Signal noise is quite a common phenomenon in practical control systems. Under the situation that the input signal is with measurement noise, low pass filter is designed to filter out the noise and keep the low frequency input signal. Two typical kinds of noise are specified, i.e Gaussian noise and Pink noise. Simulations results are displayed to show that the proposed predictive control with low-pass filter design can achieve better performance in the case of both kinds of noise.

This work presents the Czochralski growth dynamics as time-varying delay based model, applied to the growth of La3Ga5.5Ta0.5O14 (LGT) piezoelectric crystals. The growth of high-quality large-diameter oxides by Czochralski technique requires the theoretical understanding and optimization of all relevant process parameters, growth conditions, and melts chemistry. Presently, proportional-integral- derivative (PID) type controllers are widely accepted for constant-diameter crystal growth by Czochralski. Such control systems, however, do not account for aspects such as the transportation delay of the heat from crucible wall to the crystal solidification front, heat radiated from the crucible wall above the melt surface, and varying melt level. During crystal growth, these time delays play a dominant role, and pose a significant challenge to the control design. In this study, a time varying linear delay model was applied to the identification of nonlinearities of the growth dynamics. Initial results reveled the benefits of this model with actual growth results. These results were used to develop a long-range model predictive control system design. Two different control techniques using long range prediction are studied for the comparative study. Development and testing of the new control system on real time growth system are discussed in detail. The results are promising and suggest future work in this direction. Other discussion about the problems during the crystal growth, optimization of crystal growth parameters are also studied along with the control system design.

Soft robots are inherently safer than traditional robots due to their compliance and high power density ratio resulting in lower accidental impact forces. Thus they are a natural option for human-robot interaction. This thesis specifically looked at human-robot co-manipulation which is defined as a human and a robot working together to move an object too large or awkward to be safely maneuvered by a single agent. To better understand how humans communicate while co-manipulating an object, this work looked at haptic interaction of human-human dyadic co-manipulation trials and studied some of the trends found in that interaction. These trends point to ways robots can effectively work with human partners in the future. Before successful human-robot co-manipulation with large-scale soft robots can be achieved, low-level joint angle control is needed. Low-level model predictive control of soft robot joints requires a sufficiently accurate model of the system. This thesis introduces a recursive Newton-Euler method for deriving the dynamics that is sufficiently accurate and accounts for flexible joints in an intuitive way. This model has been shown to be accurate to a median absolute error of 3.15 degrees for a three-link three-joint six degree of freedom soft robot arm. Once a sufficiently accurate model was developed, a gain-based evolutionary model predictive control (MPC) technique was formulated based on a previous evolutionary MPC technique. This new method is referred to as model evolutionary gain-based predictive control or MEGa-PC. This control law is compared to nonlinear evolutionary model predictive control (NEMPC). The new technique allows intentionally decreasing the control frequency to 10 Hz while maintaining control of the system. This is proven to help MPC solve more difficult problems by having the ability to extend the control horizon. This new controller is also demonstrated to work well on a three-joint three-link soft robot arm. Although complete physical human-robot co-manipulation is outside the scope of this thesis, this thesis covers three main building blocks for physical human and soft robot co-manipulation: human-human haptic communication, soft robot modeling, and model evolutionary gain-based predictive control.

Ensuring constraint satisfaction in large-scale systems with hard constraints is vital in many safety critical systems. The challenge is to design controllers that are efficiently synthesized offline, easily implementable online, and provide formal correctness guarantees. We take a divide and conquer approach to design controllers for reachability and infinite-time/finite-time constraint satisfaction control problems given large-scale interconnected linear systems with polyhedral constraints on states, controls, and disturbances. Such systems are made of small subsystems with coupled dynamics. Our goals are to design controllers that are i) fully compositional and ii) decentralized, such that online implementation requires only local state information. We treat the couplings among the subsystems as additional disturbances and use assume-guarantee (AG) contracts to characterize these disturbance sets. For each subsystem, we design and implement a robust controller locally, subject to its own constraints and contracts. Our main contribution is a method to derive the contracts via a novel parameterization, and a corresponding potential function that characterizes the distance to the correct composition of controllers and contracts, where all contracts are held. We show that the potential function is convex in the contract parameters. This enables the subsystems to negotiate the contracts with the gradient information from the dual of their local synthesis optimization problems in a distributed way, facilitating compositional control synthesis that scales to large systems. We then incorporate Signal Temporal Logic (STL) specifications into our formulation. We develop a decentralized control method for a network of perturbed linear systems with dynamical couplings subject to STL specifications. We first transform the STL requirements into set containment problems, then we develop controllers to solve these problems. The set containment requirements and parameterized contracts are added to the subsystems’ constraints. We introduce a centralized optimization problem to derive the contracts, reachability tubes, and decentralized closed-loop control laws. We show that, when the STL formula is separable with respect to the subsystems, the centralized optimization problem can be solved in a distributed way, which scales to large systems. We present formal theoretical guarantees on robustness of STL satisfaction. We present numerical examples, including scalability studies on systems with tens of thousands of dimensions, and case studies on applying our method to a distributed Model Predictive Control (MPC) problem in a power system. / 2024-01-16T00:00:00Z

Inflatable robots with pneumatic actuation are naturally lightweight and compliant. Both of these characteristics make a robot of this type better suited for human environments where unintentional impacts will occur. The dynamics of an inflatable robot are complex and dynamic models that explicitly allow variable stiffness control have not been well developed. In this thesis, a dynamic model was developed for an antagonistic, pneumatically actuated joint with inflatable links.The antagonistic nature of the joint allows for the control of two states, primarily joint position and stiffness. First a model was developed to describe the position states. The model was used with model predictive control (MPC) and linear quadratic control (LQR) to control a single degree of freedom platform to within 3° of a desired angle. Control was extended to multiple degrees of freedom for a pick and place task where the pick was successful ten out of ten times and the place was successful eight out of ten times.Based on a torque model for the joint which accounts for pressure states that was developed in collaboration with other members of the Robotics and Dynamics Lab at Brigham Young University, the model was extended to account for the joint stiffness. The model accounting for position, stiffness, and pressure states was fit to data collected from the actual joint and stiffness estimation was validated by stiffness measurements.Using the stiffness model, sliding mode control (SMC) and MPC methods were used to control both stiffness and position simultaneously. Using SMC, the joint stiffness was controlled to within 3 Nm/rad of a desired trajectory at steady state and the position was controlled to within 2° of a desired position trajectory at steady state. Using MPC,the joint stiffness was controlled to within 1 Nm/rad of a desired trajectory at steady state and the position was controlled to within 2° of a desired position trajectory at steady state. Stiffness control was extended to multiple degrees of freedom using MPC where each joint was treated as independent and uncoupled. Controlling stiffness reduced the end effecter deflection by 50% from an applied load when high stiffness (50 Nm/rad) was used rather than low stiffness (35 Nm/rad).This thesis gives a state space dynamic model for an inflatable, pneumatically actuated joint and shows that the model can be used for accurate and repeatable position and stiffness control with stiffness having a significant effect.

ual-three phase permanent magnet synchronous motors (DTP-PMSM) are becom ing more popular in the automotive field. Their potential to increase the reliability and efficiency of the vehicle makes them an attractive replacement for the three phase alternative. However, the increased number of phases makes the control of the machine more complex. As a result, conventional controllers can see reduced perfor mance, especially at high speeds and torques. Currently, with the increased process ing power of modern micro-controllers and field-programmable gate arrays (FPGA), many researchers are investigating whether finite-control set model predictive control (FCS-MPC) can be a suitable alternative. FCS-MPC is simple to implement and can achieve a better dynamic performance when compared to other controllers. Furthermore, the algorithm can be augmented for specific optimization goals and non-linearities to the system, which gives the designer creativity in improving the system response. However, Model-Predictive Control suffers from a variable switching frequency as well as reduced steady-state performance. It generally has increased current ripple in the phase currents. This thesis presents a method of reducing the steady-state ripples in FCS-MPC by introducing the use of virtual-flux in the model equations, the incremental model, and a dynamic vector search-space. All three of these applications make FCS-MPC have a iv significantly improved steady-state performance when compared to the conventional algorithm, while still keeping the benefit of the improved dynamic response. The benefits of the proposed techniques techniques are verified through simulation as well as on an experimental setup. / Thesis / Master of Applied Science (MASc)